Rock Properties Model Rev 01, ICN 00

MDL-NBS-GS-000004

September 2004

1. PURPOSE 
The purpose of this model report is to document the Rock Properties Model version 3.1 with 
regard to input data, model methods, assumptions, uncertainties and limitations of model results, 
and qualification status of the model. The report also documents the differences between the 
current and previous versions and validation of the model. 
The rock properties model provides mean matrix and lithophysae porosity, and the 
cross-correlated mean bulk density as direct input to the Saturated Zone Flow and Transport 
Model Abstraction, MDL-NBS-HS-000021, REV 02 (BSC 2004 [DIRS 170042]). The 
constraints, caveats, and limitations associated with this model are discussed in Section 6.6 and 
8.2. Model validation accomplished by corroboration with data not cited as direct input is 
discussed in Section 7. 
The revision of this model report was performed as part of activities being conducted under the 
Technical Work Plan for: The Integrated Site Model, Revision 05 (BSC 2004 [DIRS 169635]). 
The purpose of this revision is to bring the report up to current procedural requirements and 
address the Regulatory Integration Team evaluation comments. The work plan describes the 
scope, objectives, tasks, methodology, and procedures for this process. 
The steps for developing the model consist of the following: 
1. 
Conversion of the input data (laboratory measured porosity data, x-ray diffraction 
mineralogy, petrophysical calculations of bound water, and petrophysical calculations 
of porosity) for each borehole into stratigraphic coordinates 
2. 
Re-sampling and merging of data sets 
3. 
Development of geostatistical simulations of porosity 
4. 
Generation of derivative property models via linear co-regionalization with porosity 
5. 
Postprocessing of the simulated models to impart desired secondary geologic attributes 
and to create summary and uncertainty models. 
The rock properties model is one of the three models developed as part of the integrated site 
model work scope. The integrated site model provides a consistent volumetric portrayal of the 
rock layers, rock properties, and mineralogy of the Yucca Mountain site and consists of 
three components: 
 
Geologic framework model 
 
Rock properties model, which is the subject of this model report 
 
Mineralogic model. 
Figure 1-1 shows the geographic boundaries of the rock properties model and other component 
models of the integrated site model. 

The specific report that uses the rock properties product output parameters is the Saturated Zone 
Flow and Transport Model Abstraction MDL-NBS-HS-000021, REV 02 (BSC 2004 
[DIRS 170042]). The report uses the mean matrix and lithophysae porosity, and the 
cross-correlated mean bulk density values from the rock properties model report as direct input. 
Source: Rautman, C. and McKenna, S. 1998 [DIRS 107442], Section 2 and MDL-NBS-GS-000002, REV 00 ICN 02, 
Figure 1. 
Figure 1-1. Index Map Showing the Location of the Geologic Framework Model, the Rock Properties 
Model (this report), and the Mineralogic Model 

2. QUALITY ASSURANCE 
Development of this model report and the supporting modeling activities have been determined 
to be subject to the Yucca Mountains Site Characterization Projects (YMP) quality assurance 
(QA) program, as indicated in the Technical Work Plan for: The Integrated Site Model, 
TWP-NBS-GS-000003 REV 05 (BSC 2004 [DIRS 169635], Section 8.1, Work Package 
(WP) ARTM01). Approved QA procedures identified in the Technical Work Plan (TWP) 
(BSC 2004 [DIRS 169635] have been used to conduct and document the activities described in 
this model report. The TWP also identifies the methods used to control the electronic 
management of data (BSC 2004 [DIRS 169635]), Section 8.4 WP ARTM01) during the model 
documentation activities. 
This Model Report provides representations of selected rock properties: matrix and lithophysae 
porosity and bulk density that are used as inputs to models that are subsequently used as input to 
Total System Performance Assessment that is used to evaluate compliance with the postclosure 
performance objectives prescribed in 10 CFR 63.113 [DIRS 156605]. This model report 
evaluates some properties of the Lower Natural Barrier and Upper Natural Barrier, that are 
classified in the Q-List (BSC 2004 [DIRS 168361]) as Safety Category SC because they are 
important to waste isolation, as defined in AP-2.22Q, Classification Analysis and Maintenance 
of the Q-List. The report contributes to the analysis and modeling data used to support 
performance assessment; the conclusions do not directly impact engineered features important to 
safety, as defined in AP-2.22Q Classification Analysis and Maintenance of the Q-List. 

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3. USE OF SOFTWARE 
3.1 SOFTWARE TRACKED BY CONFIGURATION MANAGEMENT 
The Rock Properties Model was constructed principally using geostatistical algorithms that are 
part of the public domain geostatistical subroutine library, GSLIB (Deutsch and Journel 1992 
[DIRS 100567] and 1998 [DIRS 102895]). The major modeling codes subject to configuration 
management are listed in Table 3-1, together with a brief description of their functionality. 
These computer software packages were obtained from configuration management at the time of 
model development, and are judged appropriate for use in this type of modeling activity. The 
software was used within the range of validation. 
The GSLIB software was selected for use as it provides the geostatistical algorithms needed to 
provide the volumetric representation of the selected rock properties. Earthvision was used only 
to produce geologic or real-world coordinate visualizations of the volumetric representation of 
the selected rock properties. MATCHUP and MATCH were selected for use as they permitted 
the generation of a single file of depth-matched values from two input files. There are no 
limitations on the output caused by the selected software. 
The software listed in Section 3.1 and 3.2 is provided for information only and to document their 
completeness and traceability. When the product output was developed the software was 
qualified according to the controlling procedure at the time AP-SI.1Q Software Configuration 
Management. After the development of the product output the software 
GSLIB 2.0MIK3 DV2.0, MATCHUP 12/05/98, and MATCH 12/05/98 were retired. 
Table 3-1. Software Tracked by Configuration Management 
Code Name Version STN Number 
Platform/Operating 
System Brief Description 
GSLIB 1.4SGSI 
MV1.40 
10110-
1.4MSGSIMV1.40-00 
[DIRS 113569] 
Deutsch and Journel, 
1998 [DIRS 102895], 
Section V 7.2 
HP 9000/ HP-UX 10.20 
Generates conditional or 
unconditional Gaussian 
simulations of a continuous 
variable; optional 
normal-score forward and 
back transformation (from the 
software library, GSLIB) 
GSLIB 2.0MIK3 
DV2.0 
10122-2.0MIK3D 
V2.0-00 
[DIRS 113587] 
Deutsch and Journel, 
1998 [DIRS 102895], 
Section IV 5 
PC/Windows NT V. 4.0 
Conducts indicator kriging of 
continuous or discrete 
variables using either hard or 
soft data or both (from the 
software library, GSLIB) 
GSLIB 
14MNS 
COREV1 
.201 
10109-1.4MNSCORE 
V1.201-00 
[DIRS 113595] 
Deutsch and Journel, 
1998 [DIRS 102895], 
Section VI 2.5 
HP 9000/ HP-UX 10.20 
Transforms a distribution of 
values to standard-normal 
form while preserving quantile 
relationships (from the 
software library, GSLIB) 

Table 3-1. Software Tracked by Configuration Management (Continued) 
Code Name Version STN Number Platform/Operating 
System 
Brief Description 
EARTHVISION 4.0 
30035.1 V4.0 
[DIRS 113625] 
SGI INDIGO R4000/IRIX 
6.4 
3-D earth science modeling 
package; used only to 
produce visualizations (report 
figures; e.g., Figure 63.5-18) 
of the Integrated Site Model 
3.0/ and Rock Properties 
Model 3.0 for this report 
Reads a set of simulation 
ETYPE 2.01 
10731-2.01-00 
[DIRS 159417] 
MS 2000 Server 
output (GSLIB format) files 
and creates summary E-type 
model; optional uncertainty 
output as standard deviation 
of simulations. Use is of this 
code is documented in 
Appendix I. 
Software routine that creates 
MATCHUP 12/05/98 10196-12/05/98-00 
[DIRS 113660] PC/Windows NT V. 4.0 a single file of depth-matched 
values from two columnar 
input files 
MATCH 12/05/98 10195-12/05/98-00 
[DIRS 113664] PC/Windows NT V. 4.0 
Modification of software 
routine MATCHUP for one 
GSLIB format file 
A number of additional software routines were developed as part of the rock properties 
modeling process. These routines are listed in Table 3-2 and they are documented for quality 
assurance purposes in the scientific notebooks associated with this activity (Table 6.3-1); the use 
of scientific notebooks is explained in Section 6.3.2. Exceptions to this practice are included as 
appendices to this report (noted in tables that follow). With the exception of STRATC4 (which 
is a macro-type software routine), all software routines listed in Table 3-2 run as Microsoft 
Fortran PowerStation Version 4.0 and have been compiled and executed on Intel-type personal 
computers under the Microsoft Windows NT Version 4.0 operating system. 
3.2 EXEMPT SOFTWARE 
Various exempt software products were also used during the course of this modeling activity to 
assist in organizing, managing, manipulating, comparing, and displaying data and information 
and computing standard statistical measures. This includes commercial off-the-shelf software 
and other industry-standard software. These software packages were used to perform support 
activities and were not used as the controlled source of information for the analysis, as defined in 
AP-SI.1Q. They were not required to be qualified under these procedures. 

Table 3-2. Software Routines 
Routine Name Version Reference Brief Description 
Acquired Software 
TRANS GSLIB V1.4 Module 
TRANS V1.3 
[DIRS 114225] 
Appendix B Transforms an input distribution to match the 
histogram of a target distribution (both in GSLIB 
format) while preserving the quantile relationships 
(from the software library, GSLIB) 
BICALIB GSLIB V1.4 Module 
BICALIB V2.0 
[DIRS 114227] 
Rautman and 
McKenna 1998 
[DIRS 107442], 
p. 1143 
Calibrates soft data (GSLIB format) for use as prior 
cdf values from matched hard-soft data pairs (from 
the software library, GSLIB) 
BACKTR GSLIB 
V1.4 
MBACKTRV1.2. 
V1.2. [DIRS 113642] 
Rautman and 
McKenna 1998 
[DIRS 107442], 
p. 1127 
Transforms a standard-normal distribution (GSLIB 
format) to match a reference histogram (from the 
software library, GSLIB; inverse of program NSCORE 
[Table 1]) 
Developed Software 
BUD 6/25/98 
[DIRS 113651] 
Rautman and 
McKenna 1998 
[DIRS 107442], 
p. 1013 
Reads a log-analysis standard .las file and extracts 
specified well log traces 
TWOFOOT 6/25/98 
[DIRS 113653] 
Rautman and 
McKenna 1998 
[DIRS 107442], 
p. 1020 
Reads an ASCII file of well log data and resamples it 
on a user-specified spacing, averaging values over 
two-foot intervals 
COREGPC 5/22/99 
[DIRS 113671] 
Rautman 1999 
[DIRS 109035], 
p. 307 
Postprocesses a porosity simulation (GSLIB format) 
in standard-normal format and an unconditional 
simulation with the same spatial correlation structure, 
and generates a co-regionalized simulation of a 
secondary property given a correlation coefficient 
between the two variables 
ZEOLITE5 9/10/98 
[DIRS 113682] 
Rautman and 
McKenna 1998 
Postprocesses a set of simulated Ks values 
(GSLIB format) together with an indicator model of 
[DIRS 107442], 
p. 1266 
zeolite alteration that is used as a template to insert 
random Gaussian Ks values at altered grid notes; 
appropriate only for altered units 
VITROPHYRE 9/10/98 
[DIRS 113694] 
Rautman and 
McKenna 1998 
[DIRS 107442], 
p. 1274 
Postprocesses a set of simulated Ks values 
(GSLIB format) together with the corresponding 
porosity simulation and substitutes uniform random 
Ks values at grid nodes where the simulated porosity 
value is less than 0.05; appropriate only for TSw 
model unit. 
COORDS 12/17/98 Rautman and Adds Nevada state plane (x,y,z) coordinates to a 
[DIRS 113704] McKenna 1998 
[DIRS 107442], 
p. 1287 
GSLIB-format output file given a grid specification 
and a corresponding structure contour file of top 
elevations and thicknesses 
STRATC4 4 
[DIRS 113708] 
Appendix A Computes unit-specific stratigraphic coordinates 
using Equation 6.4-2 from Section 6.4.3.2 using a 
SIGMA PLOT transform (macro). 
NOTE: Formal version numbers were not used for software routines under SNL QAIP 19-1 [DIRS 129329]; instead, 
the date that the documentation associated with the routine was entered into the referenced scientific 
notebook is shown. 

Microsoft Excel 95 and/or Excel 97 were used to compile and organize rock properties 
measurements by drill hole and depth, and later by modeling unit prior to writing the final ASCII 
text files used as input to the modeling algorithms. Standard cell-based arithmetic operations of 
Excel were used on a temporary basis to check for and identify duplicate sample data (by depth), 
but the results of these calculations did not change any measurement values, and the columns 
involved were deleted prior to writing the final ASCII text input files. Standard cell-based 
arithmetic operations were also used to construct two new variables in selected data files related 
to hydrous-phase mineral alteration. One variable represents the total hydrous mineral phase 
content, and was constructed simply as the sum of the four relevant mineral fractions described 
in Section 6.4.3.1. The other variable is an indicator variable constructed using the logic of 
Equation 6.4-1, also described in Section 6.4.3.1. These computations are immediately verifiable 
by visual inspection. Cell-based arithmetic was also used to adjust the core bound-water 
contents for parity with the petrophysically based bound-water contents, as described in 
Section 6.4.7.1. This computation also is immediately verifiable by visual inspection. 
SIGMA PLOT, versions 2.0 and 5.0, was used to compile and organize rock properties 
measurements and to generate graphic displays of drill hole data for determining that the 
selection and organization of the drill hole data was conducted properly. SIGMA PLOT 
arithmetic spreadsheet operations were also used to create the bound-water content variable 
described in Sections 6.4.3.1 and 6.4.7.1 as the simple difference between two original variables. 
This calculation is immediately verifiable by visual inspection. SIGMA PLOT was also used to 
generate various statistical measures, including histograms, cumulative distribution functions, 
crossplots, and linear regressions of selected data sets. 
EVS, version 3.75, was used to generate selected graphic visualizations (report figures, including 
block diagrams and cross-sectional views: e.g., Figures 6.5-1 [top half] and 6.5-2) of the 
completed rock properties models in stratigraphic coordinates only. The geostatistical and other 
modeling capabilities of EVS were not used for this analysis. 
VARIO, versions 1.16 and 1.20, from the geostatistical package UNCERT, was used to compute 
standard 3-D experimental variograms using the input data files. Proper operation of this 
software was verified by visual inspection of the results and by a simple manual verification 
documented in Appendix C. 
VARIOFIT, version 1.20, from the geostatistical package, UNCERT, was used to fit standard 
variogram models to the experimental variograms produced with VARIO. Proper operation of 
this software was verified by visual inspection of the results and by a simple manual verification 
documented in Appendix D. 
HISTPLT, version 1.2, from the geostatistical software library GSLIB (Deutsch and 
Journel 1992 [DIRS 100567]), was used to generate histograms and summary statistics for 
various data sets, including quality checks of completed simulations and other models. Proper 
operation of this software was verified by visual inspection of the results and by comparison of 
results with similar values produced by other software (especially SIGMA PLOT, above). 

GAM3, version 1.2, from the geostatistical software library GSLIB (Deutsch and Journel 1992 
[DIRS 100567]), was used to generate exhaustive 3-D variogram data for plotting as a quality 
checks of completed simulations and other models. Proper operation of this software was 
verified by visual inspection of the results. 

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4. INPUTS 
4.1 DIRECT INPUTS 
There are seven different classes of data used as input to rock properties modeling. Four of these 
categories (Sections 4.1.1 through 4.1.4) involve actual measurements of rock material 
properties, either in the laboratory or by calculation from geophysical measurements obtained in 
the field. A fifth class of property (Section 4.1.5) is also derived from in situ geophysical 
measurements; it is sufficiently different in nature and used for a different purpose, therefore it is 
discussed separately. The remaining two classes of input data (Sections 4.1.6 and 4.1.7) consist 
of the stratigraphic contacts bounding the four rock properties modeling units. The sixth class 
consists of actually observed (measured) contacts. The seventh class involves interpreted or 
modeled contacts, used in cases where a given drill hole did not go deep enough to penetrate a 
unit completely. These seven types of data are described in the paragraphs that follow. Data 
tracking numbers (DTNs) associated with each type of data are provided in tables in the relevant 
section. The description provided for the seven classes of data constitutes the justification for 
their selection as appropriate inputs. Each discussion documents data attributes that were the 
basis for their selection as direct inputs. 
4.1.1 Laboratory Core Porosity Data 
Laboratory measurements of porosity have been obtained for core samples taken from boreholes 
within both the repository block and immediately adjoining areas. The measurement process is 
described by Flint (1998 [DIRS 100033], pp. 11 to 17). DTNs associated with the 
laboratory-measured core porosity values used in the rock properties model are presented in 
Table 4-1. 
Table 4-1. Source of Input Laboratory Core Porosity Data 
Data Source Description Reference 
RH core porosity data DTN: MO0109HYMXPROP.001 [DIRS155989] 
RH porosity, SD-6 DTN: GS980808312242.014 [DIRS 106748] 
RH porosity, WT-24 DTN: GS980708312242.010 [DIRS 106752] 
DIRS=Document Input Reference System; DTN=data tracking number; RH=relative 
humidity 
Flint reports two porosity values for each sample: one obtained after drying the sample in a 
relative humidity oven at elevated humidity levels (60C and 65 percent relative humidity), and 
the other after drying in a 105C oven (oven-dried) and ambient, but very low relative humidity 
(Flint 1998 [DIRS 100033], pp. 11 to 17, Table 7). The distinction is important, as Flint has 
demonstrated that matrix saturated hydraulic conductivity is better correlated with the relative 
humidity porosity values (Flint 1998 [DIRS 100033], pp. 17 and 32 to 33, Figure 12). Because 
part of the intent of this rock properties modeling exercise is to model spatial variability in the 
hydraulic conductivity of the site, the relative humidity core porosity values were used 
exclusively as input to the geostatistical modeling of porosity. 

4.1.2 Calculated Petrophysical Porosity Data 
Borehole petrophysical logs have been converted to quantitative estimates of porosity in 
Combined Porosity from Geophysical Logs (CRWMS M&O 1999 [DIRS 109022], 
pp. 5-1 to 5-2). These data provide substantial information regarding spatial heterogeneity 
across the entire site area (Figure 1-1), particularly for regions remote from the repository block 
for which no core samples exist. Use of data from these regions outside the immediate rock 
properties model boundaries is important in identifying long-range spatial correlation patterns 
that reduce uncertainty within the modeled area. The DTNs associated with the calculated 
petrophysical porosity data are presented in Table 4-2. 
Table 4-2. Source of Input Petrophysical Porosity Data 
Data Source Description Reference 
POREF, VWC DTNs: MO0010CPORGLOG.002 [DIRS 155229], 
MO0010CPORGLOG.003 [DIRS 155959] 
DIRS=Document Input Reference System; DTN-data tracking number; 
POREF= Effective Porosity or RH Porosity; VWC=volumetric water content
Combined Porosity from Geophysical Logs generated two different porosity values using 
petrophysics (CRWMS M&O 1999 [DIRS 109022], p. 5-1). These values are identified in the 
files as effective porosity (POREF), which corresponds approximately to the relative humidity 
laboratory core measurements, and total porosity (POROTOT), corresponding approximately to 
the 105C oven-dried samples. The data files in Table 4-2 also include a calculated 
petrophysical value, VWC, representing the volumetric water content, derived from neutron 
logging (CRWMS M&O 1999 [DIRS 109022], p. 5-2). Values of VWC have been substituted 
for POREF in certain geologic intervals and under certain circumstances that are described in 
Section 6. The method of calculating porosity and volumetric water content followed standard 
industry practice (CRWMS M&O 1999 [DIRS 109022], Section 5). 
4.1.3 Laboratory Measured Secondary Property Data 
A second class of laboratory measurements performed on core samples consists of secondary 
material properties that generally are of greater interest in design or performance assessment 
modeling than simple porosity (completed models are referred to in Section 6 as derivative 
properties). Laboratory values of bulk density, matrix saturated hydraulic conductivity (Ks) 
(Flint 1998 [DIRS 100033], pp. 11 to 17), thermal conductivity, and additional measurements of 
porosity (specifically those correlated with the secondary properties) were also measured for 
selected samples, in addition to the primary porosity data of Section 4.1.1. This measurement 
of multiple properties on the same physical specimen allows identification of statistically valid 
cross-variable correlations. Such correlations thus permit modeling the spatial variability of 
these secondary or derivative properties in regions where no actual measurements of these 
properties exist. DTNs associated with the secondary laboratory property data are presented in 
Table 4-3. 

Table 4-3. Source of Input Secondary Property Data 
Data Source Description Reference 
Porosity, bulk density, Ks data DTN: MO0109HYMXPROP.001 [DIRS 155989] 
Porosity, thermal conductivity samples DTN: SNL01A05059301.007 [DIRS 108980] 
Thermal conductivity, NRG- holes DTN: SNL01A05059301.005 [DIRS 109002] 
DIRS=Document Input Reference System; DTN= Data Tracking Number; 
NRG= North Ramp Geologic 
4.1.4 X-Ray Diffraction Indicators of Mineral Alteration 
The identification and modeling of hydrous-phase mineral alteration (see Section 6.2.4) as it 
affects matrix Ks is complicated by a general scarcity of both mineralogic and hydraulic 
conductivity data across the entire Yucca Mountain site area. As described in more detail 
(Section 6.2.4), the approach is to use both direct (x-ray diffraction analyses [XRD]) and indirect 
(petrophysical data) in combination to model this type of alteration. Quantitative XRD 
mineralogic data (as mineral fractions of several zeolite minerals and smectite clays) are used as 
input to the alteration modeling portion of the rock properties modeling effort. The mineralogic 
data are both converted to hard (certain) indicators of mineral alteration and used to calibrate 
petrophysical data (Section 4.1.5) as soft (uncertain) indicators. Generation of both types of 
indicators is described in Section 6.4.7. The DTN associated with the XRD mineralogic analyses 
is presented in Table 4-4. 
In Technical Work Plan for: The Integrated Site Model, TWP-NBS-GS-000003 
REV 05 (BSC 2004 [DIRS 169635]) there was no plan to qualify the XRD mineral abundance 
data cited by the rock properties model. However, because the qualification report prepared for 
the mineralogic model also qualifies the XRD mineral abundance data for the rock properties 
model it was included in this report. The qualification report was prepared in accordance with 
the plan contained in the Technical Work Plan for: The Integrated Site Model, 
TWP-NBS-GS-000003, Rev 05 (BSC 2004 [DIRS 169635]), Appendix A. Documentation of 
the qualification of unqualified mineral alteration data are discussed and documented in 
Appendices E, F, and G. Information listing the sources and related qualification activities for 
DTN: LA9910DB831321.001 [DIRS 113568] are provided in Appendix H. The documentation 
provided by Appendices E through H qualifies all of the XRD mineral abundance data cited as 
direct input by the rock properties model. 
Table 4-4. Source of Input X-ray Diffraction Mineralogy Data 
Data Source Description 
Reference 
Zeolite mineral contents 
DTN: LA9910DB831321.001 [DIRS 113568] 
DIRS=Document Input Reference System; DTN=data tracking number 

4.1.5 Petrophysical Indicators of Hydrous-Phase Mineral Alteration 
The use of both direct and indirect indicators to model hydrous-phase mineral alteration as it 
affects the magnitude and spatial distribution of matrix Ks was noted in Section 4.1.4. 
Petrophysically derived data, specifically the POROTOT and POREF calculated porosity values 
described in Section 4.1.2, are used to generate soft indicators of mineral alteration. Soft 
indicators are data that provide indirect indications of hydrous phase mineral alteration such as 
porosity and hydraulic conductivity. The use and calibration of soft indicators is documented in 
Section 6.4.7. The DTNs associated with the petrophysical porosity data used to create the 
petrophysical indicators of hydrous-phase mineral alteration are presented in Table 4-5. The 
mechanics of generating and calibrating those indicators is presented in Section 6.4.7. 
Table 4-5. Source of Input Petrophysical Data Used for Identification of Mineral Alteration 
Data Source Description Reference 
POREF, POROTOT, VWC DTNs: MO0010CPORGLOG.002 [DIRS 155229], 
MO0010CPORGLOG.003 [DIRS 155959] 
DIRS=Document Input Reference System; DTN=data tracking number; POREF=Effective 
Porosity; POROTOT=Total Porosity; VWC=volumetric water content 
4.1.6 Observed (Measured) Lithostratigraphic Contacts 
Rock properties modeling has been conducted within stratigraphic coordinates, which represent 
the relative vertical position of each measured property value within a model unit. This 
stratigraphic coordinate conversion requires depth values for both the upper and lower contact of 
each aggregate model unit in each borehole, as described in Section 6.3.2. Typically, the 
required depth values were physically observed, either in core, in geophysical logs, or in 
downhole video records. DTNs associated with the observed lithostratigraphic contact data are 
presented in Table 4-6. 
Table 4-6. Source of Input Observed Lithostratigraphic Contacts 
Data Source Description Reference 
Contacts for Geologic Framework Model 3.0 DTN: MO0004QGFMPICK.000 [DIRS 152554] 
Contacts for USW SD-6 DTN: SNF40060298001.001 [DIRS 107372] 
Contacts for USW WT-24 DTN: SNF40060198001.001 [DIRS 107239] 
DIRS=Document Input Reference System; DTN=data tracking number 
4.1.7 Modeled Lithostratigraphic Contacts 
The vast majority of model-unit contacts required for the stratigraphic coordinate conversion are taken 
directly from a tabulation of observed contacts (Table 4-6). However, a small number of contacts were 
projected using Geologic Framework Model version 3.0 (DTN: MO9804MWDGMFM03.001 
[DIRS 109050]) or Geologic Framework Model version 3.1 (DTN: MO9901MWDGFM31.000 
[DIRS 103769]) because the drill hole did not fully penetrate the model unit. This situation most 
frequently involves projecting the depth of the lower contact of a given modeling unit where the 
borehole bottomed within that unit. A few upper contacts had to be projected where the top of a 
particular model unit had been eroded at the borehole location. The stratigraphic coordinate 

conversion is also complicated for boreholes that intercept a fault. In this case, it has been 
necessary to use modeled isochore information to reconstruct the missing portion of the 
stratigraphic section and infer the unfaulted depths based on unit-thickness trends obtained 
using the Geologic Framework Model. These instances are documented in the scientific 
notebooks associated with this modeling activity (Rautman, C. and McKenna, S. 1998 
[DIRS 107442] and Rautman, C. 1999 [DIRS 109035]. DTNs associated with the geologic 
framework models used to extract the projected lithostratigraphic contacts are presented in 
Table 4-7. Note that Rock Properties Model version 3.1 (DTN: MO9901MWDGFM31.000 
[DIRS 103769]) was created simply by inserting the new drill hole data for holes SD-6 and 
WT-24 into the data set from rock properties model version 3.0 (created using projected contacts 
from Geologic Framework Model Version 3.0 (DTN: MO9804MWDGFM3.001 
[DIRS 109050]) and regenerating the model. 
Use of projected stratigraphic contacts for boreholes, where the observed contacts were not 
available, is justified because this is one of the specific purposes of the GFM as described in 
Section 1 of the report (BSC 2004, [DIRS 170029]). The primary difference between the 
Geologic Framework Model version 3.0 and version 3.1 is the inclusion of lithostratigraphic 
contact data from boreholes USW SD-6 and USW WT-24 (BSC 2004 [DIRS 170029] 
Section 6.3.3). The principle changes from Geologic Framework Model version 3.1 to 
version 2000 are to correct the thickness of the RHH model layer, and an anomalous flexure in 
the extreme northern edge of the model (BSC 2004 [DIRS 170029] Section 6.3.4). Neither of 
these changes impacts the rock properties model. This is because the RHH is not a model layer 
that is used by the rock properties model, and the extreme northern edge of the geologic 
framework model exceeds the boundary of the rock properties model, as shown on Figure 4. 
Therefore, this impact assessment will focus on the changes in predicted stratigraphic contacts 
between Geologic Framework Model version 3.0 and version 3.1 due to the inclusion of 
boreholes USW SD-6 and USW WT-24. 
Figures 6.4-1 through 6.4-4 shows that changes caused by the inclusion of borehole USW SD-6 
are constrained by the surrounding nearest boreholes: USW H-5, USW H-6, USW SD-12, and 
USW UZ-6. All four of these surrounding boreholes penetrate the tops of the PTn, TSw, CHn, 
and Tcp layers model layers. These surrounding boreholes already constrain the stratigraphic 
tops of the model layers. Therefore, the inclusion or exclusion of USW SD-6 contact data would 
have a minimal impact on the predicted tops for the rock properties model layers. The base of the 
lowest model layer, Tcp (equivalent to the top of the Tcb layer), is not penetrated by borehole 
USW SD-6. However, the base of the Tcp layer is observed in the nearby surrounding 
boreholes: USW H-5, USW H-6, USW SD-12, and USW H-3 (DTN: MO0004QGFMPICK.000 
[DIRS 152554]). Therefore, changes in predicted contacts caused by the inclusion of borehole 
USW SD-6 will be minimal between version 3.0 and 3.1 of the geologic framework model 
because of the close proximity of surrounding boreholes that already constrain the model layers. 
Similarly, for borehole USW WT-24 model layer, tops and bottoms are constrained by the 
surrounding nearest boreholes: USW G-2, USW WT-18, and USW UZ-14, as shown by 
Figures 6.4-1 through 6.4-4. USW WT-24 partially penetrates or ends within model layer CHn. 
The surrounding three boreholes penetrate to the top of model layer Tcp. Therefore, again the 
surrounding boreholes provide control or constrain the location of the model layers. Therefore, 
changes in the predicted stratigraphic contacts caused by inclusion of borehole USW WT-24 will 

be minimal between version 3.0 and 3.1 of the geologic framework model due to the close 
proximity of surrounding boreholes that constrain the model layers. 
In addition, as shown by Figure 6.4-5 the rock properties model is constructed in stratigraphic 
coordinates. Therefore, all borehole data and results are located relative to their position in the 
model layer as shown by Equation 2. Therefore, changes caused by the difference between the 
use of the Geologic Framework Model versions 3.0, as compared to version 3.1, for predicted 
contacts would only result in the shifting of the projected properties when modeled in 
stratigraphic coordinates. Examination of the results of the model layer representations of the 
parameters, as shown by Figures 6.5-16, 6.5-17, and 6.5-18, do not indicate that any 
discontinuities exist in the parameter representations. 
Therefore, based on the evaluation of existing boreholes that surround USW SD-6 and USW 
WT-24, and the lack of observed discontinuities in the property representations, there is no 
significant impact to the rock properties model caused by the use of the Geologic Framework 
Model 3.0 and 3.1 instead of the current version of the Geologic Framework Model 2000 
(DTN: MO0012MWDGFM02.002 [DIRS 153777]). 
Table 4-7. Source of Input Projected Lithostratigraphic Contacts and Isochore Information 
Data Source Description Reference 
Geologic Framework Model 3.0 DTN: MO9804MWDGFM03.001 [DIRS 109050] 
Geologic Framework Model 3.1 DTN: MO9901MWDGFM31.000 [DIRS 103769] 
Geologic Framework Model 2000 DTN: MO0012MWDGFM02.002 [DIRS 153777] 
DIRS=Document Input Reference System; DTN=data tracking number 
NOTE The Geologic Framework Model 2000 DTN: DTN: MO0012MWDGFM02.002 
[DIRS 153777] is not cited as direct input but listed for completeness and 
traceability. 
4.2 CRITERIA 
The acceptance criteria that will be used by the Nuclear Regulatory Commission (NRC) to 
determine whether the technical requirements have been met are identified in the Yucca 
Mountain Review Plan, Final Report (YMRP; NRC 2003 [DIRS 163274]). An evaluation of the 
acceptance criteria against the information in this model report determined that criteria other than 
those documented in the TWP should be addressed. The identification of the technical users and 
their specific use of the product output, rather than the intended use of the product output, was 
basis for this re-examination of the acceptance criteria. The result of this re-examination was the 
identification of Acceptance Criteria not listed in the TWP. 
2.2.1.3.9.3, Radionuclide Transport in the Saturated Zone 
Acceptance Criterion 1: System Description and Model Integration Are Adequate. 
Subcriterion (2): The description of the aspects of hydrology, geology, geochemistry, design 
features, physical phenomena, and couplings, that may affect radionuclide transport in the 
saturated zone, is adequate. For example, the description includes changes in transport 
properties in the saturated zone, from water-rock interaction. Conditions and assumptions in the 

abstraction of radionuclide transport in the saturated zone are readily identified, and consistent 
with the body of data presented in the description; 
Acceptance Criterion 2: Data Are Sufficient for Model Justification. 
Subcriterion (3): Data on the geology, hydrology, and geochemistry of the saturated zone, 
including the influence of structural features, fracture distributions, fracture properties, and 
stratigraphy, used in the total system performance assessment abstraction, are based on 
appropriate techniques. These techniques may include laboratory experiments, site-specific field 
measurements, natural analogue research, and process-level modeling studies. As appropriate, 
sensitivity or uncertainty analyses used to support the U.S. Department of Energy total system 
performance assessment abstraction are adequate to determine the possible need for additional 
data. 
4.3 CODES, STANDARDS, AND REGULATIONS 
No specific formally established codes, standards, or regulations have been identified as applying 
to this modeling activity other than those previously cited in this report. 

INTENTIONALLY LEFT BLANK 

5. ASSUMPTIONS 
The principal assumption involved in constructing the rock properties models involves the use of 
porosity-as-a-surrogate for modeling the spatial variability of other, secondary material 
properties that are typically of greater interest in performance modeling than porosity itself, but 
which are less sampled when compared to porosity at Yucca Mountain. This concept of using 
abundant porosity data as a surrogate for other properties is not new. Rautman and McKenna 
(1997 [DIRS 100643], p. 13) list a sampling of published references using the 
porosity-as-a-surrogate technique. For Rock Properties Model version 3.0, porosity has been 
used to model the spatial distributions of bulk density, saturated hydraulic conductivity, and 
thermal conductivity (MO9804MWDGFM03.001 1998 [DIRS 109050]). 
The concept of porosity as a surrogate is based on empirically observed correlations of porosity 
with a secondary properties. A consequence of undersampling is that the spatial variability of 
the undersampled variable cannot be described confidently on a stand-alone basis, let alone such 
that the joint spatial continuity patterns of the two (or more) variables can be reproduced 
simultaneously. It is important to understand that modeling the spatial distribution of several 
material properties without considering the inter-variable correlations can lead to highly 
unrealistic input to physical-process modeling codes, which in turn can lead to highly 
unreasonable estimates of performance parameters. Sampling randomly from separate 
(univariate) probability density functions may easily produce such un-physical combinations as a 
low porosityhigh thermal conductivityhigh hydraulic conductivity tuff. The severity of 
neglecting cross-variable correlations in modeling spatially variable domains increases as 
physical-process modeling captures multiple coupled processes. 
Using porosity as a surrogate for various other materials in modeling, Yucca Mountain is 
supported by consideration of the physics involved in the site-specific rock units being modeled. 
For example, for a given rock type, increasing the volume of pore space must decrease the bulk 
density of the rock mass. The part of the rock that isnt there is available to hold fluids, but it 
contributes nothing to the total mass contained within a unit volume: the definition of bulk 
density. Again for a given rock type, the conduction of heat energy through the material is 
directly related to the density (or, inversely to the pore space) of the material. All else being 
equal, a higher porositylower density tuff will conduct heat less readily, leading to a lower 
thermal conductivity value. Note here that it is the total amount of void space in a rock that 
affects thermal conductivity, not simply the amount of pore space that is conducting water within 
the unsaturated zone (UZ). This fact has important implications to modeling of whole-rock 
thermal conductivity in the presence of large-diameter (up to 1 meter) lithophysal cavities. 
As another example, although hydraulic conductivity is not generally well correlated with 
porosity across many classes of soils and/or rock materials, the empirical observation at Yucca 
Mountain is that this correlation is quite strong within limited groupings of rock types. 
Specifically, both welded and nonwelded lithologies appear to be associated with a continuum of 
saturated hydraulic conductivity values (see Section 6.4.5.1). Evidently, unless affected by some 
additional physical process (such as zeolitic or other similar alteration), there is a strong 
relationship between the progressive, overall reduction in porosity and the progressive reduction 
in the average diameter of the passages between interconnected pores (which is what exerts 
principal control on the flow of water through the existing pore space) across this continuum of 

nonwelded to densely welded materials. Conversely, a genetic process that changes the diameter 
and/or geometry of the pore throats (e.g., zeolitization), while not commensurately filling in the 
total quantity of void space, can reduce the hydraulic conductivity of the rock by orders of 
magnitude while leaving the porosity essentially unchanged. In fact, both these cases are 
observed and modeled at Yucca Mountain. 
This assumption does not require confirmation. 

6. MODEL DISCUSSION 
6.1 INTRODUCTION AND ROCK PROPERTIES MODEL OVERVIEW 
Yucca Mountain is located in the southwestern Nevada volcanic field and consists of faulted and 
tilted blocks composed of layered sequences of ash-flow, ash-fall, and bedded tuffs of Miocene 
age (Sawyer et al. 1994 [DIRS 100075], pp. 1304 to 1318). 
The rock properties model (RPM) is a 3-D, discretized numerical representation of spatial 
variability and heterogeneity of a number of fundamental bulk and hydrologic material properties 
for the majority of the rocks within the UZ at Yucca Mountain. The model divides the model 
volume into four internally lithologically similar model units. In descending stratigraphic order 
these are (1) the Paintbrush nonwelded (PTn) model unit, (2) the Topopah Spring welded (TSw) 
model unit, (3) the Calico Hills nonwelded (CHn) model unit, and (4) the Prow Pass Tuff (Tcp). 
The Tiva Canyon Tuff was not modeled for two primary reasons, which are discussed in detail in 
Rautman and McKenna (1997 [DIRS 100643], p. 10). First, such a model is not needed because 
the Tiva Canyon Tuff is highly fractured, so that matrix properties are subordinate to the role of 
fractures in hydrologic flow. Second, because most boreholes are located in ravines eroded into 
the Tiva, few boreholes penetrate a meaningful stratigraphic section of the Tiva Canyon Tuff and 
data are accordingly sparse. The Bullfrog Tuff underlies the Prow Pass Tuff and was not 
modeled, as the amount of borehole rock property data is significantly less when compared to the 
four overlying model layers. This can be verified by examining Table 7 from Flint (1988 
[DIRS 100033]). Table 6.1-1 presents a correlation of the RPM model units with other project 
stratigraphies. For all four model units, the modeled material properties are: 
 
Matrix porosity 
 
Whole-rock bulk density 
 
Matrix saturated hydraulic conductivity. 
For the TSw model unit, additional material properties are: 
 
Lithophysal porosity: 
 
Whole-rock thermal conductivity. 

Table 6.1-1. Correlation Chart for Model Stratigraphy 
1 1 Model Unit 
GroupMemberZoneSubzoneium Qal, Qc 
Timber Mountain Group Tm 
Paintbrush Group 
TillPre-Tiilll 
lliln 
TSwllCHn 
Crater Flat Group 
Stratigraphic UnitAbbreviationRock Properties 
Formation 
Alluvium and ColluvTp 
va Canyon Tuff Tpc 
Crystal-Rich Member Tpcr 
Not Modeled 
Crystal-Poor Member Tpcp 
Vitric zone Tpcpv 
Densely welded subzone Tpcpv3 
Moderately weded subzone Tpcpv2 
Nonweded subzone Tpcpv1 
va Canyon bedded tuff Tpbt4 
Yucca Mountain tuff Tpy 
Pre-Yucca Mountain bedded tuff Tpbt3 
Pah Canyon Tuff Tpp 
Pre-Pah Canyon bedded tuff Tpbt2 
PTn 
Topopah Sprng Tuff Tpt 
Crystal-Rich Member Tptr 
Vitric zone Tptrv 
Nonweded subzone Tptrv3 
Moderately weded subzone Tptrv2 
Densely welded subzone Tptrv1 
Nonlithophysal zone Tptm 
Lithophysazone Tptrl 
Crystal-Poor Member Tptp 
Upper lithophysal zone Tptpul 
Middle nonlithophysa zone Tptpmn 
Lower lithophysal zone Tptpll 
Lower nonthophysal zone Tptp 
Vitric zone Tptpv 
Densely welded subzone Tpcpv3 
Moderately weded subzone Tpcpv2 
Nonweded subzone Tpcpv1 
Pre-Topopah Spring bedded tuff Tpbt1 
Calico Hills Formation Ta 
Bedded tuff (Tacbt) 
Tc 
Prow Pass Tuff Tcp 
Pre-Prow Pass bedded tuff (Tcbbt) 
Tcp 

Table 6.1-1. Correlation Chart for Model Stratigraphy (Continued) 
1 1 Model Unit 
GroupMemberZoneSubzonellStratigraphic UnitAbbreviationRock Properties 
Formation 
Bufrog Tuff Tcb 
Pre-Bullfrog Tuff bedded tuff (Tcbbt) 
Tram Tuff Tct 
Bedded tuff (Tctbt) 
Not Modeled 
Source: DTN: MO9510RIB00002.004 [DIRS 103801] 
NOTE: 1Shaded areas represent header lines for subdivided units. 
Lithophysal porosity and the two whole-rock properties in the above list are intended to represent 
the effect of the large (up to 1 meter in diameter) lithophysal cavities that are a very prominent 
feature of certain intervals within the Topopah Spring Tuff and the potential horizon. The rock 
properties model is tied geometrically to the bounding surfaces of model units within Geologic 
Framework Model (GFM) 3.1 (MO990MWDGFM31.000 [DIRS 103769]). 
There are three fundamentally different types of models included within the RPM. First, a suite 
of 50 simulated property models (or realizations) have been generated for each material property 
using Monte Carlo-style geostatistical conditional simulation techniques. The number of 
simulated property models generated is a balance between the number of times the material 
property will be sampled to generate each representation, and the computational resources 
needed to generate the models. These models reproduce both the measured data values and the 
full range of spatial heterogeneity and spatial continuity exhibited by those data. At locations 
other than those of the measured values, material properties are generated (simulated) randomly 
from the local conditional probability density functions, and the variability of these values is 
interpreted to represent the uncertainty associated with predictions of material properties at these 
unsampled locations. The second types of models included in the RPM are summary expectation 
(E-type) models for each rock property. These E-type models are computed as the 
node-by-node mathematical expectation (average) across the 50 simulated realizations, and they 
represent the property values most likely to be encountered at each discretized location. The 
third type of model included in the RPM is also a summary-type model, and these are computed 
as the node-by-node standard deviations of the 50 individual simulated property models. This 
third type of model is intended to provide users with a first-pass estimate of geologic uncertainty 
throughout the repository site, where geologic uncertainty consists of that uncertainty that 
results from less-than-exhaustive site characterization. 
The two primary data sources used to model the spatial variability of rock properties at Yucca 
Mountain are laboratory-measured core samples and down-hole measured petrophysical property 
values. Porosity is the primary variable for which spatial variability and heterogeneity have been 
modeled directly from measured values. The other material properties are derivative properties 
that have been generated from the simulated porosity models using the geostatistical technique 
known as linear coregionalization. Additional information has been incorporated from x-ray 

diffraction (XRD) measurements of (principally) zeolite mineral content. This form of rock 
alteration is considered to be an important control on the variability of matrix saturated hydraulic 
conductivity. Correlation between XRD alteration mineralogy and down-hole petrophysical 
measurements has been used to extend modeling of this type of alteration into regions from 
which no mineralogical samples have been obtained. 
The rock properties models are intended principally for use as input to numerical 
physical-process modeling, such as of ground-water flow and/or radionuclide transport. In 
anticipation of such use on a site scale, the models are discretized uniformly in the horizontal 
plane on a 200 m by 200 m (656.2 ft by 656.2 ft) grid. Vertical discretization is unit-dependent 
at a nominal 2 m (approximately 7 ft) spacing in the PTn model unit and 5 m (approximately 
16 ft) spacing elsewhere. The rock property models can be regenerated at virtually any desired 
level of discretization to meet the needs of downstream model users. 
6.2 CHANGES BETWEEN MODEL VERSIONS 
Four significant changes in modeling methodology and data from those used in RPM 2 (Rautman 
and McKenna 1997 [DIRS 100643] page 8; note that RPM 2 is the first numbered rock 
properties model) have been implemented for the current version of the rock properties model 
(RPM 3.1). These changes are described briefly in this section. Expanded discussions are 
presented as appropriate in Section 4.1 and 6.4. 
6.2.1 Changes in Model Unit Subdivisions and Modeled Region 
The first major change in the rock properties models (RPM) 3.1 is that the combined Calico 
Hills-Prow Pass model unit of RPM 2 (Rautman and McKenna 1997 [DIRS 100643]) has been 
subdivided into two new modeling units along the formation boundary at the top of the Prow 
Pass Tuff (Table 6.1-1). This revised subdivision scheme was adopted because the area of rock 
properties modeling for RPM 3.1 is smaller than that for RPM 2. The smaller model area is a 
consequence of increased focus on the immediate repository area by the YMP site-scale UZ 
hydrologic model. This decrease in the modeled volume resulted in an improved spatial 
distribution of boreholes within the remaining area and decreased the need to aggregate data in 
search of statistical mass, as that term was described by Rautman and McKenna (1997 
[DIRS 100643], pp. 10 to 11). 
6.2.2 Adjustment of Model Unit Contacts to Reflect Mineralogy 
A second change is the adjustment of contacts between the welded interior core of the Topopah 
Spring Tuff (model unit TSw) and the respectively overlying and underlying PTn and CHn 
nonwelded units. For RPM 3.1, these contacts were moved outward one lithostratigraphic 
interval, with the result that the breaks are now between the Tptrv3Tptrv2 and the 
Tptpv2-Tptpv1 lithostratigraphic units (Table 6.1-1). The rationale for this contact adjustment is 
that the revised boundaries better separate rocks with the potential to have been altered to zeolite 
minerals from high-temperature-crystallized rocks that almost never undergo such alteration. 
The spatial distribution of one derivative property, matrix saturated hydraulic conductivity, is 
strongly affected by the presence or absence of zeolitic minerals; thus a more accurate 

representation of this particular transition is the objective. Note that such modeling of alteration 
has been conducted only for the CHn and Tcp model units. 
6.2.3 Revised Petrophysical Data 
The third and most significant, but perhaps least obvious change between the rock properties 
models of RPM 2 and 3.1, is that all of the petrophysically based porosity data have been 
recalculated by CRWMS M&O (1999 [DIRS 109022], pp. 5-1 to 5-2) following identification of 
a number of problems with the earlier data sets (see Rautman and McKenna 1997 
[DIRS 100643], pp. 138 to 139). Specifically, the original petrophysical porosity data for RPM 
2 involved two different calculation methods described by Nelson (1996 [DIRS 101270], 
pp. 17 to 23 and CRWMS M&O 1996 [DIRS 100632], pp. 26 to 29), with the result that some 
spatial variability appeared to represent only the source of the calculated values (see discussion 
in Rautman and McKenna 1997 [DIRS 100643], pp. 26 to 29 and 49 to 51). A separate issue 
involved the absolute magnitude of some of the porosity values calculated by Nelson 
(1996 [DIRS 101270]; the data values themselves are contained in GS960708312132.002 
(1996 [DIRS 113584]) within selected stratigraphic intervals, principally the densely welded 
vitric subzones (Tptrv1, Tptpv3) of the Topopah Spring Tuff. As a consequence, it was unclear 
if some portion of the inferred spatial heterogeneity was related simply to the mathematical 
calculations and not to physical geology. 
The RPM 3.1, (CRWMS M&O 1999 [DIRS 109022], pp. 4-1 to 4-25) re-examined all of the 
underlying petrophysical logs, and processed the necessary fundamental petrophysical traces 
through a common data-reduction algorithm to calculate porosity. Specifically, use of the 
balanced-water approach using resistivity logs (CRWMS M&O 1996 [DIRS 100632], p. 28) to 
calculate water saturation and volumetric water content was abandoned. Instead, porosity was 
calculated directly from the downhole density logs (separately above and below the water table), 
with volumetric water content derived directly from calibrated neutron logs (CRWMS M&O 
1999 [DIRS 109022], pp. 4-13 to 4-25 and pp. 5-1 to 5-2). This recalculation and common 
processing history eliminate one (nongeologic) source of apparent variability. 
Recalculation of the petrophysical porosity data involved not only a common data-reduction 
algorithm, but also calibration of the input bulk density trace against a set of internal standards 
(CRWMS M&O 1999 [DIRS 109022], pp. 3-1 to 3-2 and 4-2 to 4-5). Bulk density is the 
principal petrophysical measurement used in the porosity calculation. The internal standards are 
provided by four distinctive lithostratigraphic units, at least one of which is present in virtually 
each of the site boreholes. These units are the crystal-rich densely welded vitric (Tptrv1) and 
nonlithophysal (Tptrn) lithostratigraphic units, and the crystal-poor middle nonlithophysal 
(Tptpmn) and densely welded vitric (Tptpv3) units. Based on evidence from recovered drill core 
that these four intervals are of relatively uniform material, a set of density-correction factors was 
developed for each borehole (CRWMS M&O 1999 [DIRS 109022], pp. 4-2 to 4-5, Table 3). 
The relatively uniform character of the four intervals in terms of density is shown in 
Figure 6.4-9. The density logs throughout each borehole were then adjusted proportionately (by 
individual logging run if appropriate) so that the average value in these distinctive zones 
indicated the correct bulk density. 

6.2.4 Revised Approach to Identifying Hydrous-Phase Mineral Alteration 
The term hydrous-phase mineral alteration is used in this report to indicate changes in initial 
(volcanic) mineral compositions that collectively act to reduce the matrix hydraulic conductivity 
of laboratory specimens (and in situ rocks) compared with equivalent unaltered rocks of 
approximately the same porosity (Rautman and McKenna 1997 [DIRS 100643], pp. 35 to 40, 
Figures 25 and 26 and Table 8; Flint 1998 [DIRS 100033], pp. 31 to 33 and Figure 12). Such 
rocks typically exhibit mineralogical alteration either to zeolite minerals, principally but not 
exclusively clinoptilolite and mordenite, or to expandable-layer clay minerals (smectite or 
montmorillonite; Flint 1998 [DIRS 100033], p. 33). Within the two rock properties modeling 
units of principal interest (CHn and Tcp), hydrous-phase mineral alteration translates fairly 
precisely to zeolitic alteration. However, this distinction is maintained with respect to 
petrophysically based indicators of reduced Ks to emphasize that this approach is unable to 
distinguish zeolites and montmorillonitic clays in the downhole environment. 
A fourth change in rock properties modeling procedure involves the approach used to identify 
the spatial distribution of hydrous-phase mineral alteration, which affects the coregionalization 
of hydraulic conductivity with porosity in the lower two (CHn, Tcp) of the four properties 
modeling units. For version 2 of the rock properties models, each grid node was identified as 
either likely or unlikely to exhibit such alteration based on a spatial continuity model identified 
through petrophysical measurements (see Rautman and McKenna 1997 [DIRS 100643], 
pp. 43 to 44 and 57 to 59). For version 3, altered grid nodes were modeled as a weighted 
combination of both hard (X-Ray Diffraction Indicators of Mineral Alteration, Section 4.1.4) and 
soft (Petrophysical Indicators of Hydrous-Phase Mineral Alteration, Section 4.1.5) data. 
Generation and use of the combined alteration data set is described in see Section 6.4.7. 
6.3 PHILOSOPHICAL AND CONCEPTUAL MODELING FRAMEWORK 
This section is intended to provide a relatively high-level conceptual description of the 
geostatistical modeling approach, including a brief discussion of how stochastic 
material-property models such as these fit into the overall performance modeling of the Yucca 
Mountain site. The methodology has been described in some detail by Rautman and McKenna 
(1997 [DIRS 100643], pp. 6 to 69). Additional information regarding geostatistical modeling 
techniques in general may be found by following the references cited in the Rautman and 
McKenna document (1997 [DIRS 100643]). 
6.3.1 Modeling Philosophy and Conceptual Approach 
Licensing of the Yucca Mountain site as a geologic disposal site for nuclear waste will require 
quantitative predictions of waste-isolation performance of both the rocks that form Yucca 
Mountain and the engineered barrier system for an extended period of time into the future. 
These predictions will require the use of numerical modeling techniques in an effort to capture 
critical aspects of highly complex physical processes; for example, the flow of ground-water and 
the transport of radionuclide contaminants under both unsaturated and saturated conditions. 
Modeling the performance of the site will be influenced both by present-day conditions and by 
future conditions that must account for perturbation of ambient conditions by the thermal pulse 
related to the presence of heat-generating radioactive materials and potential changes in climate. 

A fundamental principle involved in the numerical representation of real-world physical 
processes is that the relevant material properties of the modeled domain must be known at all 
positions within that domain. However, in contrast to this requirement for an exhaustive 
spatial description, the process of describing or characterizing a site invariably consists of 
collecting observations of properties or state variables at a limited number of locations, the exact 
positions of which are frequently determined by less-than-optimal external factors. This is 
particularly true for the 3-D characterization of a geologic site, such as Yucca Mountain. 
Because descriptive characterization is limited both by access (particularly to the subsurface) and 
by the availability of resources, that description is necessarily incomplete. Therefore, the 
exhaustive description of a geologic site for purposes of numerical modeling requires the prior 
assumption of some type of conceptual model for the site, which is then implemented to assign 
the necessary properties and other variables at every relevant point in space. 
6.3.1.1 Capturing Heterogeneity 
A slightly refined, but still quite simplified, conceptual model for the site makes use of the fact 
that, at Yucca Mountain (as in many other geologic environments), the lithologic deposits were 
produced by relatively widespread but temporally variable geologic processes. In particular, the 
volcanic activity responsible for the formation of Yucca Mountain was episodic in nature, with 
thick, widespread ash deposits produced by near-instantaneous (geologically speaking) eruptions 
separated by thin inter-eruption deposits that probably represent much longer intervals of time. 
If the time represented by a progressively accumulating geologic deposit is considered to be 
frozen and preserved in the vertical dimension, then the resulting conceptual model is one of 
successive subhorizontal layers, that may be broken and tilted, or folded and otherwise moved 
about at some later time. 
The GFM component of the ISM is such a layered representation of Yucca Mountain. However, 
without further refinement, a layered rock-properties model corresponding precisely to the GFM 
still relies upon the prior assumption that, within each originally subhorizontal layer, the material 
properties of interest are essentially uniform and homogeneous. Geologic studies of the 
volcanogenic rocks at Yucca Mountain and of similar deposits elsewhere in the world indicate 
that the geologic processes responsible for deposition of these materials vary both temporally 
and areally. For example, variations in cooling rates caused by local conditions affect the 
material properties in the resultant rocks. This spatial variation of process has produced spatial 
heterogeneity of material properties in all three dimensions. Yet despite the existence of vertical 
and lateral heterogeneity, the spatial distribution of material properties within geologic layers is 
not simply random. Knowledge of property values at one location imposes limits on the 
values of those properties likely to exist at nearby locations. Therefore, an alternative 
conceptual model of filling-in a geologic framework with values randomly assigned from 
some inferred univariate distribution without regard for other nearby values (spatial correlation) 
is an unnecessary oversimplification (and potentially an unwarranted distortion) of the real 
world. 
6.3.1.2 Heterogeneity Versus Uncertainty 
In contrast to heterogeneity, which is an objective feature of the real world, uncertainty is a 
knowledge-based concept. Distinguishing properly between uncertainty (as a state of imperfect 

information resulting from less-than-complete observation) and spatial heterogeneity (as a state 
of being, unaffected by the availability or lack of information) becomes critically important in 
the application of predictive engineering methods to the geologic environment. Natural earth 
materials are heterogeneous to a far greater degree than most materials of conventional 
engineering interest. Heterogeneity in rock properties affects the actual operation of physical 
processes, even though there is only one, unique heterogeneous reality. The impact of that 
heterogeneity on the numerical approximation (modeling) of those physical processes is only 
compounded by geologic uncertainty. Incomplete information must be accounted for in 
predictive modeling, as must the effects of material properties that are different in different 
locations. A key attribute, therefore, of the rock properties modeling activities, has been the 
description and quantification of the effects of geologic uncertainty on the physical description 
of the Yucca Mountain site. 
6.3.1.3 Geostatistical Methods 
The current approach to modeling rock properties as part of the ISM strikes a balance between 
the inherent simplification of a hollow-shell GFM and the near-infinite degree of complexity that 
is the real world. Selected major lithostratigraphic horizons are used as the constraining 
(framework) boundaries for a statistically based description of the measured rock material 
properties that have been sampled within those boundaries. Geostatistical methods have been 
used to create the exhaustive material property descriptions comprising the rock properties 
model. Geostatistical methods in general are one of a variety of methods for distributing isolated 
measurements of different attributes in space, and thus for modeling spatial heterogeneity. A 
fundamental principle underlying all geostatistical techniques is the quantification and use of 
some measure of spatial correlation, which may be defined informally as the degree to which 
samples close to one another resemble each other more than do samples far away from each 
other, where close and far are defined from the data values. Furthermore, unlike many other 
methods for predicting the material property attributes of a large volume from direct observation 
of a relatively minuscule fraction of that volume, geostatistical methods offer a quantitative and 
more-or-less rigorous approach to the issues of knowledge-based uncertainty discussed in 
Section 6.3.1.2. 
Within the purview of geostatistical methods are two broad classes of algorithms for predicting 
attributes at unsampled locations constrained by some limited set of actual measurements: 
estimation and simulation. Geostatistical estimation is focused on the prediction of the attribute 
values most likely to be encountered at a given spatial position, and may be thought of as 
modeling the expected value of a variable of interest. Geostatistical estimation is most 
frequently described using the term, kriging, and it is simply a weighted-average interpolation 
method using some neighborhood of near-by relevant data. A common thread connecting all 
estimation methodologies (including nongeostatistical ones) is that they are interpolation 
techniques directed toward producing a model in which the estimated values grade progressively 
and generally smooth away from the data locations and away from one another. 
The other broad class of geostatistical methods comprises a variety of simulation algorithms. In 
contrast with estimation, geostatistical simulation places principal emphasis on reproducing the 
input data values and the overall statistical character (including the spatial correlation 
characteristics) exhibited by the data ensemble, the total collection of input values. Models 

produced by geostatistical simulation typically do not grade smoothly between measured data 
values, but rather are more highly variable at the same time that they represent the broad 
heterogeneity structure of the measurements. These techniques are conceptually equivalent to 
the Monte Carlo simulation process frequently employed in engineering analyses. In common 
with other Monte Carlo simulation approaches, the emphasis is less on the specific predicted 
values, which are in effect simply the products of a random number generator with certain 
desirable properties, and much more on evaluation of the space of uncertainty associated with 
some performance measure computed to represent the behavior of the modeled system. 
6.3.1.4 Evaluating the Consequences of Heterogeneity and Uncertainty 
A schematic diagram of the geostatistical process for combining statistical description with the 
Monte Carlo generation of multiple replicate models, which are then evaluated for performance 
consequences are presented in Figure 6.3-1 (see also Rautman and McKenna 1997 
[DIRS 100643], Figure 6). The logic is straightforward Monte Carlo, except that the need to 
capture both heterogeneity and spatial correlation jointly causes the geostatistical simulation 
process to consist of drawing entire intact property models (rather than single property values) 
from a conceptual distribution of alternative realities. 
As shown by the upper row of panels in Figure 6.3-1, the individual measured values of a 
material property are combined with the overall statistical character of the complete data set 
(the data ensemble) and with the spatial correlation patterns exhibited by the data to produce 
replicate exhaustive models (center part of Figure 6.3-1) showing the distribution of that 
material property in space. Each replicate model reproduces the measured data at the data 
locations, and the overall variability of all the values in the model reproduces the histogram of 
the measurements. Additionally, the spatial correlation structure of the model, evaluated as a 
whole, approximates the spatial correlation among the input measurements. Completing the 
Monte Carlo methodology, each of these individual realizations (property models) may then be 
processed through some relevant transfer function or consequence analysis (for example, a 
radionuclide-transport computer code), and the likelihood of various acceptable versus 
unacceptable performance responses evaluated. This final step is represented in the lower 
portion of Figure 6.3-1. 

NOTE: Conceptual representation of a Monte Carlo process incorporating geostatistical simulation techniques as the 
basis for assessing the impact of geologic uncertainty on a performance measure relevant to licensing of a 
geologic repository. A consequence analysis is any postsimulation mechanism for computing a measure of 
performance across the suite of replicate stochastic simulations (from Rautman and McKenna, 1997 [DIRS 
100643], Figure 6) 
Figure 6.3-1. Conceptual Representation of a Monte Carlo Process 

6.3.2 Methodology Overview 
Construction of the 3-D rock properties models has been guided by the philosophically separate 
but related concepts of heterogeneity and uncertainty described in the preceding section. The 
models make maximum use of deterministic genetic processes to constrain the modeling of 
measured property values away from the physical locations of those measurements. The effort to 
capture genetic processes as they relate to material properties has led to the separation of the 
geologic column of interest into several discrete geologic units, each of which is internally more 
homogeneous in some identifiable manner than subdivisions based on other criteria. 
An interpretation that the material properties of the rocks are controlled principally by the 
original genetic geologic processes and that much of the postdepositional alteration that has 
produced second-order variability in properties occurred before tectonic tilting and faulting 
indicates that the influence of such deformation should be discounted in the modeling process. A 
concept has been adopted of using a stratigraphic coordinate system during the modeling 
process, as distinct from a real-world coordinate system that describes the present-day location of 
points within the several geologic units. Both the measured data and the geostatistical models 
are described in stratigraphic coordinates throughout the modeling exercise. Models are 
back-transformed into real-world coordinates upon completion. 
Because measurements of most material properties of direct interest for use in performance 
calculations are quite limited in number and spatial distribution, the concept of 
porosity-as-a-surrogate is adopted in order to use relatively abundant and widely distributed 
(in three dimensions) porosity data as a first approximation of the geologic heterogeneity of the 
site. The conceptual assumption (Section 5) is that the spatial variation and trends in many 
different properties should be roughly the same, as the entire ensemble of material properties is 
related to the geologic processes responsible for formation of these rocks. 
Sequential gaussian simulation techniques are used to generate the discretized, exhaustive, 
primary porosity models, conditioned to both laboratory core and downhole petrophysical 
measurements in an effort to integrate virtually all available data from the entire Yucca Mountain 
site. Conditional simulation methods produce models that effectively reproduce both the 
conditioning measurements and the overall (geo)statistical character of those measurements. A 
technique known as linear coregionalization is then used to produce similarly exhaustive models 
of selected derivative material properties. For certain material properties, additional 
postprocessing of these derivative models is required to induce desired geologic attributes in the 
final models. For example, an indicator-kriged model of hydrous-phase mineral alteration is 
used to constrain the derivative models of saturated hydraulic conductivity for two units where 
the presence of this type of alteration is known. 
The modeling process has been documented using a scientific notebook procedure 
(SNL QAIP 20-2, Rev. 02 [DIRS 109474]). Details of the modeling process, including 
expansion of topics discussed only in abbreviated or illustrative form in this report, may be found 
in the scientific notebooks listed in Table 6.3-1. 

Table 6.3-1. Scientific Notebooks for Rock Properties Models 
Scientific Notebooks Title Reference 
SNL-SCI-006 Scientific Notebook, Geostatistical Modeling of Rautman and McKenna 1998 
Porosity and Derivative Properties (FY 1998) [DIRS 107442] 
SNL-SCI-011 Scientific Notebook, Geostatistical Modeling of Rautman 1999 [DIRS 109035] 
Porosity and Derivative Properties (FY 1999) 
6.4 DEVELOPMENT OF THE MODELS 
6.4.1 Model Domain 
The location of the 3-D rock properties model is shown in relationship to the general Yucca 
Mountain area and to the Exploratory Studies Facility (ESF) in Figure 1-1. The modeled region 
extends from west to east from the vicinity of Fatigue Wash to the middle of Midway Valley, 
and from north to south from central Yucca Wash to the middle portion of Dune Wash. The 
model domain for the rock properties model is closely tied to the location of the YMP UZ 
site-scale model numerical process-model grid. 
6.4.2 Separate Modeling of Distinctive, Aggregate Geologic Units 
Rock properties models have been created for four distinctly different aggregate geologic units: 
the upper Paintbrush nonwelded interval, the welded portion of the Topopah Spring Tuff, the 
Calico Hills nonwelded interval, and the Prow Pass Tuff. The relationship of these four 
aggregate modeling units to the detailed lithostratigraphic subdivision of Yucca Mountain and to 
other modeling units involved in the GFM and the mineralogical model of Yucca Mountain is 
shown in Table 6.1-1. 
Note that the aggregate geologic units selected for separate rock properties modeling efforts do 
not coincide in general with the breaks between genetic packages of rock, which at Yucca 
Mountain are typically collections of virtually coeval pyroclastic flow deposits associated with a 
major volcanic event such as a caldera-collapse sequence. However, as documented by Rautman 
and McKenna (1997 [DIRS 100643], pp. 6 to 11, Figures 3 and 4), the available measurements 
of material properties indicate that the modeling units defined here are more homogeneous 
(consistent) internally than are the major genetic packages. 
6.4.3 Available Data and Preliminary Processing 
The data used in modeling the spatial variability of material properties in the rock properties 
model were obtained from a number of sources, as described in Section 4, including both 
laboratory measurements of core samples (Sections 4.1.1 and 4.1.3) and down-hole petrophysical 
measurements (Sections 4.1.2 and 4.1.5) of in situ rocks. Only surface-based drill holes have 
been used in constructing the rock properties model, as the large areal extent of the model and 
the requirement that the vertical positions of all data be expressed in stratigraphic coordinates 
effectively precludes the use of samples from the underground workings of the ESF. The 
locations of the various drill holes used in modeling each separate geologic unit are shown in 
Figures 6.4-1 through 6.4-4. Note that although there is major consistency of the drill hole 

coverage from unit to unit, the suite of holes that contain data relevant to any particular model 
unit is unique. Data tracking numbers associated with all data values are tabulated in Section 4. 
6.4.3.1 Data Compilation, Resampling, and Generation of Derived Quantities 
Compilation of porosity, secondary property data, and mineralogical analyses into initial 
working files, even though from a large number of sources, is relatively straightforward. 
Laboratory core data were used essentially as-is. Relevant petrophysical variables were 
extracted from much more massive source files, containing all extant downhole geophysical data, 
using software routine BUD (see Table 3-2). Core and petrophysical porosity data were 
compiled on an individual drill hole basis in spreadsheets using the graphics package Sigma Plot 
(Section 3.2), prior to being combined on a by-unit basis in Excel. 
Because the petrophysical data were recorded in the field on 0.5-ft depth spacings (some older 
holes were recorded on only 1 ft spacings), it was determined that direct use of all available 
petrophysical values would statistically overwhelm the available core porosity measurements 
(collected on a nominal 3 ft spacing) available for an equivalent length of drill hole. Therefore, 
to maintain approximate parity of statistical mass across the two different types of porosity data, 
the petrophysical values were resampled to a 3 ft depth spacing as well. In keeping with the 
practice of Rautman and McKenna (1997 [DIRS 100643], pp. 29 to 30, Figure 14) in developing 
RPM2, the resampling algorithm also calculated a simple average of the adjoining measurements 
within plus and minus 1 ft of the nominal 3 ft depth value. This averaging process is necessary 
to deal with missing values present in the original petrophysical data (generally related to 
unacceptable borehole conditions). The averaging process is standard practice for smoothing or 
aliasing the petrophysical data. Both resampling and computation of the desired two-foot 
average values was accomplished using software routine TWOFOOT (see Table 3-2). 
Relevant mineralogical analyses (hydrous-mineral phase altered mineral fractions of smectite, 
clinoptilolite, mordenite, and chabazite) were extracted from the source data files (Section 4.1.4) 
using the data manipulation capabilities of Excel. A new variable, total hydrous-phase mineral 
content, was created as the sum of these individual mineral fractions. A hydrous-mineral phase 
alteration-indicator variable, I, was also constructed for the mineralogical data set by the logical 
statement: 
I
=
...
1
0
.
.
.
(Minerals) Altered 
=
percent -5 
(Eq. 6.4-1) 
Otherwise 

Source: Rautman, C. and McKenna, S. 1998 [DIRS 107442] Section 2. 
NOTE: Some holes may include only partial penetrations of the unit. 
Figure 6.4-1. Drill Holes Used in Modeling the PTn Model Unit 

Source: Rautman, C. and McKenna, S. 1998 [DIRS 107442] Section 2. 
NOTE: Some holes may include only partial penetrations of the unit. 
Figure 6.4-2. Drill Holes Used in Modeling the TSw Model Unit 

Source: Rautman, C. and McKenna, S. 1998 [DIRS 107442] Section 2. 
NOTE: Some holes may include only partial penetrations of the unit. 
Figure 6.4-3. Drill Holes Used in Modeling the CHn Model Unit 

Source: Rautman, C. and McKenna, S. 1998 [DIRS 107442] Section 2. 
NOTE: Some holes may include only partial penetrations of the unit. 
Figure 6.4-4. Drill Holes Used in Modeling the Tcp Model Unit 

In preparation for calibrating the soft indicators of hydrous-phase mineral alteration 
(Section 6.2.4), additional variables were created for both the core and petrophysical data sets. 
These variables, referred to as bound-water, were calculated simply as the arithmetic 
difference between the OD and RH porosity values (for core; Section 4.1.1) or between the 
POROTOT and POREF porosity values (for petrophysics; Section 4.1.5). Further manipulation 
and use of the bound-water variables is discussed below in Section 6.4.7.1. 
6.4.3.2 Stratigraphic Coordinate Conversion 
Each major lithologic interval selected for modeling has been modeled in a stratigraphic 
coordinate system that reflects the original, prefaulting depositional continuity of these ash-flow 
and air-fall tuffaceous deposits (Figure 6.4-5). Stratigraphic coordinates use the same east-west 
and north-south coordinates (Nevada state plane coordinate system, defined in feet) as the drill 
hole from which the relevant data were obtained. However, the vertical coordinate of a sample is 
represented as the relative fractional position of that sample within the thickness of the entire 
unit at that horizontal location. The stratigraphic coordinate concept effectively removes the 
effect both of depositional thinning away from the source volcanic vent(s) and of 
postdepositional tilting and deformation, and it thus positions samples from equivalent portions 
of the overall unit at the same nominal internal position within a rectangular volume. 
As shown schematically in part (a) of Figure 6.4-5, regions of varying material properties are 
presumed to have been emplaced or otherwise formed by various alteration processes in an 
essentially stratiform manner. At Yucca Mountain, the volumetrically dominant rocks were 
formed by deposition by pyroclastic flows to form thick ash-flow sheets that thin laterally away 
from their source. Thus, there is a tendency for regions of similar material properties that were 
formed under similar pressure-temperature conditions to occupy roughly the same relative 
vertical position within a unit. Later faulting as part of Basin and Range tectonism disrupted the 
originally continuous volcanic rocks and tilted the rock units, with their contained material 
properties, toward the east, as indicated in part (b) of the figure. Modeling of those rock 
properties is illustrated in part (c) of Figure 6.4-5. The vertical locations of drill hole samples are 
specified within the stratigraphic coordinate system as a fractional distance where the base of the 
unit is assigned a distance of zero and the top of the unit is assigned a distance of one. 
Stratigraphic coordinates are thus dimensionless. 
As suggested by the mesh of intersecting dotted lines in the right-hand portion of Figure 6.4 -5, a 
regular rectangular modeling grid is defined within each stratigraphic coordinate system. 
Because the various material property zones have been stretched or compressed vertically so that 
the overall stratigraphic thickness of the unit is constant, defining the modeling grid within this 
framework generally positions nodes with similar materials on a stratigraphically horizontal 
plane. This repositioning of similar materials in similar relative locations greatly simplifies the 
search for data in the neighborhood of an unsampled location, as shown conceptually by the 
search ellipse in part (c) of Figure 6.4-5. Although it is possible to rotate the principal direction 
of the search ellipse to match the overall tectonic dip of the unit (see part [b] of the figure), it is 
virtually impossible to modify the search strategy to account for vertical displacement of the 
material property zones by discrete faults. 

Source: For Illustration Purposes Only 
NOTE: (a) Rock unit is formed by areally extensive volcanic (or sedimentary) processes. Zones of differing rock 
properties (shaded colors) are formed in a stratiform manner. (b) Tectonic deformation tilts and disrupts 
original stratiform continuity by faulting. (c) Modeling unit is returned to an approximation of original 
continuity in a rectangular coordinate system in which all vertical distances are measured as a fractional 
position measured from the top or bottom of the rock unit. 
Figure 6.4-5. Conceptual Illustration of the Construction and Use of Stratigraphic Coordinates 
At the end of a modeling exercise, the transformation process between parts (b) and (c) in 
Figure 6.4-5 is reversed by assigning each grid node a computed vertical position derived from 
knowledge of the structure contour model for the top of each unit and the spatially varying 
thickness of each unit (software routine coordinates, Table 3-2). These values are obtained from 
the independently developed 3-D GFM (e.g., GFM 3.0 [DIRS 109050]). 
In practice, implementation of the stratigraphic-coordinate concept is slightly more complicated 
than the idealized example of Figure 6.4-5. First, sample locations are typically specified in 
terms of their depth within a specific drill hole (the drilling process measures all locations from 
the collar of the hole regardless of the physical elevation of the hole and its contained samples). 
Thus, the measured depths were converted to stratigraphic depths initially, and only to 
stratigraphic elevations at the time of modeling. Second, for reasons involving principally 
numerical precision within the computer programs that implement the actual rock properties 
modeling algorithm(s), the fractional stratigraphic positions indicated in Figure 6.4-5 are 
multiplied by unit-specific scaling constant to obtain values that approximate the nominal 
thickness of the different units in the real world. Additionally, unlike the 2-D example shown in 
Figure 6.4-5, actual modeling of rock properties was conducted in full 3-D space. 

Finally, the issue arises regarding how to treat samples from a drill hole that fails to penetrate the 
entire thickness of the geologic unit in question (represented by the drill hole on the left-hand 
side of Figure 6.4-5). Clearly it is inappropriate to assign a stratigraphic elevation of zero to a 
sample obtained from the very bottom of the hole itself, as the materials at this elevation in 
general are not representative of materials at the base of the unit here or elsewhere. Yet, without 
drilling deeper, the distance between the foot of the hole and the true base of the unit is 
unknown. Such situations have been reconciled by inferring the base of the unit in question from 
the GFM (e.g., GFM 3.0 [DIRS 109050]) and adjusting the fractional position accordingly. The 
presumption is that the base of the unit projected using the framework model is a reasonable 
approximation of the unknown true position at that location. This is a reasonable modeling 
assumption given that the GFM provides a 3-D representation of the geology surrounding the 
geologic repository (BSC 2004, [DIRS 170029] Section 1). Similar reconstructions are required 
in the case where erosion has removed the top of a particular modeling unit at a drill hole 
location or if part of a unit is missing from a drill hole because of faulting. 
The equation for calculating stratigraphic depth (StratDepth) is: 
StratDepth =h SampleDept - UnitTop . cknessNominalThi , (Eq. 6.4-2) 
UnitBottom - UnitTop 
where SampleDepth is the depth of the relevant sample measurement in a given drill hole, 
UnitTop and UnitBottom are the measured or projected depths to the top and bottom contacts of 
that model unit at the drill hole location used to determine the thickness of the unit, and 
NominalThickness is the appropriate unit-specific scaling constant from Table 6.4-1. Conversion 
of drill hole depths to stratigraphic depths was accomplished using the transform capabilities of 
SIGMA PLOT (STRATC4.XFM; Table 6.4-1; Appendix B). Stratigraphic elevation is calculated 
simply as NominalThicknessStratDepth, and was calculated using the mathematical capabilities 
of EXCEL prior to writing final ASCII text files of the data values. 
Table 6.4-1. Unit-Specific Scaling Constants for Nominal Model Unit Thickness 
Model Unit Nominal Thickness 
PTn 200 
TSw 1000 
CHn 400 
Tcp 400 
Source: SN9910T0501399.001 [DIRS 129717]. 

6.4.4 Statistical and Spatial Description of Porosity 
Porosity is the primary material property used to generate the material properties models 
contained in RPM3. Even the models of derivative properties such as hydraulic conductivity are 
based ultimately on porosity. This section presents the statistical description (the ensemble 
characteristics; Sections 6.3.1.3 and 6.3.1.4) of the underlying porosity measurements that have 
been used to condition the stochastic models of each modeling unit. This presentation consists 
principally of histograms for the various model units and a summary of their descriptive statistics 
(Table 6.4-2). Experimental variograms calculated using the measured values and a fitted model 
for each unit are presented to represent the spatial continuity patterns expressed in the data. 
Finally, because of the unique geologic nature of the TSw model unit, two different types of 
porosity (matrix and lithophysal) are presented and justified. 
Table 6.4-2. Statistical Summary of Porosity Data Used in Modeling 
PTn 
TSwCHn Tcp Matrix Lithophysal 
Mean 0.342 0.122 0.146 0.254 0.229 
Std.Dev. 0.122 0.061 0.076 0.075 0.090 
Minimum 0.001 0.001 0.001 0.001 0.001 
Maximum 0.750 0.519 0.551 0.511 0.494 
N 2,300 11,052 11,796 2711 2603 
Source: SN0004T0501399.003 [DIRS 155045], folder 3dm_input. 
NOTE: All values are porosity as a fraction (equivalent to m3/m3), except number of values (N). 
6.4.4.1 PTn Model Unit 
Porosity DataPorosity data obtained from the upper Paintbrush nonwelded model unit are 
portrayed in histogram format in Figure 6.4-6. A statistical summary of these data is given in 
Table 6.4-2. 
Source: SN9910T0501399.001 [DIRS 129717]. 
Figure 6.4-6. Histogram and Cumulative Distribution Function of Matrix Porosity in the PTn Model Unit 

Spatial Continuity DataModeled variograms for porosity in the PTn model unit are presented 
in Figure 6.4-7. Parameters of the fitted variogram model are presented in Table 6.4 -3. Details 
of the variogram modeling exercise for the PTn model unit may be found in the scientific 
notebook (Rautman and McKenna 1998 [DIRS 107442], pp. 361 to 368). 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) stratigraphic vertical and (b) stratigraphic horizontal directions. 
Figure 6.4-7. Experimental and Fitted Model Variograms of Matrix Porosity in the PTn Model Unit 
Table 6.4-3. Variogram Parameters for Spatial Continuity Model of Porosity in the PTn Model Unit 
Range (ft) 
Rotation Angle 
(degrees)1 
Anisotropy 
Ratio1 
Maximum Inter-Minimum 
Nest No. Model Type (horizontal) mediate (vertical) Sill 1 2 3 1 2 
Nugget 0.01 
1 Spherical 300 50 20 0.20 135 135 135 0.167 0.067 
2 Spherical 5,000 2,500 80 0.55 135 135 135 0.500 0.016 
3 Spherical 10,000 7,500 100 0.24 135 135 135 0.750 0.010 
Source: SN9910T0501399.001 [DIRS 129717], and Rautman, C. and McKenna, S. 1998 [DIRS 107442], Section 5. 
NOTE: Rotation angles 13 and anisotropy ratios 12 are keyed to input requirements of the SGSIM computer 
code, as documented in Deutsch and Journel 1992 [DIRS 100567], pp. 22 to 29 and 167. 
6.4.4.2 TSw Model Unit 
Porosity DataPorosity values obtained from the Topopah Spring welded (TSw) model unit are 
presented graphically in histogram format in Figure 6.4-8; the corresponding statistical summary 
of these data is presented in Table 6.4-2. Examination of the raw porosity data indicates that 
there are two different porosity values of interest in modeling rock material properties: matrix 
and lithophysal. Lithophysal porosity, as that term is used in this report, is taken to mean the 
porosity of volumes of rock many tens of centimeters in diameter, such that the porosity effect of 
large (decimeter scale and larger) lithophysal cavities is included. In contrast, the term matrix 

porosity is used in this report to refer to the porosity approximately equivalent to that measured 
for laboratory core samples, in which the size of the matrix pores is small enough that water is 
held in them by capillarity under slightly unsaturated (negative pressure) conditions. The 
difference between the two types of porosity measurements is not trivial, as illustrated in 
Figure 6.4-9, a comparative down-hole plot of matrix and lithophysal porosity data from drill 
hole SD-7. Lithophysal porosity is indicated by the dark solid curve, whereas the matrix 
porosity values measured for core samples are indicated by the filled-circle symbols. Note the 
marked divergence of the porosity values indicated by these two sets of data in two vertical 
locations within the drill hole. In general, these zones of divergence correspond to the 
two lithophysal zones (upper and lower) defined by Buesch et al. (1996 [DIRS 100106]). 
However, the correspondence is not at all exact, and the petrophysical POREF (= lithophysal) 
porosity curve from downhole geophysics indicates that substantial lithophysal cavity 
development may extend significantly above and below the limits of the formally named 
lithophysal zones (Figure 6.4-9). A scatterplot of these lithophysal porosity values versus the 
depth-equivalent core relative humidity porosity for the formally named lithophysal-zone 
intervals only is presented in Figure 6.4-10, part (a). The region of marked divergence from the 
45 one-to-one correspondence line represents high lithophysal porosities matched on a 
nearest-sample basis to the lower core porosity values. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) matrix porosity; (b) lithophysal porosity in the TSw model unit. 
Figure 6.4-8. Histograms and Cumulative Distribution Functions 

Source: SN9910T0501399.001 [DIRS 129717] 
NOTE: Horizontal dashed lines indicate lithostratigraphic units as defined by Buesch et al. (1996 [DIRS 100106]) 
Figure 6.4-9. Comparison of Different Types of Porosity Data for Drill Hole USW SD-7 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) petrophysical POREF porosity vs. core relative humidity porosity; (b) petrophysical VWC vs. core relative 
humidity porosity for formally defined lithophysal intervals in drill hole SD-7. Line indicates one-to-one 
correspondence 
Figure 6.4-10. Crossplots of Core Porosity 
Note also the third set of data plotted in Figure 6.4-9; this curve is identified as volumetric water 
content (equivalent to the amount of water-filled porosity) and is shown by the dashed line 
without symbols. This third curve is observed essentially to overlie the true matrix (= core) 
porosity data throughout much of the drill hole. In locations where the water-filled porosity trace 
does not closely match the core values, it is always observed to indicate markedly lower values 
than the solid lithophysal porosity curve. The near-equivalence of core and VWC values is 
demonstrated in part (b) of Figure 6.4-10. The small set of mismatched pairs may be identified 
in Figure 6.4-9 as representing the interval between approximately 480 and 550 feet. 
Because many drill holes lack core data from which to obtain matrix porosity for modeling 
purposes, the practice has been adopted (for those noncored holes only) of using volumetric 
water content as a surrogate for matrix porosity. Outside these lithophysae-bearing intervals, 
matrix porosity is set equal to POREF porosity, whereas within these intervals of curve 
separation, matrix porosity is set equal to the VWC values. 
It is clear that this practice is merely a simple heuristic device, and that use of the volumetric 
water content as representing water-filled porosity unquestionably will underestimate the actual 
matrix porosity within the UZ for the simple reason that all the available matrix pore space in the 
UZ is not water filled (any large lithophysal cavities also will not be water filled). However, the 
approach is a reasonable approximation for several reasons. (1) Water saturation throughout 
much of the Topopah Spring Tuff is rather high, typically greater than about 80 percent. (2) The 
VWC values in zones of significant lithophysal cavity development are much closer to the true 
matrix porosity than are the lithophysal porosity values, which can be observed from 
Figures 6.4-9 and 6.4-10 to be as much as double the matrix (core) porosity values for drill hole 
USW SD-7. And finally, (3) because one of the purposes of modeling the spatial heterogeneity 
of porosity is to model the variability of saturated hydraulic conductivity using porosity as a 
surrogate, the volumetric water content representing the water-filled porosity (at any in situ 

saturation) most likely represents essentially all the pore space that is available for the 
transmission of water under unsaturated conditions. 
Note that it is possible to significantly underestimate the whole-rock saturated hydraulic 
conductivity of the Topopah Spring welded interval under true saturated conditions in this 
manner, because the effect of lithophysal cavities on the whole-rock permeability is neglected. 
However, such underestimation is likely to be a nonissue, as this relatively high stratigraphic 
interval almost invariably is present in the UZ throughout the entire modeled region. 
Additionally, under true saturated conditions, most ground-water flow through the TSw model 
unit would be through fractures (and lithophysae), and not through the matrix. 
Spatial Continuity DataModeled variograms for both matrix and lithophysal porosity in the 
TSw model unit are presented in Figure 6.4-11. Parameters for both fitted variogram models are 
presented in Table 6.4-4. Details of the variogram modeling exercise may be found in the 
scientific notebook (Rautman and McKenna 1998 [DIRS 100643], pp. 437 to 444 and 
459 to 464). 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) matrix porosity, stratigraphic vertical and (b) stratigraphic horizontal. (c) Lithophysal porosity, stratigraphic 
vertical and (d) stratigraphic horizontal. 
Figure 6.4-11. Experimental and Fitted Model Variograms for Porosity in the TSw Model Unit 

Table 6.4-4. Variogram Parameters for Spatial Continuity Model of Porosity in the TSw Model Unit 
Range (ft) 
Rotation Angle 
(degrees)1 
Anisotropy 
Ratio1 
Maximum Inter-Minimum 
Nest No. Model Type (horizontal) mediate (vertical) Sill 1 2 3 1 2 
TSw Model Unit, Matrix Porosity 
Nugget 0.01 
1 Spherical 250 250 20 0.19 0 0 0 1.000 0.080 
2 Spherical 2,000 6,500 90 0.25 0 0 0 3.250 0.045 
3 Spherical 29,000 12,500 350 0.55 0 0 0 0.431 0.012 
TSW Model Unit, Lithophysal Porosity 
Nugget 0.01 
1 Spherical 600 400 15 0.19 0 0 0 0.667 0.025 
2 Spherical 5,000 8,000 175 0.20 0 0 0 1.600 0.035 
3 Spherical 38,000 17,000 200 0.60 0 0 0 0.447 0.006 
Source: SN9910T0501399.001 [DIRS 129717] and Rautman, C. and McKenna, S. 1998 [DIRS 107442], Section 6. 
NOTE: Rotation angles 13 and anisotropy ratios 12 are keyed to input requirements of the SGSIM computer 
code, as documented in Deutsch and Journel 1992 [100567], pp. 22 to 29 and 167. 
6.4.4.3 CHn Model Unit 
Porosity DataPorosity values obtained from the Calico Hills nonwelded (CHn) model unit are 
presented graphically in Figure 6.4-12; the corresponding statistical summary of these data is 
presented in Table 6.4-2. 
Source: SN9910T0501399.001 [DIRS 129717]. 
Figure 6.4-12. Histogram and Cumulative Distribution Function of Matrix Porosity in the CHn Model Unit 

Spatial Continuity DataA modeled variogram for matrix porosity in the CHn model unit is 
presented in Figure 6.4-13. Parameters of the fitted variogram model are presented in 
Table 6.4-5. Details of the variogram modeling exercise may be found in the scientific notebook 
(Rautman and McKenna 1998 [DIRS 107442], pp. 534 to 556). 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) stratigraphic vertical; (b) stratigraphic horizontal. 
Figure 6.4-13. Experimental and Fitted Model Variograms for Matrix Porosity in the CHn Model Unit 
Table 6.4-5. Variogram Parameters for Spatial Continuity Model of Porosity in the CHn Model Unit 
Range (ft) 
Rotation Angle 
(degrees)1 
Anisotropy 
Ratio1 
Maximum Inter-Minimum 
Nest No. Model Type (horizontal) mediate (vertical) Sill 1 2 3 1 2 
Nugget 0.05 
1 Spherical 3,000 3,000 30 0.25 1.000 0.010 
2 Spherical 12,000 5,000 400 0.70 0.417 0.033 
Source: SN9910T0501399.001 [DIRS 129717] and Rautman, C. and McKenna, S. 1998 [DIRS 107442], Section 7. 
NOTE: Rotation angles 13 and anisotropy ratios 12 are keyed to input requirements of the SGSIM computer 
code, as documented in Deutsch and Journel 1992 [DIRS 100567], pp. 22 to 29 and 167. 
6.4.4.4 Tcp Model Unit 
Porosity DataPorosity values obtained from the Prow Pass Tuff (Tcp) model unit are presented 
graphically in histogram format in Figure 6.4-14 the corresponding statistical summary of these 
data is presented in Table 6.4-2. 

Source: SN9910T0501399.001 [DIRS 129717]. 
Figure 6.4-14. Histogram and Cumulative Distribution Function of Matrix Porosity in the Tcp Model Unit 
Spatial Continuity DataA modeled variogram for matrix porosity in the Tcp model unit is 
presented in Figure 6.4-15. Parameters of the fitted variogram model for porosity in the Tcp 
model unit are presented in Table 6.4-6. Details of the variogram modeling exercise may be 
found in the scientific notebook (Rautman and McKenna 1998, [DIRS 107442] pp. 704 to 726). 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) stratigraphic vertical; (b) stratigraphic horizontal. 
Figure 6.4-15. Experimental and Fitted Model Variograms for Matrix Porosity in the Tcp Model Unit 

Table 6.4-6 Variogram Parameters for Spatial Continuity Model of Porosity in the Tcp Model Unit 
Range (ft) 
Rotation Angle 
(degrees) 
Anisotropy 
Ratio 
Maximum Inter-Minimum 
Nest No. Model Type (horizontal) mediate (vertical) Sill 1 2 3 1 2 
Nugget 0.05 
1 Spherical 6,000 4,000 80 0.40 0.667 0.013 
2 Spherical 15,000 10,000 800 0.55 0.667 0.053 
Source: SN9910T0501399.001 [DIRS 129717] and Rautman, C. and McKenna, S. 1998 [DIRS 107442], Section 8. 
NOTE: Rotation angles 13 and anisotropy ratios 12 are keyed to input requirements of the SGSIM computer 
code, as documented in Deutsch and Journel 1992 [DIRS 100567], pp. 22 to 29 and 167. 
6.4.5 Statistical Description of Secondary Material Properties 
The secondary, or derivative, material properties considered in this analysis are matrix saturated 
hydraulic conductivity, bulk density, and thermal conductivity (Section 4.1.3). In general, there 
are insufficient individual measurements of these material properties to determine their spatial 
continuity patterns independently of porosity. Accordingly, they have been modeled under the 
presumption that each variable is coregionalized with porosity. This section contains the basic 
statistical description of each derivative material property, including the correlation of that 
property with corresponding porosity measurements, which is used in the coregionalization 
process. 
6.4.5.1 Matrix Saturated Hydraulic Conductivity 
The histogram of all available laboratory measurements of matrix saturated hydraulic 
conductivity is presented in Figure 6.4-16(a). A scatterplot of these same data against the 
corresponding laboratory porosity values is shown in part (b) of the figure, and a statistical 
summary of the data is presented in Table 6.4-7. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) Histogram of laboratory-measured matrix saturated hydraulic conductivity from core samples; and (b) 
crossplot of hydraulic conductivity versus matrix porosity for the same samples. 
Figure 6.4-16. All Measured Core Samples 

Table 6.4-7. Statistical Summary of Matrix Saturated Hydraulic Conductivity Measurements 
All Data Unaltered Altered 
Mean -8.9553 -8.6536 -9.6805 
Std.Dev. 1.7317 1.7248 1.5269 
Minimum -11.7086 -11.3360 -11.7086 
Maximum -4.6866 -4.6955 -4.6866 
N 405 286 119 
Source: SN9910T0501399.001 [DIRS 129717], folder 3dm_input. 
NOTE: All values in log10 m/sec except number of samples (N); no-flow samples omitted. 
Figure 6.4-16(a) is distinctively multi-modal, and Figure 6.4-16(b) indicates four subpopulations 
that can be identified in the matrix hydraulic conductivity data set. Essentially, cluster A 
consists of samples for which the delta-porosity, computed simply as the arithmetic difference 
between the OD and relative humidity laboratory-measured porosity values, is less than 5 percent 
(0.05). Cluster A corresponds to a continuum of hydraulic conductivity values that are rather 
strongly correlated to porosity, as presented separately in Figure 6.4-17. Rautman and McKenna 
(1997 [DIRS 100643], pp. 37 to 40) present an expanded discussion of identifying hydrous-
phase mineral alteration in core samples through the use of delta-porosity values. 
. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Conductivity of nonflowing samples set to 10-14 m/sec for plotting only. (a) Histogram of laboratory-measured 
matrix saturated hydraulic conductivity from unaltered core samples (identified by delta-porosity); and (b) 
crossplot of hydraulic conductivity versus matrix porosity for the same samples. Conductivity of nonflowing 
samples set to 10-14 m/sec for plotting only 
Figure 6.4-17. Unaltered Core Samples 

. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) Histogram of laboratory-measured matrix saturated hydraulic conductivity from altered core samples 
(identified by delta-porosity) and (b) crossplot of hydraulic conductivity vs. matrix porosity for the same 
samples. Conductivity of nonflowing samples set to 10-14 m/sec for plotting only 
Figure 6.4-18. Altered Core Samples 
Cluster B in Figure 6.4-16(b) contains samples for which the delta-porosity between the OD and 
relative humidity porosity measurements is greater than 5 percent. These are samples that 
exhibit significant hydrous-phase mineral alteration, as evidenced by the removal of moderately 
large quantities of water during drying at 105C. This subpopulation is plotted separately in 
Figure 6.4-18, and it appears largely indistinguishable from a normally distributed (gaussian) 
population if the small group of samples with conductivities greater than 106 m/sec are 
discounted as potentially misclassified samples. These samples may be misclassified because 
hydrous-phase mineral alteration causes development of the secondary mineral phases 
thatoccluded the matrix flow pathways. Therefore if affected by hydrous phase mineral 
alteration these samples are unlikely to have hydraulic conductivity values greater than 10-6 
m/sec. 
Cluster C of Figure 6.4-16(b) is represents a small subset of the entire population of matrix 
hydraulic conductivity measurements for which the corresponding porosity measurement is very 
low, and for which there appears to be no particular correlation of porosity with conductivity. 
These samples are almost exclusively from two vitrophyric lithostratigraphic units: Tptrv1 and 
Tptpv3, Figure 6.4-19(a) and (c). Experimentation indicates that almost the identical subset of 
samples can be extracted from the complete hydraulic conductivity data set simply as those 
samples whose measured porosity is less than 5 percent (0.05). These samples are presented in 
Figure 6.4-19(b) and (d). Flint (1998 [DIRS 100033], p. 38) interpreted the relatively 
uniform-random distribution of these matrix hydraulic conductivities as caused by 
microfracturing within these densely welded rock types. 

. 
Source: SN9910T0501399.001 [DIRS 129717] 
NOTE: (a) core samples from vitrophyric lithostratigraphic units Tptrv1 and Tptpv3 and (b) core samples with matrix 
porosity less than 0.05. (c) and (d) histograms and cumulative distribution functions for samples presented 
in parts (a) and (b), respectively; no-flow samples omitted 
Figure 6.4-19. Scatterplots of Matrix Saturated Hydraulic Conductivity Versus Matrix Porosity 
Cluster D of Figure 6.4-16(b) represents those core samples for which the matrix saturated 
hydraulic conductivity was below the sensitivity limit of the laboratory permeameter. Note that 
these null values have been ignored in all statistical calculations for this modeling exercise. As 
shown in Figure 6.4-16, the saturated hydraulic conductivity of these no-flow samples have 
been arbitrarily set equal to 1014 m/sec for plotting purposes, both here and elsewhere in this 
report. 
In contrast to the discussion of Figures 6.4-17 and 6.4-18, which involves more or less global 
characteristics of the measured matrix saturated hydraulic conductivity measurements from 
Yucca Mountain, a broader objective of this modeling exercise is to reproduce the material 
property characteristics appropriate for individual model units. The ability to discriminate and 
identify the hydraulic conductivity characteristics of specific vitrophyric rock units essentially by 

looking solely at their porosity characteristics (e.g., Figure 6.4-17 and discussion) is one 
example. Other relevant matrix hydraulic conductivity characteristics for purposes of this report 
are the by-unit statistical distributions of values. These target histograms for the individually 
simulated models of the four modeling units are presented in Figure 6.4-20 (a) through (d). 
. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) PTn; (b) TSw; (c) CHn; (d) Tcp Model Units. 
Figure 6.4-20. Matrix Saturated Hydraulic Conductivity Histograms and Cumulative Distribution 
Functions 
6.4.5.2 Bulk Density 
A histogram of more than 3,000 relative-humidity-oven-dried bulk density values from all four 
model units is presented in Figure 6.4-21(a). The composite histogram is distinctly multimodal, 
as might be expected from aggregation of densely welded tuffs with a collection of non- to 
partially welded ash-flow tuffs and nonwelded air-fall tuffaceous debris. 

. 
Source: 6.4-21(a) SN9910T0501399.001 [DIRS 129717] and 6.4-21(b) SN0004T0501399.002 [DIRS 155046]. 
NOTE: (a) histogram and cumulative distribution function of Relative-Humidity Oven-Dried bulk density values from 
all four modeling units; (b) scatterplot of bulk density as a function of matrix porosity for the same data. 
Figure 6.4-21. Bulk Density Relationship 
Despite the compositing of a group of diverse lithologies (and mineralogies) for 
Figure 6.4-21(a), the bulk density data appear to belong to a relatively consistent family of 
values. A scatter diagram showing the relationship between the bulk density values and relative 
humidity dried porosity (equivalent to relative humidityoven-dried porosity) is presented in 
Figure 6.4-21(b). Although there is some segregation of the cloud of data points into two 
overlapping subpopulations (which can be demonstrated to relate to hydrous-phase mineral 
alteration), the coefficient of determination (r2) value is a very strong 0.912, indicating only 
slightly less than one-to-one correspondence. Given the generally low importance of bulk 
density in most performance assessment analyses, it has not been determined to be of sufficient 
importance to analyze altered and unaltered bulk densities separately. Unit-specific histograms 
that serve as the target distributions for the coregionalized bulk density models are presented in 
Figure 6.4-22 Model unit genesis is reflected clearly in the four parts of the figure: high-density 
densely welded tuffs in the TSw model unit (part (b)), low-density nonwelded and pumiceous 
rich materials in the PTn unit (part (a)), and intermediate-density nonwelded to only partially 
welded tuffaceous materials in the CHn and Tcp model units (parts (c) and (d)). 

. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) the PTn, (b) TSw, (c) CHn, and (d) Tcp Model Units. 
Figure 6.4-22. Bulk Density Histograms 
6.4.5.3 Thermal Conductivity 
Thermal conductivity and porosity data have been obtained from a modest number of samples 
collected from the PTn and TSw model units. Figure 6.4-23(a) presents a histogram and 
cumulative distribution function for these thermal conductivity values measured at 70C and 
under 105C-dried saturation conditions (note that thermal conductivity is a function of both 
absolute temperature and saturation state). The crossplot of these same values in 
Figure 6.4-23(b) clearly indicates an inverse relationship of thermal conductivity with porosity. 
A statistical summary of these available thermal conductivity data is presented in Table 6.4-8. 
There are two significant difficulties in using the existing thermal conductivity data, which is 
derived from laboratory testing on matrix-sized specimens. First, the in situ bulk density of the 
Topopah Spring welded unit is most directly related to the lithophysal porosity of this unit, not to 
the matrix porosity. Open cavities too large to be measured as part of the matrix porosity 
laboratory procedure (or its petrophysical equivalent) will significantly reduce the whole rock 

bulk density, leading to lower effective thermal conductivity. Previous discussion of porosity 
data from the TSw model unit (Section 6.4.4.2) indicates that the lithophysal porosity values can 
be greater by a factor of two compared with the depth-equivalent matrix porosity values. Use of 
this factor-of-two porosity difference applied to the regression relationship shown in 
Figure 6.4-23(b) would lead to overprediction errors of roughly 30 to 50 percent for thermal 
conductivity, depending on the actual porosity level considered. Given that the thermal 
conductivity models will be coregionalized using lithophysal porosity in an effort to model the 
in situ thermal conductivity of the rock mass, the phrase whole-rock thermal conductivity will be 
used in this report. 
. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: All thermal conductivities measured at 70C and 105C-dried conditions. Solid line in (b) is regression fit 
(used in Section 6.4.5.3); dashed lines are 95-percent confidence limits. (a) histogram and cumulative 
distribution function for measured thermal conductivity data. (b) scatterplot of thermal conductivity as a 
function of matrix porosity. 
Figure 6.4-23. Thermal Conductivities from Non-Zeolitic Rock Samples 
Table 6.4-8. Statistical Summary of Measured Thermal Conductivity Data from Non-Zeolitic Rock 
Samples 
Thermal Conductivity (W/m-K) at 
70C and 105C dry 
Porosity (105C)All Data TSw Only 
Mean 1.054 1.241 0.197 
Std.Dev. 0.516 0.427 0.156 
Minimum 0.160 0.620 0.040 
Maximum 2.200 2.200 0.610 
N 52 35 54 
Source: SN9910T0501399.001 [DIRS 129717], folder 3dm_input. 
NOTE: All values in Watts per meter-Kelvin (W/m-k) except N = number of 
samples. 
MDL-NBS-GS-000004 REV 01 6-37 September 2004 

A second, and rather severe difficulty with the available thermal conductivity data is that the 
density of sampling is not great (a maximum of 52 samples; see Table 6.4-8), and furthermore, 
those samples are highly biased both spatially and toward low-porosity materials, as 
follows: (1) Two of four drill holes that were sampled for thermal conductivity specimens, 
although located within the extended site area, are actually located some distance from the region 
transected by the main part of the ESF (NRG-4, NRG-5; see Figure 5). (2) The sampling 
vertically within a drill hole is not systematic, and in fact, the vertical distribution of samples 
cannot be considered particularly representative of the entire Topopah Spring welded model 
unit. (3) The only samples that represent the higher porosity values (needed to model the effect 
of lithophysal cavities on heat conduction) are taken from the PTn model unit, and thus represent 
nonwelded tuffs rather than the lithophysal portion of welded materials. 
With respect to the several biases known to exist in the thermal conductivity data, consider 
Figure 6.4-24 (a), which is a histogram of the 54 porosity values measured on the thermal 
conductivity test specimens (Table 6.4-8). Comparison of this figure with the histogram of all 
lithophysal porosity values measured from the TSw model unit (part (b) of the figure) clearly 
indicates the extent of the sampling bias. First, Figure 6.4-24(a) is clearly bimodal, representing 
as it does, samples from both welded and nonwelded rock types. If the group of samples with 
porosities higher than about 40 percent (presumably representing nonwelded tuffs) is discounted, 
the mode corresponding to the welded Topopah Spring samples is strongly skewed toward lower 
porosity values. For example, the approximate modal value in Figure 6.4-24(a) is 8 to 
10 percent, whereas the modal value for the TSw model unit as a whole (Figure 6.4-24(b)) is 
18 to 20 percent for lithophysal porosity. 
. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) total porosity measured for thermal conductivity test specimens; (b) lithophysal porosity from the TSw 
model unit (repeated from Figure 6.4-23[b]). 
Figure 6.4-24. Histograms and Cumulative Distribution Functions 
These sampling biases have been reduced in the following manner. First, the regression 
relationship presented in the scatterplot of Figure 6.4-23(b) is assumed to be a valid predictor of 
thermal conductivity across the range of porosity values appropriate for the TSw model unit. A 

systematic prediction of the nominal thermal conductivity of the TSw model unit is then 
generated using the systematically sampled (nominal 3 ft spacing) porosity data available for 
three drill holes located along the ESF main drift (SD-7, SD-9, and SD-12; Figure 6.4-26). 
These three sets of predicted values are then aggregated and the appropriate statistical quantities 
and histogram are computed (Figure 6.4-25; Table 6.4-9). The histogram of Figure 6.4-25 will 
be used as the target distribution for the coregionalized models. Note that although the mean 
thermal conductivity for both the 35 measured samples of the Topopah Spring welded unit and 
the systematically predicted thermal conductivity of the TSw model unit as a whole (Table 6.4-9) 
are remarkably similar at 1.2 W/m-K (rounded), the local thermal conductivity of major intervals 
within the unit differ markedly from one another (Figure 6.4-26). 
. 
Source: SN9910T0501399.001 [DIRS 129717]. 
Figure 6.4-25. Histogram and Cumulative Distribution Function for Thermal Conductivity Systematically 
Predicted from Lithophysal Porosity Values in Drill Holes USW SD-7, SD-9, and SD-12 
Using the Regression Relationship from Figure 6.4-23(b) 
Table 6.4-9. Statistical Comparison of Measured and Predicted Thermal Conductivity Data for the TSw 
Model Unit 
Measured Thermal 
Conductivity @ 70C 
Predicted Thermal 
Conductivity @ 70C 
Mean 1.241 1.183 
Std.Dev. 0.4271 0.1822 
Minimum 0.620 0.676 
Maximum 2.200 1.550 
N 35 6063 
Source: SN9910T0501399.001 [DIRS 129717], folder 3dm_input. 
NOTES: Units are Watts/meter-Kelvin (W/m-K) except for number of values (N). 
1 Also includes effect of measurement errors and lithologic variability. 
2 
Includes effect of lithologic variability only. 
(see text for discussion of prediction methodology) 
MDL-NBS-GS-000004 REV 01 6-39 September 2004 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Thermal conductivity key: predicted thermal conductivity at 70C and 105C-dried conditions. Porosity key: 
dark linelithophysal porosity from petrophysical logs; light grey line with symbolsmatrix porosity from core 
samples. (a) USW SD-7, (b) SD-9; (c) SD-12. 
Figure 6.4-26. Downhole Variation in Predicted Thermal Conductivity Values Based on Systematically 
Measured Lithophysal Porosity Data for Drill Holes 
6.4.6 Modeling Techniques 
This section contains a summarized, yet, logically accurate description of the geostatistical 
modeling techniques used to produce the rock properties models, including reference to the 
various computer codes (Table 3-1) and software routines (Table 3-2) used to generate the actual 
models. Sequential gaussian simulation is used to generate the primary porosity models, 
whereas linear coregionalization is used to generate the derivative models of secondary 
properties. Indicator kriging is used to produce the model of hydrous-phase mineral alteration 
that constrains the distribution of the derivative models of saturated hydraulic conductivity in 
two of the model units. 
6.4.6.1 Discretization of the Model Domain 
Details of the grid defined for all geostatistical modeling are presented in Table 6.4-10 The 
horizontal discretization of 200 m by 200 m is tied directly to the horizontal resolution of the UZ 

site-scale flow model across its entire areal extent. Vertical resolution of the geostatistical grid is 
also tied to the UZ zone flow model, particularly within the general repository volume. 
Additionally, the total number of grid nodes in the resulting suite of models was computationally 
tractable. 
Table 6.4-10. Geostatistical Modeling Grid Specification Parameters 
Corresponding to the Southwest Corner of the Grid 
Grid Dimension 
Midpoint 
(ft/m) 
Spacing 
(ft/m) 
No. of 
Nodes 
Total 
Nodes 
Model X 549,895.6 656.2 34 
(Easting) 167,600.0 200.0 
Model Y 751,349.0 656.2 43 
(Northing) 229,000.0 200.0 
Model Z PTn 3.3333 6.667 30 43,860 
(Stratigraphic 
Vertical) 
1.0159 2.032 
TSw 8.2025 
2.5000 
16.405 
5.000 
61 89,182 
CHn 8.4444 
2.5399 
16.666 
5.080 
24 35,088 
Tcp 8.4444 
2.5399 
16.666 
5.080 
24 35,088 
Source: Rautman (1999 [DIRS 109035]) Section 2, page 16. 
6.4.6.2 Sequential Gaussian Simulation of Porosity 
Geostatistical simulation comprises a large class of modeling techniques that can produce very 
complex, and presumably therefore highly realistic numerical representations of spatially 
variable properties. Simulation may be thought of as expanding the actual information 
available in a stochastic manner that is also compatible with additional information derived from 
the data ensemble and the spatial context of those data. The process builds on the geologic 
intuition that unsampled locations nearby a known value will tend to resemble that value, 
whereas unsampled locations at increasing distances from a known measurement tend 
progressively to resemble that datum less and less. This intuition will be observed statistically 
across a suite of several equiprobable simulations. 
The philosophical framework of simulation is simple. Using concepts of random variables, one 
develops a model of the probability density function (pdf) for a material property of interest at all 
locations in space. By transforming the measured data to their respective positions on the 
probability density function and using simple kriging (Deutsch and Journel 1992 [DIRS 100567], 
pp. 62 and 137), the desired pdfs can be made conditional to a set of measured values. 
Alternative realizations are simply generated by sampling from these pdfs. The variance of 
individual, location-specific, pdfs will vary with the amount of geologic uncertainty. Near 
conditioning data (Figure 6.4-27), the pdf associated with an unsampled location will be 
relatively narrow. Where less information is known, such as away from data or in the vicinity of 
conflicting measurements, the pdf will be relatively broad (Figure 6.4-27[a-b]), leading to 
generation of a wide range of likely values across a suite of realizations. Because the underlying 
kriging algorithm used to derive the pdfs is an exact interpolator, the pdf degenerates to a spike 
with probability = 1 at a measured location (Figure 6.4-27(d)). 

Source: For Illustrative Purposes Only. 
NOTE: (a) beyond the range of spatial correlation: pdf is virtually identical to the univariate histogram; essentially all 
that is known about the unsampled location is what is known about the population as a whole. (b) far away 
from a sample, but within the range of spatial correlation: pdf is broad, indicating considerable uncertainty; 
distribution begins to focus on expected value. (c) nearby a sample value: pdf is narrower indicating less 
uncertainty. (d) immediately adjacent to a sample value: pdf is nearly a spike value corresponding to the 
adjacent sample datum. 
Figure 6.4-27. Conceptual Probability Density Functions Representing the Uncertainty Associated With 
Various Unsampled Locations 
Simulations may be conditional or unconditional. Conditional simulations are numerically 
anchored to a specific set of real-world data, and they exhibit three properties that adds to their 
usefulness in evaluating the effects of geologic uncertainty on physical process models. 
Specifically, conditional simulations: 
1. 
Reproduce the known data values at the same locations within the model as 
represented by the real-world samples 
2. 
Reproduce the full range of measurement variability, as represented by histogram and 
univariate descriptive statistics of the known data values 
3. 
Reproduce the bivariate statistics, or two-point spatial correlation structure, of the 
input data. 
Unconditional simulations are similar, except that they are not conditioned to any particular 
spatially anchored data, and thus item 1 does not apply. As simulations with these three 
characteristics cannot be distinguished statistically from the ensemble of data used in their 
construction nor from each other, they serve as alternative, equally likely stochastic realizations 
of an incompletely sampled and measured reality shown conceptually in Figure 6.3-1. 
Simulations may be developed using parametric or nonparametric techniques for mechanically 
inducing the desired univariate (item 2 above) and bivariate (item 3) statistical properties. 
Parametric techniques rely upon the predictive power of well-understood multivariate probability 
functions, almost invariably the multivariate gaussian. A number of algorithms have been 
developed that implement gaussian-related simulation (e.g., references in Deutsch and 
Journel 1992 [DIRS 100567]). 

The sequential gaussian simulation program SGSIM (Table 3-1; see also Deutsch and 
Journel 1992 [DIRS 100567], pp. 123 to 125 and 164 to 167) was used to generate 50 replicate 
models of porosity at all unsampled locations for each of the four model units, conditioned to the 
observed porosity data from the several drill holes. The sequential modeling process is relatively 
straightforward and is implemented as follows: 
1. 
All data values are converted to positions on a univariate standard-normal (m=0, s2=1) 
distribution using the graphical normal-score transform (Figure 6.4-28), implemented 
in program NSCORE (Table 3-1; see also Deutsch and Journel 1992 [DIRS 100567], 
pp. 138 and 209 to 211). This transformation does nothing to the spatial correlation 
structure because the relative positions of all values with respect to each other are 
preserved (i.e., the transform is quantile-preserving). 
2. 
The spatial correlation structure is identified using the normal-score transformed 
values and modeled using standard variography. 
3. 
The transformed measured data are mapped into the model volume; samples located 
(only fortuitously) at a node in the stratigraphic-coordinate grid are assigned to that 
node and the node is not simulated. 
4. 
A sequential random path is defined that will visit each unsampled node once and only 
once. 
5. 
At each node along this path, a search is conducted for nearby data and any previously 
simulated grid nodes. The search parameters (anisotropic radii; number of data to use) 
are user specified. 
6. 
The (user-specified) N closest data are identified and weighted by their geological 
distance (in contrast to simply their Euclidean distance), as defined in the 
stratigraphic coordinate system according to the mathematical formulation of the 
spatial continuity model (variogram). Because the normal-score transformed values 
are effectively relative positions on a cumulative distribution function, the resultant 
value is also effectively a relative position on the same cumulative distribution 
function. 
7. 
A value (in normal-score space) is drawn at random from the conditional probability 
distribution defined in step 6 and this value is assigned to represent the porosity at that 
point. The simulation process then moves to the next unsampled location along the 
random path defined in step 4 and the process is repeated beginning with step 5. 
8. 
After all originally unsampled grid nodes have been simulated using the logic of steps 
5 through 7, the resulting spatial array of normal-score values are back-transformed to 
the original porosity space using the inverse of the normal score transform of step 1 
(software routine BACKTR, Table 3-2), and the simulation process is complete. 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: A population with virtually any univariate distribution (a) can be transformed to any other univariate 
distribution (b) (here standard gaussian) in a manner represented by the arrows such that the quantile 
relationships among the data are preserved. The reverse transformation is also possible in the same 
manner. 
Figure 6.4-28. Graphical Representation of the Quantile-Preserving Normal-Score Transform Process 
Using Cumulative Distribution Functions 
Because porosity values are drawn at random for each unsampled grid node, the values obtained 
in different simulation runs will be different. Indeed, the weighting scheme used to develop the 
conditional expectation in each independent simulation will be different as well in that the same 
path through the 3-D grid is not used in successive simulations. Additionally, because the 
data-search process considers previously simulated grid nodes as well as measured data 
(non-varying), the nearby values used to estimate the conditional expectation will also vary 
among simulation runs. At grid locations that are well constrained by consistently measured 
data, the variability of the simulated values across a suite of simulations will be small, as 
described by the spatial continuity model. However, at grid locations far from any conditioning 
measured data, or at grid nodes that are in the vicinity of conflicting measurements, the spread of 
porosity values that will be generated by the simulation algorithm across different computer runs 
will be quite broad, approaching the univariate variance of the data when considered without 
regard for spatial position. Uncertainty, measured by variability across the suite of simulations is 
small where much is known about the rock mass and progressively greater at longer distances 
from actual sampled values. 
6.4.6.3 Indicator Kriging Using Uncertain Data 
The indicator methodology is most easily understood through the following definition of an 
indicator transformation of a spatially distributed variable, Z: 
=
...
1
0
.
.
Z
=
Z
* 
()
I 
x 
(Eq. 6.4-3) 
() 
Otherwise
x
where I(x) is the indicator-transformed variable at spatial location x, Z(x) is the original variable 
also at location x, and Z* is a particular threshold value of interest. In other words, the original 
MDL-NBS-GS-000004 REV 01 6-44 September 2004 

variable is replaced by the value 1 at all locations where it is less than or equal to the threshold 
and by the value 0 at all locations where it is greater. 
All of this presumes that there is precise knowledge of the original variable of interest, Z(x). 
Should knowledge of Z be less than 100 percent certain, however, it is possible to restate 
Equation 6.4-3 to account for the soft probability, p, that Z less than or equal to Z*. Thus: 
Z
=
Z *
=
...
p
.
()
I 
x 
(Eq. 6.4-4) 
() 
1
-
p
.
Otherwise
x
Note that it is perfectly legitimate to mix hard and soft indicators in the same analysis, and thus 
the methodology is suitable for combining the relatively definitive XRD indicators of 
hydrous-phase mineral alteration with the less certain but spatially more abundant petrophysical 
indicators (Sections 4.1.4 and 4.1.5, respectively). It remains to calibrate the appropriate 
value(s) of p, given the petrophysical estimates of bound-water content; this calibration effort is 
described below in Section 6.4.7.1. 
Implementation of indicator kriging process (Program IK3D, Table 3-1; see also Deutsch and 
Journel 1998 [DIRS 102895], pp. 103 to 106) is straightforward enough in concept. 
1. 
Hard XRD measurements of hydrous-phase mineral content are converted to a set of 
ones and zeros with respect to some threshold concentration judged relevant. 
2. 
Soft petrophysical measurements of hydrous-phase mineral content are converted to a 
set of values ranging between zero and one in accordance with the calibration exercise 
(using program, BICALIB, Deutsch and Journel 1998 [DIRS 102895], pp. 235 to 236). 
3. 
The spatial continuity structure of the composite hard and soft data set is determined 
through standard variography. 
4. 
The transformed measured data are mapped into the model coordinate system. 
5. 
At each desired grid location a search is conducted for nearby indicator data, I(x) 
(either hard or soft). 
6. 
The nearest N values identified by the search are then weighted according to their 
relative geologic distances, as computed from the variogram model. The weighted 
average is assigned as the appropriate indicator value at that location, and the 
estimation process moves to the next grid node. Steps 5 and 6 are iterated until the 
grid is complete. 
7. 
All grid nodes with a modeled probability of p less than 0.5 may be considered 
unaltered, and that those with p greater than 0.5 may be deemed altered. 

Note that implicit in Equations 6.4-3 and 6.4-4 is probabilistic interpretation that p is the 
likelihood of Z(x) being less than the threshold, Z*. If one is dealing with a binary classification 
in which the rock present at location x is either altered versus unaltered, it is a logical conclusion 
that: 
6.4.6.4 Coregionalization Modeling of Derivative Properties 
There are two frequently used methods that have been used to incorporate cross-variable 
correlations of material properties into rock properties models in the presence of undersampling 
of one variable. First, one may assume a coefficient of determination (r2) equal to one and 
simply apply the empirically determined regression equation to predict the secondary 
(undersampled) variable. A second method is to model a randomly distributed error (noise) 
about the regression line. However, neither of these two alternatives is particularly satisfying. In 
many instances, r2 = 1.0 implies a substantially stronger relationship than exists in fact. In the 
second case, a cross-plot of the two resulting variables reproduces the desired cross-variable 
relationship, but any spatial correlation exhibited in nature by the secondary variable is 
effectively destroyed by the addition of spatially uncorrelated noise. Cokriging and cosimulation 
(Journel and Huijbregts 1978 [DIRS 102898], pp. 324 to 326; Deutsch and Journel 1992 
[DIRS 100567], pp. 121 to 123) are well-established algorithms for producing models that 
reproduce both the observed correlation between two variables and the observed spatial 
correlation structure of each. However, with the degree of undersampling of the secondary 
variable, such as is the case at Yucca Mountain, there are insufficient data from which to infer 
the necessary auto-correlation and cross-correlation relationships. 
A practical alternative mechanism to cokriging or cosimulation in the presence of undersampling 
involves the assumption of a linear model of coregionalization (Journel and Huijbregts 1978, 
pp.171 to 175 and 516 to 517; Luster 1985 [DIRS 102904]; Altman et al. 1996 [DIRS 100591], 
pp. 54 to 59), which effectively states that the spatial continuity of the secondary variable, and of 
the joint (cross-variable) correlation structure, is presumed to be approximately identical to that 
of the primary variable. Although the mathematical relationships involved in the derivation are 
tedious (e.g. Rautman and McKenna 1997 [DIRS 100643], pp. 63 to 66, and references therein), 
it can be shown that it is possible to generate a pair of cross-correlated simulations as a weighted 
linear combination of two independent standard-normal spatially correlated simulations where 
the weighting factor is effectively the cross-variable correlation coefficient, r. This translates to 
in practical terms, is to take a conditional porosity simulation in standard-normal form, such as is 
generated in step 7 of Section 6.4.6.2, and combine it (software routine COREGPC, Table 3-2) 
with an identically generated but independent unconditional simulation (these are paired in this 
activity by run number), also in standard-normal form, using r and as the appropriate weighting 
coefficients. The resulting linear combination is also essentially in standard normal form, and it 
can be back-transformed to match the desired distribution of the target secondary variable 
(software routine TRANS, Table 3-2). If appropriate, the back transformed coregionalized 
property simulation is post-processed further to impart additional relevant characteristics as 
described below in Section 6.4.8. 

6.4.7 Modeling of Hydrous-Phase Mineral Alteration 
Volcanic glass within the lower two modeling units (CHn and Tcp) has been variably altered to 
(dominantly) zeolite minerals throughout a major portion of the model area. These altered rocks 
exhibit markedly reduced saturated hydraulic conductivity by comparison with unaltered 
materials of approximately the same porosity. The rock properties modeling effort included not 
only the XRD mineralogic data (described in Section 4.1.4), which provide virtually 100-percent 
certain identification of hydrous-phase mineral alteration, but also less accurate but more 
abundant and widely distributed petrophysical indicators of such alteration (Section 4.1.5). 
6.4.7.1 Calibrating Soft Indicators of Hydrous-Phase Mineral Alteration 
Because the petrophysical data available provide a less than 100 percent certain identification of 
hydrous-phase alteration, it is necessary to calibrate these values to account for the added uncertainty. 
The calibration effort involved 334 samples from the CHn and Tcp model units for which 
depth-matched pairs (generated using software routine MATCHUP, Table 3-1) of both XRD mineral 
analyses and petrophysical bound-water contents could be obtained. Petrophysical bound-water 
content was calculated simply as the difference between the POROTOT and POREF log traces (see 
also Section 4.1.5). Core bound-water content was calculated as the difference between the OD and 
relative humidity porosity values (Section 4.1.1), and is initially identical to the delta-porosity value 
described in Section 6.4.5.1. Comparison of depth-matched core and petrophysical bound-water data 
(software routine MATCH, Table 3-1) indicate that the laboratory core measurement process gave a 
bound-water content of approximately twice that indicated by the down-hole petrophysical 
measurements; see Figure 6.4-29. Although a precise explanation for the discrepant measurements is 
uncertain, the empirical relationship can be used to adjust the core measurements to provide a common 
basis with the petrophysical values. Adjusted values for core bound water content have been used in 
the calibration work that follows. 
00121DC_AMR_117.cdr 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Regression line (solid, with 95-percent confidence limits, dotted) has been forced through the origin and has 
a slope of 1.74; unconstrained regression has r2 = 0.67 (r-squared for a constrained line is not meaningful) 
Figure 6.4-29. Scatterplot of Core Versus Petrophysically Derived Bound-Water Content for 354 
Depth-Matched Pairs of Samples 

A scatterplot shown in Figure 6.4-30 represents the total hydrous-phase mineral content vs. 
adjusted bound-water content. In general, an increase in adjusted bound-water content 
corresponds directly to an increase in the total hydrous minerals. The calibration consists of 
cross-tabulating (software routine BICALIB, Table 3-2) the number of pairs in each of the 
categories: 
 
Hydrous-phase mineral content: 
- Greater than 5 percent 
- Less than or equal to 5 percent. 
 
Adjusted bound-water content: 
- Less than or equal to 0.03 
- Greater than 0.03 to less than 0.04 
- Greater than 0.04 to less than 0.05 
- Greater than 0.05. 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Interior lines correspond to the various threshold values used in Table 6.4-19. 
Figure 6.4-30. Scatterplot of Total Hydrous-Phase Mineral Content Versus Adjusted Bound-Water 
Content for 334 Depth-Matched Paired Samples 
The cross-tabulated counts of soft-value pairs were converted to a decimal proportion (software 
routine BICALIB), and these values were taken as the prior probability of obtaining the specified 
total hydrous-phase mineral content given a specified adjusted bound-water content. These soft, 
prior-probability values are presented in Table 6.4-19. In contrast, XRD hydrous-phase mineral 
contents were coded as hard probability values of zero or one. 

Table 6.4-11. Prior Probability of Altered vs. Unaltered Rocks as a Function of Bound-Water Content 
Hydrous-Phase Mineral Content 
Bound-Water Content, Cumulative Prior Probability 
< 0.03 0.03  0.04 0.04  0.05 > 0.05 
< 5 percent 0.2474 0.6122 .7778 0.8625 
> 5 percent 0.7526 0.3878 0.2222 0.1375 
Source: SN9910T0501399.001 [DIRS 129717], Rautman, C. and McKenna, S. 1998 [DIRS 107442], 
Section 9, and Rautman, C. 1999 [DIRS 109035], Section 9. 
6.4.7.2 Indicator Kriging of Hydrous-Phase Mineral Alteration 
Both hard and soft indicators of hydrous-phase mineral alteration were combined and supplied as 
input to an indicator kriging exercise (Program 1K3D, Deutsch and Journel 1998 
[DIRS 102895], pp. 103 to 106). Indicator kriging (Section 6.4.6.3) is a variant of ordinary 
kriging, in which the variable of interest is estimated as a weighted linear combination (average) 
of the available observed values within some local neighborhood of influence. In common with 
ordinary kriging, the weights applied to the observed values are calculated in accordance with 
the spatial continuity model developed from the combined indicator data set. Necessarily, 
alteration in the CHn and Tcp model units was modeled separately, even though the two units 
were combined for purposes of estimating the prior probability values discussed above. 
CHn Model UnitThe variogram model for hydrous-phase mineral alteration indicators in the 
CHn model unit is presented in Figure 6.4-31, and the parameters of the fitted variogram model 
are in Table 6.4-12. Details of the variogram modeling exercise may be found in the scientific 
notebook (Rautman and McKenna 1998 [DIRS 100643], pp. 663 to 695). 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: (a) stratigraphically vertical; (b) stratigraphically horizontal directions. 
Figure 6.4-31. Indicator Variogram and Fitted Model Computed for Alteration in the CHn Model Units 

Table 6.4-12. Variogram Parameters for Spatial Continuity Model, Alteration in the CHn and Tcp Model 
Units 
Rotation 
Nest Model 
Range (ft) 
Angle 
(degrees) 
Anisotropy 
Ratio 
Maximum Inter-Minimum 
No. Type (horizontal) mediate (vertical) Sill 1 2 3 1 2 
CHn Model Unit 
Nugget 0.005 
1 Spherical 4000 1500 30 0.010 0 0 0 0.3750 0.0075 
2 Spherical 7000 4000 500 0.035 0 0 0 0.5714 0.0714 
Tcp Model Unit 
Nugget 0.010 
1 Spherical 2500 2500 150 0.025 0 0 0 1 0.060 
2 Spherical 15000 15000 150 0.058 0 0 0 1 0.010 
Source: SN9910T0501399.001 [DIRS 129717] and Rautman, C. and McKenna S. 1998 [DIRS 107442], Section 9, 
page 827 to 831. 
. 
Source: SN9910T0501399.001 [DIRS 129717.] 
NOTE: (a) stratigraphically vertical; (b) both stratigraphically horizontal directions (isotropic). 
Figure 6.4-32. Indicator Variogram and Fitted Model Computed for Alteration in the Tcp Model Unit 
Tcp Model UnitThe variogram model for hydrous-phase mineral alteration indicators in the 
Tcp model unit is presented in Figure 6.4-32. The parameters of the fitted variogram model are 
given in Table 6.4-12. Details of the variogram modeling exercise may be found in the scientific 
notebook (Rautman and McKenna 1998 [DIRS 107442], pp. 772 to 786). 
6.4.8 Postprocessing of Simulated Models 
6.4.8.1 Incorporation of Specific Attributes into Simulated Models 
The actual rocks at Yucca Mountain are the composite result of numerous geologic processes 
that overlap in both space and time. Consequently, rock properties modeling involves more than 

the simple generation of a set of porosity values. This is particularly true for the models of 
derivative material properties that have been generated by coregionalization with porosity. This 
section presents the techniques used to incorporate two specific types of secondary geologic 
attributes into raw simulated property models. 
VitrophyresThe widely variable hydraulic conductivity values associates with densely welded 
vitrophyric core samples of uniformly low porosity from the Topopah Spring Tuff 
(lithostratigraphic units Tptrv1 and Tptpv3) have been described by Flint (1998 [DIRS 100033], 
p. 38) as resulting from microfractures present within these glassy, brittle rocks. Additional 
consideration of these samples (Flint 1998 [DIRS 100033], Figure 12) suggests that vitrophyric 
samples may independently be identified by their uniformly very low porosity (less than 
approximately 0.05; see Section 6.4.5.1, Figure 6.4-19). Accordingly, the simulated models of 
saturated hydraulic conductivity for the TSw model unit (only) were postprocessed (software 
routine VITROPHYRE, Table 3-2) such that if the corresponding porosity value was less than 
0.05 (simulations paired by run number), the coregionalized hydraulic conductivity value was 
discarded. Under the assumption that such low-porosity grid nodes represent vitrophyre or other 
essentially nonporous brittle materials, a value of Ks was generated by random sampling from a 
uniform population with a range of 10-14 to 10-6 m/sec as derived from the histogram of 
Figure 6.4-19. A conceptual representation of the logic underlying the modeling of vitrophyric 
rocks is presented in Figure 6.4-33. 
Source: For illustration purposes only. 
NOTE: see text for discussion. 
Figure 6.4-33. Logic Diagram for Postprocessing Porosity and Hydraulic Conductivity Simulations to 
Recognize Vitrophyre Rock Type 

Hydrous-Phase Mineral AlterationHydrous-phase mineral alteration is inferred to represent a 
secondary alteration process that affected vitric tuffaceous materials at some particular time after 
formation of the original rock mass, although generally before tectonic faulting and tilting. 
Consequently, there appears to be little or no direct correlation of saturated hydraulic 
conductivity with matrix porosity for altered (zeolitized) samples, see Section 6.4.5.1, 
Figure 6.4-18). Recall also from Figure 6.4-18 that the histogram of altered hydraulic 
conductivity values appears virtually indistinguishable from a gaussian population, given the 
relatively small sample size. 
This modeling philosophy has been implemented for the rock properties model by 
postprocessing (software routine ZEOLITE5, Table 3-2) the initial coregionalized hydraulic 
conductivity models (for both the CHn and Tcp model units) grid node by grid node together 
with a corresponding indicator kriging model (Section 6.4.7.2) indicating the probability of 
significant hydrous-phase mineral alteration. If the grid node under consideration was 
considered unaltered (palt < 0.50), the coregionalized Ks value was retained, and the processing 
moved to the next grid node. If the node was considered altered (palt > 0.50), then the 
coregionalized Ks value was discarded in favor of a normally distributed random value sampled 
from a population with the appropriate mean and variance. A schematic diagram of this 
postprocessing procedure is presented in Figure 6.4-34. 
Source: For illustration purposes only. 
NOTE: see text for discussion. 
Figure 6.4-34. Logic Diagram for Postprocessing Porosity and Alteration Indicator Simulations to 
Recognize Hydraulic Conductivity Dependence on Alteration State 

6.4.8.2 Uncertainty Modeling 
Geostatistical generation of the material property models is essentially complete at this point. As 
part of the current modeling exercise, 50 replicate, statistically indistinguishable models of 
porosity for each model unit (one set each for matrix and lithophysal porosity in the TSw model 
unit) and 50 replicate models for each one of the derivative properties (bulk density, matrix 
saturated hydraulic conductivity, andfor the TSw model unitthermal conductivity as well) 
have been generated. Each of the replicate simulations honors the measured porosity data at 
sample locations (subject to the discretization limits), exhibits the full range of variability 
captured by the histogram of the relevant property, exhibits the appropriate range of spatial 
correlation (variogram), and (for the derivative properties) exhibits the appropriate correlation 
coefficient with porosity. In effect, there is nothing objective about any one simulated 
(or coregionalized) model to prefer it over any other model of that suite. Indeed, the only 
meaningful distinguishing feature within a suite of replicate models is the arbitrarily selected 
random number seed, which initiated the simulation process. 
Because there are few, if any, objective differences to distinguish the members of each suite of 
simulated property models, it thus logically follows that the variability among members of a 
suite represents an empirical estimate of the geologic uncertainty associated with each material 
property. Geologic uncertainty is defined in this context as that which results from 
less-than-exhaustive sampling or other measurement. The difficulty arises, however, as how best 
to represent this space of uncertainty in a simple and concise manner. 
An uncertainty model has been generated (software routine ETYPE; Table 3-2) for each material 
property-modeling unit combination by computing the node-by-node standard deviations of each 
set of 50 replicate simulated models. This process produces uncertainty models that are 
themselves spatially heterogeneous. By theory and in practice, variability among 
simulations-and uncertainty as defined by the standard deviationis small in close proximity to 
measured sample values. Variability among simulationsand uncertainty as defined by the 
standard deviationis small in close proximity to measured sample values. Variability among 
simulations and uncertainty is high at great distances from measured data, or in the vicinity of 
conflicting measured values. Note that there is no particular reason other than general reader 
familiarity for selecting the standard deviation as the measure of uncertainty. Values such as the 
total range of the modeled property or the interquartile range could easily be computed during 
this postprocessing step. 
With respect to alternative uncertainty models, it is also important to remember that the best 
measure of geologic uncertainty for the potential nuclear-waste repository at Yucca Mountain is 
the impact of that uncertainty on some relevant measure of repository system performance. 
Potential examples of such global performance measures might be particle-tracking transport 
times or cumulative radionuclide release rates, as suggested by the consequence analysis step 
shown in the conceptual diagram of Figure 6.3-1. Development of these types of comprehensive 
uncertainty assessments is beyond the scope of the rock properties modeling effort. 
6.4.8.3 Expected-Value Modeling 
The postprocessing step used to generate the standard-deviation uncertainty model for each rock 
property-modeling unit combination described in Section 6.4.8.2 is easily adapted to produce 

some type of summary statistical measure associated with the central-tendency of the 
simulation process. In effect, the geostatistical simulation process develops a 3-D array of 
spatially correlated probability density functions, one distribution for each of the discretizing 
grid nodes within a model unit. Continuing with the probability density function analogy, certain 
rock property values are more likely to exist at a given location than other values. 
Note, however, that more likely to exist can be defined in more than one manner. 
A set of summary expected-value models has been generated (software routine ETYPE; 
Table 3-2) for each suite of simulated models by calculating the arithmetic mean of the 
50 replicate simulated values generated at each grid node. Journel (1983 [DIRS 102897], 
pp. 459 to 461; see also Deutsch and Journel 1992 [DIRS 102895], pp. 76 and 225) defined this 
conditional expectation representation of central tendency as the E-type estimate. Note that 
computing the median (M-type estimate, Journel 1983 [DIRS 102897], p. 460) of the 
50 replicate simulated values would be an equally legitimate measure of expectation, although 
one that is more computationally intensive. 
Because of the logistical difficulty of presenting the full simulated results for 50 models times 
19 unique material-property/model-unit combinations, the results of this geostatistical modeling 
exercise described in Section 6.5 that follows are presented in terms of the E-type estimate. It is 
important to remember, however, that the three characteristic properties of simulated models 
described as desirable in the first paragraph of Section 6.4.6.2 no longer necessarily apply to 
these summary models. Compare, for example, the statistical nature of the simulated and 
summary models from ISM2, as discussed by Rautman and McKenna (1997 [DIRS 100643], 
e.g., pp. 99 to 104), and in abbreviated form for ISM3 as presented in the discussion of model 
validation in Section 6.7. 
The first characteristic, that involving reproduction of the measured (porosity) values at the 
locations of actual measurement, is maintained. However, the ensemble of modeled E-type 
values no longer represents the full range of univariate variability of the measurement ensemble 
(the second characteristic). Additionally, the two-point spatial correlation character (variogram) 
of the E-type model no longer reproduces that of the underlying measurements (the third 
characteristic). Specifically, because of averaging across the replicate simulations, the E-type 
model typically grades relatively smoothly and continuously from one (exactly reproduced) 
measured value to the next (in three dimensions). Thus the apparent spatial continuity of the 
E-type model typically is much greater than that observed for the data themselves. This is the 
so-called smoothing effect that is typical of virtually all interpolation (in contrast to simulation) 
algorithms, including kriging, nearest-neighbor estimation, and inverse-distance-to-a-power 
weighting. 
6.5 RESULTS AND DISCUSSION 
The real results of the rock properties modeling exercise consist of the numerical model files 
containing the replicate Monte Carlo simulated models and the associated summary E-type and 
uncertainty model files for each of the four modeling units and each of the modeled properties. 
Because of the impracticality of presenting and discussing each of these simulated and summary 
models individually, the results presented in this section are illustrative and focus on gross 
heterogeneity features as revealed by the summary E-type models. Each model unit and each 

modeled material property within that model unit is discussed in turn. Discussion of the replicate 
individual simulated models is generally restricted to the section on Model Validation. Again, 
because of the impracticality of validating each and every replicate simulation, the discussion 
in this section will be illustrative. 
Because of technical limitations involving plotting the individual drill holes on some model 
views, refer to Figures 6.4-1 though 6.4-4 for information regarding the spatial distribution of 
input measurements. Note that the distribution of available drill hole information is 
unit-specific. Also note that although the color-scale legend on each figure generally represents 
the full range of the modeled property present in the underlying numerical file, it is only 
fortuitous if the highest (or lowest) value is actually shown in the particular model view. 
6.5.1 PTn Model Unit 
Heterogeneity of matrix porosity within the PTn model unit is shown in Figure 6.5-1 in both 
stratigraphic and real-world coordinates. As indicated by the projection arrows connecting the 
two halves of the figure, the vertically exaggerated rectangular volume in stratigraphic 
coordinates is back transformed to real-world coordinates, such that the material property values 
assume their correct relative positions within the tilted and faulted strata of Yucca Mountain, 
shown with no vertical exaggeration. Cross-sectional views of porosity heterogeneity are 
presented in Figure 6.5-2. 
Porosity values within the PTn model unit are generally high, varying principally from about 
30 to more than 60 percent (0.30 to 0.60). Porosity values appear relatively continuous over 
distances of 5,000 to 10,000 feet, as expected from the input range of spatial continuity. Porosity 
trends are prominently anisotropic from northwest to southeast, also as expected from the 
variogram model (top surface of the block diagram of Figure 6.5-1). 
Bulk density (Figure 6.5-3) varies spatially approximately inversely with porosity, as expected 
from the strong (r = 0.912) negative correlation coefficient. Bulk density values vary from less 
than 1.0 g/cm3 to nearly 2.0 g/cm3. Densities are generally low across the modeled area. 
Prominent regions of higher density are associated with drill hole H-6, shown on the 
southernmost east-west cross section, and particularly with drill hole G-2 near the northern 
boundary of the modeled region at the intersection of the north-south and northernmost east-west 
cross sections. At the G-2 locality, density exceeds 1.9 g/cm3 at two horizons, presumably 
corresponding to the Pah Canyon and Yucca Mountain Tuffs, which are described as moderately 
welded in this part of the model area. 
Heterogeneity in matrix saturated hydraulic conductivity values is shown in Figure 6.5-4. 
Hydraulic conductivity values are generally between 105 and 107 m/sec (note that in this and 
following similar figures, hydraulic conductivity values are shown in log10 
units; i.e., 10 7 m/sec = 7.000). Lower conductivities on the order of 108 m/sec are modeled in 
the vicinities of drill hole H-6 (not visible in Figure 6.5-4) and G-2, coincident with the lower 
matrix porosities in these regions from which the hydraulic conductivity values are 
coregionalized. 

Source: SN9910T0501399.001 [DIRS 129717] Stratigraphic Coordinates, and MO9910MWDISMRP.002 
[DIRS 145731] Nevada State Plane Coordinates figure shown for illustration purposes only. 
NOTE: Light-grey objects in real-world-coordinate view are drill holes and workings of the ESF. Easting and 
northing values are Nevada state plane coordinates in feet. 
Figure 6.5-1. Perspective Diagrams Showing E-Type Model Matrix Porosity in the PTn Model Unit in 
Both Stratigraphic and Real-World Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-2. Cross-Sectional Views Showing E-Type Heterogeneity in Matrix Porosity in the PTn Model 
Unit in Stratigraphic Coordinates 
Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-3. Cross-Sectional View Showing E-Type Heterogeneity of Bulk Density in the PTn Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-4. Cross-Sectional Views Showing E-Type Heterogeneity in Matrix Saturated Hydraulic 
Conductivity in the PTn Model Unit in Stratigraphic Coordinates 
6.5.2 TSw Model Unit 
Heterogeneity of material properties within the TSw model unit is presented in Figures 6.5-5 to 
6.5-11. Note that matrix porosity, as indicated in Figure 6.5-5 and 6.5-6, is very low, mostly less 
than 10 to 15 percent, and relatively constant in magnitude across the entire modeled region. 
This minimal variability is consistent with the definition of this unit as densely welded tuff. 
However, the front face of the block diagram, shown in stratigraphic coordinates in Figure 6.5-5, 
and the cross-section views of Figure 6.5-6 indicate that increased matrix porosity is associated 
with the major lithophysae-bearing intervals within the unit, and most particularly with what in 
the GFM would be the vapor-phase corroded crystal-rich nonlithophysal interval near the top of 
the unit. 
The heterogeneity of lithophysal porosity, in contrast to the matrix porosity, is shown in 
Figure 6.5-7 as perspective views, and in Figure 6.5-8 in cross-sectional view. Although even 
lithophysal porosity is generally low compared to the porosity of a nonwelded welded tuff, such 
as the PTn model unit, maximum porosity values within the lithophysal intervals locally exceed 
30 to 35 percent (0.30 to 0.35). Figure 6.5-8 clearly indicates two such intervals of high 
porosity, corresponding approximately to the upper and lower lithostratigraphic units (Tptpul and 
Tptpll), separated by a low-porosity (on the order of 10 percent) interval equivalent to the middle 
nonlithophysal lithostratigraphic unit (Tptpmn). Note however, that there is a fairly large 
amount of lateral heterogeneity within each of the elevated porosity intervals. It is the measured 

porosity data themselves, as propagated away from drill hole locations by the spatial continuity 
model, that produce both the apparent layering and the variations within those layers. There are 
no detailed lithostratigraphic (or other) subunits explicitly modeled within the TSw model unit. 
All property heterogeneity in the rock properties model are functions strictly of the measured 
material properties. 
Bulk density heterogeneity, which is coregionalized from lithophysal porosity, is illustrated in 
Figure 6.5-9. Density values are typically above 2.0 g/cm3 throughout most of the relatively 
lithophysae-free region. Bulk density is particularly high (approaching 2.5 g/cm3) in the lower 
parts of the TSw model unit, as indicated by the red colors on the figure. A prominent high 
density interval is associated with the lower vitrophyre in the central part of the modeled region 
(approximately corresponding to lithostratigraphic unit Tptpv3). However, bulk-rock density 
values associated with the upper lithophysal horizon in particular may be as low as 
1.5 to 1.8 g/ cm3. The alternation of lithophysal and nonlithophysal intervals are particularly 
clearly visualized through the bulk density model. 
Thermal conductivity is also coregionalized from lithophysal porosity, in an effort to predict the 
thermal conductivity of volumes of rock influenced by the presence of lithophysal cavities one 
decimeter or larger in diameter. Figure 6.5-10 presents the E-type model of spatial heterogeneity 
in thermal conductivity. High values of thermal conductivity (greater than approximately 
1.3 
to 1.4 W/m-K and shown in yellow and orange tones) are associated with the lower portion 
of the TSw model unit, and with the presumed low-lithophysae middle nonlithophysal 
lithostratigraphic unit (Tptpmn). Particularly high thermal conductivity values, approaching 
1.5 
W/m-K, are present at the very base of the TSw unit, presumably associated with the densely 
welded vitric lithostratigraphic unit. In contrast, the most lithophysal portions of the unit, which 
contain lithophysal cavities up to a meter in diameter, appear to be characterized by bulk-rock 
thermal conductivities less than 1.0 W/m-K (blue and blue-green colors in Figure 6.5-10). 

Source: SN9910T0501399.001 [DIRS 129717] Stratigraphic Coordinates, and MO9910MWDISMRP.002 
[DIRS 145731] Nevada State Plane Coordinates figure shown for illustration purposes only. 
NOTE: Light-grey objects in real-world-coordinate view are drill holes and workings of the ESF. Easting and northing 
values are Nevada state plane coordinates in feet. Note compressed porosity scale compared with 
Figure 6.5-7. 
Figure 6.5-5. Perspective Diagrams Showing E-Type Model Matrix Porosity in the TSw Model Unit in 
Both Stratigraphic and Real-World Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-6. Cross-Sectional Views Showing E-Type Heterogeneity of Matrix Porosity in the TSw Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717] Stratigraphic Coordinates, and MO9910MWDISMRP.002 
[DIRS 145731] Nevada State Plane Coordinates figure shown for illustration purposes only. 
NOTE: Light-grey objects in real-world-coordinate view are drill holes and workings of the ESF. Easting and 
northing values are Nevada state plane coordinates in feet. Note expanded porosity scale compared with 
Figure 6.5-5. 
Figure 6.5-7. Perspective Diagrams Showing E-Type Model Lithophysal Porosity in the TSw Model Unit 
in Both Stratigraphic and Real-World Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 5x. 
Figure 6.5-8. Cross-Sectional Views Showing E-Type Heterogeneity of Lithophysal Porosity in theTSw 
Model Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 5x. 
Figure 6.5-9. Cross-Sectional Views Showing E-Type Heterogeneity of Bulk Density in the TSw Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 5x. 
Figure 6.5-10. Cross-Sectional Views Showing E-Type Heterogeneity of Thermal Conductivity in the TSw 
Model Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 5x. 
Figure 6.5-11. Cross-Sectional Views Showing E-Type Heterogeneity of Matrix Saturated Hydraulic 
Conductivity in the TSw Model Unit in Stratigraphic Coordinates 
Heterogeneity in matrix saturated hydraulic conductivity is presented in Figure 6.5-11, and has 
been coregionalized from the matrix porosity model shown in Figures 6.5-5 and 6.5-6. Matrix 
conductivities are less than 1011 m/sec through much of the lower part of the model unit. As 
expected from the higher matrix porosity values associated with the lithophysae-bearing portions 
of the TSw model unit, matrix hydraulic conductivity values are markedly higher, 
109 to 1010 m/sec (yellow to green tones), in these vapor-phase altered portions of the unit. 
Some of these higher conductivity values may also be associated with vapor-phase corrosion of 
the welded tuff within the crystal-rich nonlithophysal unit. Note, however, that matrix 
hydraulic conductivity does not include conductivity attributable to flow through lithophysal 
cavities or fractures under true saturated conditions. 
6.5.3 CHn Model Unit 
Variations in the matrix porosity of the CHn model unit are presented in Figures 6.5-12 and 
6.5-13. Porosity values are generally high at 20 to 40 percent (0.20 to 0.40) throughout the unit, 
particularly in contrast to the low porosity values typical of the overlying TSw model unit 

(0.10 to 0.15). Expanding the porosity color scale indicates that a mass of particularly high 
porosity occupies the central portion of the modeled volume. Porosity values locally approach 
50 percent (0.50) within this region. 
Variations in bulk density in the CHn model unit are presented in Figure 6.5-14. Density values 
vary from more than 2.0 g/cm3 to less than 1.3 g/cm3, depending upon location, although the 
majority of this nonwelded unit exhibits limited variation in bulk density at 1.5 to 1.75 g/cm3. 
Generally speaking, bulk density varies inversely with porosity, as anticipated from the 
coregionalization relationship. 
Heterogeneity in matrix saturated hydraulic conductivity for the CHn model unit is illustrated in 
Figure 6.5-15. Although hydraulic conductivity is derived by coregionalization with matrix 
porosity, the relationship between the two material properties is not precisely straightforward 
because of the presence of hydrous-phase mineral alteration (predominantly zeolitic) within the 
unit. Matrix conductivities are typically 106 to 107 m/sec (greens to reds) within the unaltered 
portion of the CHn, and typically less than 1011 m/sec elsewhere (blue). The block diagram in 
the upper part of Figure 6.5-15 clearly indicates that vitric (to potentially devitrified) materials 
are limited to the upper portion of the model unit, and more particularly to the southwestern 
portion of the modeled volume. The overall impression is of a wedge of vitric (unaltered) 
material tapering to a feather edge toward the northeast. Zeolitic (altered) rocks are shown in 
tones of blue underlying and replacing the green- through red-colored volume to the north. 
Recall that the saturated hydraulic conductivity of altered samples are essentially uncorrelated 
with matrix porosity. The lower portion of Figure 6.5-15 presents cross-sectional views of the 
hydraulic conductivity field within the model unit, and illustrates some of the complex 
interfingering relationships of altered and unaltered rock types. 

Source: SN9910T0501399.001 [DIRS 129717] Stratigraphic Coordinates, and MO9910MWDISMRP.002 
[DIRS 145731] Nevada State Plane Coordinates figure shown for illustration purposes only. 
NOTE: Light-grey objects in real-world-coordinate view are drill holes and workings of the ESF. Easting and northing 
values are Nevada state plane coordinates in feet. 
Figure 6.5-12. Perspective Diagrams Showing E-Type Model Matrix Porosity in the CHn Model Unit in 
Both Stratigraphic and Real-World Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-13. Cross-Sectional Views Showing E-Type Heterogeneity of Matrix Porosity in the CHn Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-14. Cross-Sectional Views Showing E-Type Heterogeneity of Bulk Density in the CHn Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-15. Block and Cross-Sectional Views Showing E-Type Heterogeneity of Matrix Saturated
Hydraulic Conductivity in the CHn Model Unit in Stratigraphic Coordinates 

6.5.4 Tcp Model Unit 
Figures 6.5-16 and 6.5-17 present the spatial heterogeneity of matrix porosity within the 
Tcp model unit. Because the Prow Pass Tuff is mostly nonwelded, the porosity values are 
typically high, varying from 20 to nearly 40 percent across large volumes of the model. Lower 
porosity values, typically less than 15 percent (0.15), are present along the northern boundary of 
the modeled volume, and as a poorly defined lobate mass (indicated by the 0.15 porosity 
isoshell), which may correspond to the moderately welded portion of this unit, generally low 
within the central-eastern part of the region. 
Variations in bulk density, shown in Figure 6.5-18, substantiate the variations in porosity 
described in the preceding paragraph. Densities well in excess of 2.1 g/cm3 are prominently 
displayed along the northern boundary of the model in the vicinity of drill hole G-2. Higher 
densities on the order of 2.0 g/cm3 are also visible in the east-central portion of the block, 
corresponding to the low porosity lobe. Elsewhere across the modeled volume, bulk densities 
are more typically between 1.75 and 2.0 g/cm3 and are shown in green colors. 
Heterogeneity in matrix saturated hydraulic conductivity is presented in Figure 6.5-19. In a 
manner similar to the overlying CHn model unit, a bimodal distribution of conductivity values, 
corresponding to altered and unaltered rock types, is quite prominent. Lower hydraulic 
conductivity values, typically less than 1010 m/sec are associated with regions affected by 
hydrous-phase mineral alteration. Markedly higher values of hydraulic conductivity, varying 
from 1010 to 107 m/sec, are associated with the vitric-to-devitrified continuum of matrix 
porosity values in regions unaffected by alteration. The block diagram in the upper portion of 
Figure 6.4-19 indicates that the upper and lower margins of the Prow Pass Tuff are essentially 
completely altered. Reference to the cross-sectional views in the lower half of the figure 
indicates that the unaltered portions of the unit correspond to the devitrified interior core of the 
ash flow. 
6.6 UNCERTAINTIES AND LIMITATIONS 
Uncertainty, in the context of a stochastic modeling analysis, assumes a specific meaning as a 
descriptor of ones state of knowledge, in that a quantitative assessment of uncertainty is 
typically one of the explicit objectives of the analysis. This meaning is in contrast to another 
possible definition, which is expressed more precisely as involving limitation or doubt as to the 
accuracy or relevance of a result. This distinction is maintained in this report. This section first 
describes a number of limitations of both methodology and data that detract from the exactness 
or accuracy of the models generated by this analysis. The results of a stochastic uncertainty 
analysis are then presented, which rigorously quantifies the space of geologic uncertainty that 
results from less-than-completely exhaustive site characterization. 

Source: SN9910T0501399.001 [DIRS 129717] Stratigraphic Coordinates, and MO9910MWDISMRP.002 [DIRS 
145731] Nevada State Plane Coordinates figure shown for illustration purposes only. 
NOTE: Light-grey objects in real-world-coordinate view are drill holes and workings of the ESF. Easting and northing 
values are Nevada state plane coordinates in feet. 
Figure 6.5-16. Perspective Diagrams Showing E-Type Model Matrix Porosity in the Tcp Model Unit in 
Both Stratigraphic and Real-World Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. Note adjusted porosity scale in lower figure. Objects extending away from cross 
sections in lower figure are isoshells enclosing all regions of lowest porosity (less than approximately 0.15). 
Figure 6.5-17. Cross-Sectional Views Showing E-Type Heterogeneity of Matrix Porosity in the Tcp 
Model Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-18. Cross-Sectional Views Showing E-Type Heterogeneity of Bulk Density in the Tcp Model 
Unit in Stratigraphic Coordinates 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Vertical exaggeration 10x. 
Figure 6.5-19. Block Diagram and Cross-Sectional Views Showing E-Type Heterogeneity of Matrix 
Saturated Hydraulic Conductivity in the Tcp Model unit in Stratigraphic Coordinates 

6.6.1 Limitations 
There are a number of factors affecting this analysis that may best be described as limitations of 
the data or of the modeling process itself. These limitations include errors and biases in the 
sample data used in the analysis, the methodological use of porosity as a surrogate for other 
material properties, the combination of numerous lithostratigraphic units into the four major 
modeling units, and the effect of geologic departures from the assumptions inherent in the use of 
the stratigraphic coordinate system. 
6.6.1.1 Errors and Biases in Sample Data 
Stochastic simulation is a statistical-probabilistic methodology, with reproduction of various 
target statistical measures an important part. As such, errors (uncertainties) incorporated into 
the statistical description of the rock mass unquestionably will be propagated through the 
simulation process into the output models. These errors are of two principal types: measurement 
error and systematic bias. 
Measurement error or analytical uncertainty in the input data is not addressed in these rock 
property models. Philosophically, the underlying assumption is that measurement uncertainty, 
such as would be captured by replicate measurements of a given material property on the same 
physical sample, is small compared to the lateral heterogeneity of the actual rock mass. It is also 
assumed that these measurement types of errors are essentially unbiased, such that some may be 
high whereas others may be correspondingly low. Statistically, this assumption may be stated 
that the errors are of mean zero with a small relative variance. 
Systematic biases, on the other hand, represent a more severe challenge for rock properties 
modeling using a statistical-probabilistic approach. Two examples of systematic bias within the 
data sets used for this modeling activity are known, and have been compensated for to some 
extent during creation of the individual simulated models. These biases are discussed in the 
following paragraphs. 
10
Measurement Sensitivity LimitsMeasurement of the matrix saturated hydraulic conductivity 
values for core samples appears to have had a lower detection limit of roughly 
12 to 1011 m/ sec. Samples whose hydraulic conductivity are lower than this lower limit are 
reported in the data set as no flow. Omitting these samples entirely would lead to an 
unrealistically high set of modeled hydraulic conductivity values. On the other hand, 
substituting the no-flow samples with an arbitrary low value prior to the simulation process 
would tend to skew the results towards that arbitrary low conductivity. Therefore, as described 
in Section 6.4.5.1, the effect of these no-flow samples has been simulated explicitly during 
postprocessing by setting the appropriate fraction of values equal to the arbitrary value of 1014 
m/sec at randomly selected grid nodes within each model unit. This fix to approximate the 
nonnegligible number of nonflowing laboratory samples assumes that there is no particular 
spatial correlation among these samples. 
Preferential Sampling BiasAnother, more pronounced example of preferential bias in the 
target sample population involves the whole-rock thermal conductivity modeling. As described 
originally by Rautman and McKenna (1997 [DIRS 100643], pp. 40 to 43), the laboratory 

measurements of thermal conductivity are systematically biased by preferential sampling of 
more coherent, generally lower-porosity/higher thermal conductivity, core specimens. This bias 
was identified by differences in the histograms of porosity for those laboratory thermal-test 
specimens (Figure 6.4-24) versus the overall histogram of porosity for the Topopah Spring 
welded unit. An additional confounding influence with respect to thermal conductivity is the 
effect of larger-than-core-size lithophysal cavities on the thermal conductivity of the rock mass 
as a whole. To reduce the impact of this identified sampling bias for thermal conductivity an 
unbiased reference distribution was constructed (see Rautman and McKenna 1997 
[DIRS 100643], pp. 40 to 43) of thermal conductivity values for simulation using a porosity-
weighted distribution of estimated thermal conductivities (Section 6.4.5.3), where the porosity 
values were obtained by relatively rigorous systematic sampling of the entire TSw model unit. 
Similar sampling bias also affects matrix hydraulic conductivity determinations, in that 
laboratory testing is skewed slightly toward measurements of the more conductive samples 
(low permeability samples take longer to run) in certain drill holes. This bias was not addressed 
explicitly in this modeling work as the total number of laboratory hydraulic conductivity 
determinations is quite large (400-plus) compared to thermal conductivity (~50 total; 35 for the 
TSw model unit), and several drill holes (especially UZ-16, SD-9) were sampled on a quite 
systematic basis for the hydraulic property (compare, for example, the locations and measured 
values of hydraulic conductivity specimens sampled from SD-9 (Rautman and Engstrom 1996 
[DIRS 100642], Figure 11) with the corresponding information for SD-7 
(Rautman and Engstrom 1996 [DIRS 101008], Figure 9)). 
6.6.1.2 Porosity as a Surrogate 
A fundamental limitation of the rock properties modeling effort clearly is the use of porosity as a 
surrogate for derivative properties of more general interest to design and performance assessment 
analysts (Section 5). These derivative properties, such as matrix saturated hydraulic 
conductivity, are related to the principal modeled property only through a correlation coefficient, 
generally indicated as r. To the extent that the absolute value of r is less than one (recall that 
r = 1.0 implies a perfect inverse relationship), this modeling of a surrogate thus increases the 
uncertainty in the secondary properties. Nevertheless, use of a nonzero correlation coefficient 
combined with incorporation of spatial correlation in the modeling of those secondary properties 
works to decrease uncertainty in those secondary properties. This is in contrast to modeling 
methodologies that discount either or both of these observable statistical characteristics. 
Knowing that a particular region exhibits high porosity values most likely translates to 
higher-than-average matrix permeability in the same region (a positive r-value). However, 
actual measured values of derivative properties are not reproduced within the simulated 
(coregionalized) models in the same manner that measured porosity values located at a grid node 
are reproduced by construction. Again, the real issue is whether the modeled uncertainty in 
material properties translates to unacceptable uncertainty in an objective performance measure 
when evaluated over a number of statistically indistinguishable simulated models (Figure 6.3-1). 

Another limitation induced by the modeling process into the rock properties models through the 
porosity-as-a-surrogate mechanism involves those hydraulic conductivity values that are not 
correlated with porosity. Flint (1998 [DIRS 100033], Figures 12(a) and (b)) describes a group of 
low-porosity samples that exhibit apparently random permeabilities with respect to their 
uniformly low porosity values, and Flint interprets these samples as exhibiting behavior 
consistent with the existence of microfractures, largely in vitrophyric rocks (lithostratigraphic 
units Tptrv1 and Tptpv3). The implication is that the permeability measured is not truly a matrix 
property, even though microfracture-related flow is measurable at the core scale. These erratic, 
out-of-porosity-character permeability values have been modeled as a random overprint imposed 
only on extremely low-porosity (< 0.05) grid nodes (interpreted as representing vitrophyre). 
Because the entire sampled population of microfractured vitrophyre-like samples consists of a 
mere dozen or so individuals, it is simply impossible to determine if these values are spatially 
correlated in their own right. However, to the extent that the measured data truly represent 
vitrophyre as a rock type (geologically restricted to the upper (Tptrv1) and lower (Tptpv3) 
margins of the TSw unit), it is thus possible to generate microfractured permeability values at 
inappropriate spatial locations within the TSw model unit. This limitation is presumed to be 
relatively minor, as the restriction of generating these values to extremely low-porosity grid 
nodes (typically between five and seven percent of the TSw grid) suggests that such rocks might 
be susceptible to microfracturing even though they would not belong to the vitrophyre-type 
small-scale lithostratigraphic units (see also Flint 1998 [DIRS 100033], Figures 12(a) and (b)). 
Additionally, conditioning of the interior of the TSw model unit to porosity values substantially 
in excess of 0.05 produces models that are very unlikely to exhibit extremely low porosity values 
except near the margins where measured porosities of this magnitude are observed. 
A similar limitation affects the modeling of altered hydraulic conductivity values within the CHn 
and Tcp model units. Although the style of hydrous-mineral-phase alteration responsible for 
these reduced matrix permeability values is clearly correlated spatially (Section 6.4.7.2), it is 
unclear whether the permeability values themselves within those altered regions are correlated. 
Adequate, spatially distributed measured Ks data do not exist to provide an estimate of the spatial 
correlation structure of permeability itself in these materials. In any event, as there is no reliable 
relationship between porosity and matrix permeability for these altered specimens, it is unclear 
what would serve as a surrogate for modeling spatial continuity for these materials. 
Accordingly, the spatial distribution of altered permeability has been treated as random within 
the spatial envelope of altered rocks, and the affected grid nodes merely assigned values sampled 
from a normal population with the appropriate mean and variance. To the extent that altered 
permeabilities are, in fact, spatially correlated, this modeling approach increases uncertainty. 
Across the full suite of simulated models, however, the variability of rock properties (and 
presumably of process modeling results as well) is likely to be greater than had the properties 
been spatially correlated, thus allowing a quantitative evaluation of the consequences of that 
increased space of uncertainty. 
6.6.1.3 Underestimation of Porosity and Hydraulic Conductivity in the TSw Model Unit 
Another limitation related to the use of porosity as a surrogate for hydraulic conductivity affects 
modeling of parts of the TSw model unit. Recall from Section 6.4.4.2 that for noncored drill 
holes in this model unit, the available petrophysical data were able to provide only an estimate of 
lithophysal porosity, because the density logging tool is sensitive to the total amount of void 

space in the rock mass, including the influence of large lithophysal cavities. Accordingly, a 
surrogate for porosity-as-a-surrogate was adopted whereby the water-filled porosity data from 
the computed VWC trace were inserted into the matrix porosity data files for the named 
lithophysal zones only. Because matrix saturations, particularly in the crystal-rich lithophysal 
zone (Tptrl) and crystal-poor upper lithophysal zone (Tptpul) are less than one where these units 
are present above the static water level, substitution of the VWC data for matrix porosity 
underestimates the true matrix porosity (such as would be obtained from core) of the rock (see 
Figure 6.4-9). Coregionalization of matrix saturated hydraulic conductivity from porosity 
models conditioned to these lowered matrix porosity data produces models of Ks that are 
systematically low in localized regions. 
The impact of this limitation on the overall material property models is limited. First, the effect 
is limited almost exclusively to the upper lithophysal intervals (Tptrl and Tptpul), as matrix 
saturations within the crystal-poor lower lithophysal zone (Tptpll) are typically sufficiently high 
that there is little mismatch between measured core porosity values and the water-filled porosity 
(VWC) measurements (see Figure 6.4-9). Second, the matter becomes an issue only for areas 
populated by noncored drill holes. The immediate Yucca Mountain-ESF area is dominated by 
drill holes for which core samples were obtained (Figure 6.4-2). Close within the range of 
influence of these cored holes, the porosity (and hydraulic conductivity) models are strongly 
conditioned by the presence of laboratory measured matrix porosity data. Furthermore, 
examination of Figure 6.4-2 indicates that the principal holes lacking core, the WT-series, are 
located primarily in regions near the periphery or surrounding the volume modeled by the rock 
properties model. It is presumed that these regions are of less direct interest in the evaluation of 
the Yucca Mountain site. Third, the additional uncertainty caused by this substitution of VWC 
data for measured matrix porosity values has already been incorporated into the simulated 
models. For regions near the main part of the ESF where noncored drill holes (such as H-5, 
Figure 6.4-2) compete with cored holes (e.g., SD-9), any discordance between the laboratory 
measurements and the VWC substitute will result in the simulation algorithm generating a wider 
range of simulated values (in the appropriate stratigraphic interval) than would be the case in the 
absence of that discordance. The space of uncertainty, as measured across the suite of 
simulations (Figure 6.3-1), has been increased, which is a realistic reflection of the state of 
knowledge associated with the limitations imposed by the use of noncore drilling techniques: the 
matrix porosity is unknown. 
6.6.1.4 Use of Major Stratigraphic Units as Modeling Units 
A quite different, but potentially significant limitation of the approach used in development of 
the rock properties model is the use of composite major stratigraphic intervals and an internal 
stratigraphic coordinate system as the geometric basis for modeling. There are only four such 
model units; compare these to the virtual plethora of different lithostratigraphic units tabulated in 
Table 6.1-1. To the extent that the stratigraphic coordinate transformation for each of these 
major modeling units does not reposition equivalent parts of the model unit at the same 
stratigraphic position, the model simulates continuity (improperly) between rocks formed at 
significantly different pressure-temperature conditions. This will create an increase of 
uncertainty across the suite of replicate simulations as the simulation algorithm tries to resolve 
any inconsistency of the measured material properties in between drill hole locations. 

This limitation is probably of minimal effect within the major ash-flow units at Yucca Mountain, 
particularly for the Topopah Spring welded unit, which was effectively an instantaneous deposit 
of massive proportions. Although the rock unit thins southward away from its source, the same 
physical/chemical conditions responsible for the ultimate physical properties of the rock almost 
certainly varied with relative vertical position within the cooling rock mass. The same logical 
argument applies to a large extent to the Prow Pass Tuff modeling unit, and (to a lesser degree) 
to the multiple-cooling-unit Calico Hills nonwelded interval. 
The PTn modeling interval contains two distinctly different, significant if only locally, developed 
pyroclastic-flow deposits (the Pah Canyon and Yucca Mountain Tuffs), separated by intervals of 
unrelated and even reworked volcanic materials. The decision to model a single PTn entity is 
based on two factors. First, in general, the properties of the rocks within the PTn unit are quite 
similar (almost all nonwelded tuffaceous materials, particularly within the region transected by 
the main part of the ESF), and especially in comparison with overlying and underlying materials. 
Second, the individual genetic units are typically very thin, leading not only to a vastly increased 
bookkeeping task for selecting and tracking sample data butmore importantlyto markedly 
reduced statistical mass relevant to any one unit. Ultimately the choice to represent the 
PTn model unit as a whole was a pragmatic determination, presumably suitable for modeling at 
the site scale. 
6.6.1.5 Faulting, Erosion, and the Stratigraphic Coordinate System 
Another limitation related to the use of a stratigraphic coordinate system is that the presence of 
erosional unconformities or within-unit faulting will work to confound the petrologic and 
material-property equivalence of rocks assigned the same stratigraphic (vertical) coordinates. 
Faults are known to affect several of the drill holes at Yucca Mountain, specifically WT-1, 
WT-11, ONC-1, and UZ-7a. Drill hole p#1 is affected by erosion, and Moyer and Geslin 
(1995 [DIRS 101269], pp. 8 to 31) report progressive lateral truncation of inferred depositional 
units within the Calico Hills Formation and Prow Pass Tuff across the model area. The effects of 
fault displacements have been included in the computation of stratigraphic coordinates based on 
the best available information, and in all cases, this compensation involves the same separations 
and uncertainties as the GFM (GFM 3.0). The locations and extents of erosional complexities 
are probably less well constrained than the effects of faulting. However, in all cases, there 
should be no discontinuities between the adjustments to stratigraphic coordinates applied to the 
rock properties modeling effort and the representation of the GFM. 
A factor working to offset uncertainties related to the stratigraphic coordinate transformation is 
that all of the properties modeling activities were conducted within that conceptual and 
mathematical framework. Specifically, the quantitative description of spatial correlation 
behavior (variograms) was conducted after conversion to stratigraphic coordinates. If undetected 
faulting or erosion worked to juxtapose samples of differing rock properties, the observed range 
of spatial correlation should be reduced, and this higher lateral variability would be reflected in 
the simulated property models. For a given set of conditioning data, a lesser degree of spatial 
correlation will also translate into more variability across the suite of realizations, and thus more 
uncertainty is (accurately) reflected in the suite of simulated models. 

6.6.2 Stochastic Uncertainty Assessment 
The entire rock properties modeling effort has been designed as one method for quantifying the 
geologic uncertaintythat which results from less-than-exhaustive site characterizationin the 
material properties used in downstream design and performance assessment analyses. The 
stochastic simulation process is intended to generate an arbitrary number of individual rock 
property models, each one of which reproduces the measured site characterization data and 
exhibits the full range of heterogeneity and spatial continuity observed in those data. This 
uncertainty in material properties then must be propagated into uncertainty in some particular 
performance measure, through some relevant consequence analysis, as depicted in the conceptual 
representation of a Monte Carlo modeling process presented in Figure 6.3-1. 
Because the emphasis in a geostatistical modeling analysis such as this one is on the joint 
characteristics of heterogeneity and spatial continuity, it logically follows that estimates of 
uncertainty are spatially variable as well. By theory and intuition, uncertainty as a statement of 
confidence is low in the immediate vicinity of observations, where the effect of conditioning the 
simulated models to measured property values leads to the construction of well-constrained 
probability density functions. At great distances from measured values, the probability density 
functions are less well constrained, which leads to the generation of more disparate values when 
considered across a suite of simulations. The space of uncertainty is greater. However, even at 
these locations, there is the constraint imposed by the statistical character of the data ensemble as 
a whole. At a minimum the unconditional probability density function is effectively equal to the 
histogram of all relevant measured values, in contrast to the uniform distribution of all physically 
possible values. 
The influence of spatial correlation in the modeling process further works to reduce uncertainty, 
regardless of the presence or absence of observed information in the vicinity. Because the 
simulated models are built sequentially, inclusion of previously simulated grid nodes in the local 
search neighborhood used in constructing the probability density function means that low values 
are likely to be generated in the vicinity of other low values and high values are likely to be 
generated near other highs. Note, however, that this relationship is not absolute. Because the 
value at each simulated grid node is derived by random sampling from the relevant probability 
density function, there is a finite chance within any one realization at each node of generating an 
unusual value from the tails of the distribution. Because the properties at any unsampled 
location are, in fact, not known, it is possible that the true property value might be quite different 
from the expectation. Across multiple simulations, it is therefore possible to account 
quantitatively for the performance or design consequences of that uncertainty by propagating 
variations in input properties through numerical process models to describe uncertainty in 
performance measures (Figure 6.3-1). 
6.6.2.1 Expectations versus Individual Outcomes 
These comments regarding the representation of spatial uncertainty related to 
less-than-exhaustive site characterization apply to the individual stochastically simulated rock 
property models. In addition to these individual simulations or outcomes, summary type models 
have been created that correspond to the mathematical expectation of a probability distribution. 
These summary models were generated by computing the node-by-node arithmetic mean value 

of the suite of individual simulations (50 in the present case), and these are typically referred to 
as E-type models. 
Note, however, that simply because a value is mathematically the most likely does not mean 
that value is the appropriate value to use for a particular purpose. In fact, the expected value 
may be a very unlikely value for any actual realization or outcome. The mathematical 
expectation of one-half where zero is heads and one is tails does not mean that the result of 
tossing a coin is most likely to be standing on edge. In similar manner, an expected porosity 
value of 0.20 at a particular grid location does not necessarily imply that 0.20 is the value of 
porosity that should be entered into a numerical flow-and-transport model at this location. It 
means that over a (large) number of individual simulated outcomes, the average simulated 
porosity at the location, individual values of which may have varied from 5 to 45 percent, was 
0.20. Those simulated values were discarded in the summary process. 
The limitations of E-type models, which are in common with the shortcomings of most 
interpolation-type modeling algorithms in representing the real world, are fairly well known. For 
example, the range of variability is always reduced with respect to variability of the input data 
(the so-called smoothing effect). Modeled values outside the limits of the measured values 
generally are impossible, even though it is rather unlikely that physical sampling has actually 
encountered the absolute highest or lowest value present in the real world. Also, the apparent 
strength of spatial correlation is typically much greater (a consequence of smoothing). And 
finally, the consequences of applying a numerical physical-process algorithm to a smoothed 
input property field may be very different from the results of applying that same algorithm to one 
(or more) of the underlying more realistically varying individual outcome models. This effect 
may be particularly important in performance assessment analyses, for which the expected 
behavior of the physical system is of less interest than high-consequence/low probability events 
representing the tails of a probability density function. Also, the flow consequences of smoothly 
varying (i.e., continuous) material properties may be quite different than the consequences of a 
more heterogeneous property representation. 
6.6.2.2 Summary Uncertainty Models 
Uncertainty in rock material properties should be evaluated rigorously in terms of the 
consequences of that variability on a particular computed performance measure. However, the 
postprocessing step that produces the E-type models is easily adapted to generate models of 
spatially distributed variability across the suite of simulations. Such a spatially varying 
representation may be thought of as a first-order uncertainty model. 
This type of summary model has been generated for this modeling activity as the node-by-node 
standard deviations of the 50 individual simulated rock property models. There is no particular 
reason to prefer the standard deviation approach other than general user familiarity. It would 
also be possible to represent in one summary model the variability of individual stochastic 
simulations through use of the inter-quartile range or the tenth-to-ninetieth-percentile difference. 
In almost any such uncertainty model, the magnitude of the spatially varying uncertainty will 
decrease effectively to zero at sample locations and increase to some maximum value at great 
distances from conditioning samples or in close proximity to samples exhibiting conflicting 

values. In all cases, it is important to separate conceptually the difference between spatial 
heterogeneity and uncertainty. 
PTn Model UnitA block view of the PTn model unit in stratigraphic coordinates is presented 
in Figure 6.6-1 showing the uncertainty model of porosity for this unit. Uncertainty is generally 
low (shown in blue colors) in the immediate vicinity of drill holes containing conditioning 
measurements (Figure 6.4-1), and increases to higher values away from these locations. 
Uncertainty is spatially heterogeneous within the model as well. However, because most drill 
holes penetrate most of each unit, the general pattern of heterogeneity in uncertainty will be that 
exhibited on the top surface of the model unit. 
TSw Model UnitBlock models presenting the uncertainty models of matrix and lithophysal 
porosity in the TSw model unit are presented in Figures 6.6-2 and 6.6-3, respectively. 
Uncertainty in the figures is calculated as the node-by-node standard deviation of the replicate 
simulated material property models. As anticipated, uncertainty is lowest in the vicinity of drill 
holes containing conditioning measurements (Figure 6.4-2), and it increases away from those 
drill hole locations. Note that there are slight differences in the uncertainty models for matrix 
and lithophysal porosity, even though drill hole coverage at this stratigraphic level is essentially 
identical. As is typical in many real-world data sets, the variance (standard deviation) is a 
function of the magnitude of the variable under consideration. 
CHn Model UnitThe uncertainty model for matrix porosity in the CHn model unit is presented 
in Figure 6.6-4, again as the node-by-node standard deviation of the 50 replicate simulated 
models of this property. Low values of uncertainty are indicated by the shades of blue, and these 
regions are associated with the drill holes containing conditioning data that penetrate this model 
unit (Figure 6.4-3). Spatial heterogeneity of uncertainty can be seen on the front face of the 
block model. However, the dominant pattern of uncertainty will be vertically downward 
associated with the vertical drill holes. 
Tcp Model UnitA block view of uncertainty in matrix porosity for the Tcp model unit, as 
computed as the standard deviation of the replicate simulated models, is presented in 
Figure 6.6-5. Uncertainty is lowest in the immediate vicinity of the drill holes penetrating this 
unit (Figure 6.4-4), and increases away from these locations of conditioning data. It is 
interesting to note the marked increase in uncertainty within the Tcp model unit, in comparison 
with the uncertainty model for the CHn model unit presented previously in Figure 6.6-4. This 
increase is most noticeable in the northeast corner of the modeled volume (note also the increase 
in the maximum value of the standard deviation in these two figures, colored red on the color 
scale). The cause of this marked change in uncertainty is the loss of drill hole WT-16 from the 
Tcp data set (compare Figures 6.4-3 and 6.4-4, showing the drill hole locations). Additional 
increases in modeled uncertainty, particularly in the eastern portion of the area, in the Tcp model 
unit result from the loss of drill holes WT-14 and WT-15 (outside the modeled volume) and drill 
hole ONC-1 (within the volume). 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Color scale is in porosity units as a fraction. Vertical exaggeration 10x. 
Figure 6.6-1. Uncertainty Model Showing E-Type Standard Deviation of Matrix Porosity in the PTn Model 
Unit 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Color scale is in porosity units as a fraction. Vertical exaggeration 10x. 
Figure 6.6-2. Uncertainty Model Showing E-Type Standard Deviation of Matrix Porosity in the TSw Model
Unit 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Color scale is in porosity units as a fraction. Vertical exaggeration 10x. 
Figure 6.6-3. Uncertainty Model Showing E-Type Standard Deviation of Lithophysal Porosity in the TSw 
Model Unit 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Color scale is in porosity units as a fraction. Vertical exaggeration 10x. 
Figure 6.6-4. Uncertainty Model Showing E-Type Standard Deviation of Matrix Porosity in the CHn Model
Unit 

Source: SN9910T0501399.001 [DIRS 129717]. 
NOTE: Color scale is in porosity units as a fraction. Vertical exaggeration 10x. 
Figure 6.6-5. Uncertainty Model Showing E-Type Standard Deviation of Matrix Porosity in the Tcp Model 
Unit 

INTENTIONALLY LEFT BLANK 

7. VALIDATION 
The purpose of this section is to validate the model by showing how the confidence building 
criteria were satisfied during and after model development. Section 7.1 explains how the model 
development process satisfies the criteria in the TWP (BSC 2004 [DIRS 169635] and 
AP-SIII.10Q. Section 7.2 provides a detailed discussion of the postdevelopment validation of 
the product output parameters: matrix and lithophysal porosity and bulk density. These 
validations were demonstrated with data not relied on as direct input in the construction of the 
model. These parameters are used by Saturated Zone Flow and Transport Model Abstraction, 
MDL-NBS-HS-000021, REV 02 (BSC 2004 [DIRS 170042]). 
In contrast, the representations for thermal conductivity and hydraulic conductivity developed in 
this report are not used as direct input by project documents and will not be validated. 
Therefore, the representations of thermal conductivity and hydraulic conductivity developed in 
this report should not be cited as direct input in support of the Total System Performance 
Assessment for License Application (TSPA-LA). More recent representations of thermal 
conductivity and hydraulic conductivity are available and should be cited instead. For thermal 
conductivity the reader is referred to Thermal Conductivity of the Potential Repository Horizon 
Model Report, (BSC 2004, [DIRS 169854], Table 7-10 and Table 7-11) and Thermal 
Conductivity of the Non-Repository Lithostratigraphic Layers Model Report, (BSC 2004 
[DIRS 170033], Table 6-13). Similarly, for hydraulic conductivity, the reader is referred to 
Analysis of Hydrologic Properties Data (BSC 2004 [DIRS 170038], Table 6-6). 
The level of confidence required for the model validation activity for the rock property model 
has been determined from the guidelines in AP-2.27Q, Attachment 3, Levels of Model 
Importance, Validation, and Confidence to be Level I for the following reasons. Because the 
rock properties model does not provide any direct input to the total system performance model, 
its level of significance depends in part on its association with models that do provide direct 
input to the TSPA-LA. The rock properties model is associated with the Saturated Zone Flow 
and Transport Model Abstraction MDL-NBS-HS-000021, REV 01 (BSC 2003 [DIRS 167651]) 
which is a direct feed to the TSPA-LA. The Saturated Zone Flow and Transport Model 
Abstraction has been identified as Level II in Table 1 of AP-2.27Q. However, the Saturated 
Zone Flow and Transport Model Abstract is insensitive to the rock properties model parameters 
cited when compared to the models overal uncertainty. Citation of the rock properties model 
parameters are as mean values and used as deterministic or constant values. Accordingly 
validation Level I applies. 
7.1 CONFIDENCE BUILDING DURING MODEL DEVELOPMENT 
For Level I validation, Section 2.2.3 of TWP-NBS-GS-000003 REV 05 (BSC 2004 
[DIRS 169635]) specifies the following steps for Confidence Building During Model 
Development. The development of the model should be documented in accordance with the 
requirements of Section 5.3.2(b) of AP-SIII.10Q. Attachment 3 of AP-2.27Q also provides 
model validation guidance that is documented in the TWP (BSC 2004 [DIRS 169635] 
Section 2.2.3). 

The development of the rock properties model has been conducted according to these criteria, as 
follows: 
1. Selection of input parameters and/or input data, and a discussion of how the selection 
process builds confidence in the model [AP-SIII.10 Q, 5.3.2(b) (1) and AP-2.27Q, 
Attachment 3 Level I (a)]. 
The seven classes of data used as inputs to the rock properties model have all been 
obtained from controlled sources (see Section 4.1, Tables 4-1 through 4-7). Each of 
the seven classes of data are described and discussed in Section 4.1. Each discussion 
documents the data attributes that were the basis for their selection as model inputs. 
All input data are qualified. Two data qualification activities were conducted for the 
XRD mineral abundance data. See Section 4.1.4, Steinborn 2002 [DIRS 160702]), 
and Appendix E through H. 
Porosity has been used as a surrogate parameter for parameters of interest. The basis 
for this is discussed in Section 5. More broadly the use of scientifically acceptable 
concepts to construct a rock property model which addresses heterogeneity and 
uncertainty in ways that make the model adequate for its intended purposes are 
discussed in detail in Sections 6.3 and 6.4. 
For these reasons, the process for selecting input parameters meets the applicable 
criteria and, thus builds confidence in the model. 
2. Description of calibration activities, and/or initial boundary condition runs, and/or 
run convergences, simulation conditions set up to span the range of intended use and 
avoid inconsistent outputs, and a discussion of how the activity or activities build 
confidence in the model. Inclusion of a discussion of impacts of any non-convergence 
runs [AP- SIII.10Q , 5.3.2(b)(2) and AP-2.27Q, Attachment 3 Level I (e)]. 
Calibration related activities for the rock properties model, including the extensive 
processing of data to prepare it to support calibration are discussed in detail in Section 
6.4. Section 6.4 documents the spatial character and correlation of the input 
parameters modeled used to develop the model results. Section 6.4.1 and 6.4.2 discuss 
the model domain boundaries and subdivisions, respectively. Section 6.4.3 describes 
the basis for selecting the appropriate data and the processes used to process that data 
for use. Section 6.4.4 discusses porosity, and Section 6.4.5 discusses the secondary 
parameters matrix saturated hydraulic conductivity, bulk density, and thermal 
conductivity. 
Discussion about nonconvergence runs is not relevant for this model report. 
For these reasons, the process for selecting input parameters meets the applicable 
criteria and, thus builds confidence in the model. 
3. Discussion of the impacts of uncertainties to the model results including how the 
model results represent the range of possible outcomes consistent with important 

uncertainties (AP-SIII.10 Q, 5.3.2(b)(3) and AP-2.27Q, Attachment 3 Level 1 (d) and 
(f)). 
The impacts of model uncertainties and limitations on model results are provided in 
Section 6.6 and 8.2. The primary uncertainties are intrinsic to the input data or the 
modeling methodology. Measurement errors are shown in Section 6.6.1.1 and do not 
significantly affect the model results because they are small compared with the lateral 
heterogeneity of the rock mass. Known systematic biases in measured values do not 
significantly affect the model results because the biases are compensated for as 
described in Section 6.6.1.1. Methods and/or reasons that support a decrease in the 
possible uncertainties and limitations resulting from the use of porosity as a surrogate 
for other rock properties are discussed in Sections 6.6.1.2 and 6.6.1.3. Methods and/or 
reasons that support a decrease in the possible uncertainties and limitations resulting 
from the use of stratigraphic coordinates are discussed in Sections 6.6.1.4 and 6.6.1.5. 
Finally, as discussed in Section 6.6.2, uncertainty models are provided for use in 
calculations. 
A summary discussion on uncertainties and their impact is given in Section 8.2. That 
section explains that the rock properties model is not intended to make location-
specific predictions of rock properties. Rather, it is intended to provide a suite of 
equally plausible, realistic models which collectively describe the space of uncertainty 
for the relevant material properties and which can be used in Monte Carlo-style flow 
and transport calculations. 
For these reasons, the impacts of model uncertainties on model results and the range of 
possible outcomes consistent with important uncertainties have been adequately 
addressed to meet the applicable criteria and build confidence in the model. 
4. Formulation of defensible assumptions and simplifications [AP-2.27Q , Attachment 3 
Level I (b)]. 
The basic assumption for this model is the use of porosity as a surrogate to model the 
other rock properties of interest. Section 5 provides a defensible justification for this 
assumption. Accordingly, this criterion is met in a way that builds confidence in the 
model. 
5. Consistency with physical principles, such as conservation of mass, energy, and 
momentum [AP-2.27Q , Attachment 3 Level I (c)]. 
Because the rock properties model is a static representational model, consistency with 
physical principles is expressed through the use of standard geostatistical methods 
rather than dynamic equations expressing mass; energy and momentum. The model is 
consistent with applicable geologic and geostatistical principles. These principles and 
their applicability are discussed in Sections 6.1 and 6.2. 

7.2 CONFIDENCE BUILDING AFTER MODEL DEVELOPMENT TO SUPPORT 
THE SCIENTIFIC BASIS OF THE MODEL 
For confidence building after model development TWP-NBS-GS-000003 REV 05 (BSC 2004 
[DIRS 169635]), Section 2.2.3 imposes the following requirements for model validation for 
matrix porosity, lithophysal porosity, and bulk density. 
7.2.1 Validation: Matrix Porosity 
The purpose of this section is to validate the model by corroborating the model output 
representation of matrix porosity with data not cited as direct input. The product output values 
for the mean matrix porosity values for the model layers are listed in Table 6.4-2 and provided 
by DTN: SN0004T0501399.003 [DIRS 155045]. The difference between matrix and lithophysal 
porosity are discussed in Section 6.4.4.2 and illustrated by Figure 6.4-9. The planned approach 
for validation is corroboration with data that were not used as direct input in the development of 
the product output of this report. The explicit criterion for successful validation is that the mean 
value calculated for the corroborating data set should fall within the range defined by the mean 
value and standard deviation for the model layers listed in Table 6.4-2. 
The corroborative data used to validate the model are presented in Table 7-1. The matrix 
porosity and bulk density data were obtained from laboratory measurements on core samples. 
As shown in Table 6.1-1 the Tptpll zone is part of the TSw model layer. Therefore, the mean 
matrix porosity value calculated from DTN: SN0209L01A1202.001 [DIRS 163601] will be 
compared to the range for the TSw model layer in Table 6.4-2. 
Table 7-1. Matrix Porosity and Bulk Density Data Supporting Model Validation 
Data Source Description 
Reference 
Corroborative matrix porosity and bulk 
DTN: SN0209L01A1202.001 [DIRS 163601] 
density data 
DIRS = Document Input Reference System; DTN=data tracking number. 
The mean value obtained from DTN: SN0209L01A1202.001 [DIRS 163601] is 11.5 percent 
with a standard deviation of 2.7 percent. The range for the TSw model layer defined by the 
DTN: SN0004T0501399.003 [DIRS 155045] is based on the mean value of 12.2 percent and a 
standard deviation of 6.1 percent. Therefore, the validation range for the TSw model layer is 
6.1 to 18.3 percent. The mean value of the corroborating data fall within the range specified by 
the validation criterion, therefore this validation is determined to be successful. 
This validation is deemed to be adequate and accurate for the following reasons: First, 
regardless of the range of validation the corroborative and model mean values are very close, 
11.5 percent compared to 12.2 percent, respectively. The range of analytical uncertainty for 
porosity is 1 percent porosity (or one porosity unit 0.01) (Brodsky, 2003 [DIRS 164584]). 
Therefore, the difference between the two calculated means is within the analytical uncertainty 
for the parameter. Second, this result is not unanticipated, as the matrix porosity values for the 
TSw layer (Tptprn to Tptpln) obtained by measurement on core samples or calculated from 
borehole geophysical data are very consistent. This is illustrated in Figure 6.4-9 that shows the 
overall consistency in matrix porosity values. Third, the mean values for matrix porosity are 

based on values directly measured from core samples (Table 3-2). These data are qualified and 
verified and are either supported by a records package or data qualification report that establishes 
that its adequate for its intended use. 
Note that although this validation is only performed for the TSw model layer, it is appropriate to 
extend it as supporting the validation of the other model layers because all the data used in this 
corroboration are measured values of matrix porosity. For example, because all the data are 
measured values and the measurement of matrix porosity on core samples is relatively 
standardized, differences would most likely indicate differences in sampling. Although a similar 
corroborative validation could be performed for the other model layers, the known heterogeneity 
observed in the PTn and CHn layers makes it more appropriate to compare the corroborative data 
to the matrix porosity histogram distributions, Figures 6.4-6 and 6.4-12, respectively. 
Corroboration for the Tcp model layer could be performed based on the mean value provided in 
Table 6.4-10, or the histogram distribution provided by Figure 6.4-14. 
7.2.2 Validation: Lithophysal Porosity 
The purpose of this section is to validate the model by corroboration of model output 
representation of lithophysal porosity with data not cited as direct input. The product output 
values for mean porosity value for the TSw layer is listed in Table 6.4.2 and provided by 
DTN: SN0004T0501399.003 [DIRS 155045]. The planned approach for validation is 
corroboration with data that were not used as direct input in the development of the product 
output of this report. The explicit criterion for successful validation is that the mean value 
calculated for the corroborating data set should fall within the range defined by the mean value 
and standard deviation for the model layers listed in Table 6.4-2. 
The independent corroborative lithophysal porosity data are collected from the ECRB and 
presented in Table 7-2. Two sets of ECRB data are available for comparison. The first is 
provided by Mongano et al., (1999, ACC: MOL.20000324.0614 [DIRS 149850], p. 77, 
Figure 13), and a second is from the Drift Degradation Analysis Report, ANL-EBS-MD-000027, 
REV 02 (BSC 2004 [DIRS 166107], Section 6.1.2, 6.1.4.2, and Attachment XV). The 
lithophysal data that are presented in the Drift Degradation Analysis Report are provided by the 
DTN: GS021008314224.002 [DIRS 161910]. 
Table 7-2. Lithophysal Porosity Data Supporting Model Validation 
Data Source Description Reference 
ECRB Lithophysal porosity data ACC: MOL.20000324.0614 [DIRS 149850] 
ECRB Lithophysal porosity data DTN: GS021008314224.002 [DIRS 161910] 
ACC = Accession Number; ECRB = Enhanced Characterization of the Repository Block; DIRS = Document Input 
Reference System; DTN = data tracking number 
Corroborating RPM lithophysal porosity data with data acquired from the ECRB is not 
straightforward. The following factors that complicate the comparison: 
 
Direct comparison ECRB and borehole data are not possible since the ECRB is oriented 
horizontally, and boreholes are oriented vertically. For the most part the boreholes 
transect and, therefore, sample the horizontal formation completely (example, 

DTN: GS000308314211.002 [DIRS 158000]) when logged by borehole geophysical 
tools. However, horizontal transects, such as the ECRB, are affected by repeated 
sections or truncations of the formation due to faulting (Mongano et al. 1999 
[DIRS 149850], Table 1 and Figure 13). The table and figure document the contact 
between Tptpln-Tptpul is due to the Solitario Canyon fault at Station 25+85, while the 
contact between TptpulTptrl is due to an unnamed fault at Station 26+57.5. These 
faults have truncated the true section of the formations, though the amount of missing 
section is not provided by the reference. 
 
The calculation of lithophysal porosity that is presented in this report is based on data 
collected from borehole geophysical tools. The data acquired from the ECRB are 
obtained by visual observation of the tunnel surfaces. Because the density and neutron 
borehole tools used to calculate lithophysal porosity are known to have a depth of 
investigation into the surrounding rock, they are by definition providing an mean value 
of the rocks petrophysical properties (Schlumberger, 1972 [DIRS 129068]). The effect 
of this averaging is difficult to assess without a means of calibration, and therefore 
difficult to assess. It should be considered when comparing these two types of data. 
 
The distribution of lithophysae is not homogeneous vertically or horizontally, in the 
Topopah Spring Tuff. Vertically, lithophysae abundance decreases with depth within 
each zone of the Topopah Spring Tuff. (This is illustrated in Figure 13 from Mongano 
et al., 1999 [DIRS 149850]). Laterally, lithophysae abundance decreases from the 
central region of the modeled area as illustrated in Figure 6.5-9. This lateral decrease in 
lithophysal porosity is most likely related to the lateral thinning of the Topopah Spring 
Tuff from the central region of the model. (This is illustrated in Figure 6-15 of the 
Geologic Framework Model report, BSC 2004 [DIRS 170029]). Further this figure 
shows that the ESF and ECRB are located in a region with a thick consistent deposit of 
Topopah Spring Tuff. This shows that we should expect higher values of lithophysal 
porosity in the central regions of the model and the ESF and ECRB and that it should 
decrease near the margins of the modeled area. 
 
The mean value of lithophysal porosity for the Topopah Spring Tuff has been 
determined for the entire welded portion of the formation as shown in Table 6.1-1. Most 
other determinations of lithophysal porosity have been determined on portions of the 
welded Topopah Spring Tuff or analyzed based on zones of the Topopah Spring Tuff. 
The results of the of lithophysal porosity measurements by Mongano et al., 
(1999 [DIRS 149850]) are presented in Figure 7-1. The mean value for lithophysal porosity 
determined from the graphical data has been calculated to be 21.3 percent (BSC 2004 
[DIRS 169854], Table 7-2). The range defined by the mean value and standard deviation for 
lithophysal porosity in DTN: SN0004T0501399.003 [DIRS 155045]) is 7.0 to 22.2 percent. The 
mean value of the corroborating data set is observed to fall within the validation range for the 
TSw layer, therefore this validation is determined to be successful. Furthermore, the mean value 
for lithophysal porosity is higher for the independent corroborative data than the models mean 
value. Therefore, in terms of its specific use in the Saturated Zone Flow and Transport Model 
Abstraction, (BSC 2004 [DIRS 170042]) the models mean value is conservative when compared 
to the independent corroborative value when evaluating SZUZflow. 

Figure 7-2 illustrates the lithophysal porosity measurements from DTN: GS021008314224.002 
[DIRS 161910], Table: S03045_001, File Name: Tptpll Litho PMap A-T-Trav L-Litho 
Graph.pdf. This DTN provides three measurements of lithophysal porosity, tape and angular 
measurements, and panel maps (DTN: GS021008314224.002 [DIRS 161910], Table Name: 
S03045_001, File Name: Tptpll Lithop SEP Data File.xls). The mean value for the tape 
measurements is 19.4 percent with a standard deviation of 10.2 percent. The mean value for the 
angle measurements is 15.3 percent with a standard deviation of 6.9 percent. The mean value for 
the panel mapping is 12.9 percent with a standard deviation of 3.6 percent. Based on the results 
of the angle measurements and panel maps the values for the tape measurements were then 
corrected. The mean value and standard deviation for the corrected tape measurements is 
13.1 and 5.3 percent, respectively (BSC 2004 [DIRS 166107], Table XV-10). 
Source: Mongano et al., 1999 [DIRS 149850], Figure 13. 
Figure 7-1. ECRB Lithophysal Porosity Versus Stationing 
Source: GS021008314224.002 [DIRS 161910], File Name: Tptpll Litho PMap A-T-Trav L-Litho Graph.pdf 
Figure 7-2. Calculated Porosity of Lithophysal Cavities Calculated By Tape, Angular, and Panel Map 
Measurements in the Tptpll Exposed Along the ECRB Cross-Drift 

As illustrated while the tape measurements are available for the entire exposure of the Tptpll 
zone the mean value of 19.4 percent is high when compared mean value observed for the entire 
ECRB of 21.3 percent in Mongano et al., (1999 [DIRS 149850]). This is graphically illustrated 
by examining Figure 7-1. It was felt that the angular traverses and panel map data were of 
similar resolution (BSC 2004 [DIRS 166107], Attachment XV, Section XV.1) and therefore the 
results of the tape measurement data were corrected (BSC 2004 [DIRS 166107], Attachment 
XV, Table XV-10). This correction shifted the mean value for lithophysal porosity from 
19.4 to 13.1 percent that appears to be consistent with the results provided by Mongano et al., 
(1999 [DIRS 149850]). 
The range for validation of lithophysal porosity for the TSw layer is 7.0 to 22.2 percent. The 
mean values for all of the corroborating data sets are observed to fall within the range defined by 
model layer, therefore this validation is determined to be successful. 
Prior to discussing the adequacy and accuracy of this validation the three factors listed earlier 
will be addressed. While the three factors complicate the corroboration they do not invalidate it. 
First, while faults are observed in the ECRB they occur towards the end of the tunnel and do not 
appear to significantly truncate the Topopah Spring Tuff zones. Second, to account for regional 
and stratigraphic differences in lithophysal porosity, values from the model could be extracted 
along the ECRB transect for comparison. However, for the purposes of validating the mean 
lithophysal porosity value for the TSw layer this activity was not warranted. Third, while none 
of the ECRB derived data samples the entire TSw model layer the data from Mangano et al., 
(1999 [DIRS 149850]) nearly does. Further, the examination of the Tptpll zone 
(BSC 2004, [DIRS 166107] and DTN: GS021008314224.002 [DIRS 161910]) provides results, 
which are consistent with the results of the Mongano et al., (1999 [DIRS 149850]) data. 
In addition the mean value for lithophysal porosity provided by this model report is 14.6 percent. 
This value is used in the Saturated Zone Flow and Transport Model Abstraction, 
MDL-NBS-HS-000021, REV 02 (BSC 2004 [DIRS 170042]). The mean value from Mongano 
et al., (1999 [DIRS 149850]) is 21.3 percent. The mean value from the Drift Degradation 
Analysis Report (BSC 2004 [DIRS 166107]) is 13.1 percent. Therefore, the mean values for the 
two independent corroborating data sets bracket the value provided by this model report. The 
bracketing of model reports mean value by the two independent data sets provides further 
confidence that it is appropriate for its intended use. 
This validation is deemed to be adequate and accurate for the following 
reasons: 1) Comparisons between the data used for corroboration with the models product 
output the lithophysal porosity values and overall trends are comparable and consistent. The 
corroborative data are also consistent with the complicating factors that affect the distribution of 
lithophysal porosity; borehole versus tunnel data, vertical and lateral heterogeneity, and differing 
stratigraphic divisions. 2) The method of determining lithophysal porosity values differs for the 
model and the corroborative data set, and both are equally valid. The models product output 
was calculated based on borehole geophysical data, while the corroborative data are based on 
physical measurements or visual observations. Both techniques are valid methods of 
determining lithophysal porosity. 

7.2.3 Validation: Bulk Density 
The purpose of this section is to validate the model by corroborating the correlated relationship 
between porosity and bulk density illustrated in Figure 6.4-21b (DTN: SN0004T0501399.002) 
[DIRS 155046] with data not cited as direct input. The linear correlation shown in Figure 7-3 
and the model layers average matrix porosity are used to determine the layers average bulk 
density in the Saturated Zone Flow and Transport Model Abstraction 
(BSC 2004 [DIRS 170042], Section 4.1]. The planned approach for validation is corroboration 
with data that were not used as direct input in the development of the product output. The 
explicit criterion for successful validation is if corroborating data set fall within the field defined 
by the data used as input or within the boundary defined by analytical uncertainty surrounding 
the observed field. The analytical uncertainty for porosity is defined as 1 percent porosity (or 
1 porosity unit), and the uncertainty associated with bulk density is the measurement error 
determining its mass and volume (Brodsky 2003 [DIRS 164584]). 
NOTE: Series 1 are data from direct input DTN: SN0004T0501399.002 [DIRS 155046]. 
Series 2 are corroborative data from DTN: SN0209L01A1202.001 [DIRS 163601]. 
Figure 7-3. Scatterplot of Bulk Density as a Function of Matrix Porosity for Corroborating and Input Data 
The independent corroborative data listed in Table 7-1 (DTN: SN0209L01A1202.001 
[DIRS 163601]), are used to validate bulk density and also used to validate matrix porosity. 
Figure 7-3 shows that the corroborative data (DTN: SN0209L01A1202.001 [DIRS 163601]) fall 
within the field defined by the data used to construct the model. Therefore, this validation 
activity is determined to be successful. 

In addition, relative to the linear correlation used to establish the layers bulk density, the 
corroborative data plot above the linear correlation line with higher values. Bulk density is 
related to the retardation factor, R , of a sorbing constituent as follows (Freeze and Cherry 1979 
[DIRS 101173], equation 9.14): 
R =v = 1 +
.b * Kd (Eq. 7-1) 
vc 
n 
where 
v is the average linear velocity of the ground water 
/
vc is the C C 0 = 0.5 point on the concentration profile of the retarded constituent (where 
C is the solute concentration and C0 is the original solute concentration) 
.
b is the bulk density 
n is the porosity 
K
d is the distribution coefficient. 
Equation 7-1 shows that the retardation factor decreases as the bulk density decreases. 
Comparing the linear correlation used to establish the bulk density for the model layers and the 
corroborative data, the bulk density values of the corroborative data are generally higher. 
Therefore, the use of the correlation line conservatively underestimates the retardation of 
radionuclides in the saturated zone. 
While the corroborating data are observed to fall within only a portion of the field defined by the 
model data this validation is deemed to be adequate and accurate for the following 
reasons. 1) The corroborating and input data sets are all obtained by direct measurements from 
core samples. All of these data are qualified and verified and are either supported by a records 
package or data qualification report that establishes that it is adequate for its intended use. The 
restricted range observed for the corroborative data is indicative of the relatively low and 
consistent matrix porosity values observed for the Tptpll zone from which they were collected. 
2) As illustrated in Figure 6.4-22b and discussed in Section 6.4.5.2 location of the Tptpll samples 
in Figure 7-3 are consistent with the highly densely welded TSw model units and therefore 
consistent with model results. The low-density samples are observed to represent nonwelded ash 
fall PTn samples (Figure 6.4-22a). Intermediate density nonwelded to partially welded materials 
are represented by the CHn and Tcp model units (Figures 6.4-22c and 6.4-22d, respectively). 
7.3 MODEL VALIDATION SUMMARY AND CONCLUSIONS 
The model validation of matrix and lithophysal porosity and bulk density met the explicit 
validation criteria and therefore the validation activities were determined to be successful. The 
model validation criteria were established in accordance with the procedural requirements based 
on the required level of confidence. Therefore, by process, the validation criteria met are 
consistent with the stated level of confidence required for the model. Further, the model product 
outputs were corroborated with equivalent or comparable data. This permitted a direct 

comparison with the product output that adds additional confidence that the stated validation 
criteria are consistent with the stated level of confidence. Activities required for confidence 
building during model development have also been satisfied. The model development activities 
and the postdevelopment validation activities described establish the scientific bases for the rock 
properties model. Based on this, the rock properties model is considered to be sufficiently 
accurate and adequate for the intended purpose and to the level of confidence required by the 
models relative importance to the potential performance of the repository system. 
As stated in the TWP and demonstrated by the successful model validation documented in this 
report no future or additional validation activities are needed for this model. 

INTENTIONALLY LEFT BLANK 

8. CONCLUSIONS 
The RPM provides exhaustive, 3-D, discretized, numerical representations of several important 
hydrologic and thermal rock properties (porosity, bulk density, thermal conductivity, and 
hydraulic conductivity) that are intended for further use in numerical design and performance 
assessment analyses. The four composite modeling units defined for this analysis encompass the 
majority of the rocks within the UZ throughout the immediate vicinity of the repository at Yucca 
Mountain. The product output from the RPM is provided by the DTNs: SN9910T0501399.001 
[DIRS 129717], SN0004T0501399.002 [DIRS 155046], and SN0004T0501399.003 
[DIRS 155045]. It should be noted that as discussed in Section 7 the product output parameters 
matrix and lithophysal porosity and bulk density have been validated. For thermal and hydraulic 
conductivity the user should refer to the technical products discussed in Section 7. 
There are no confirmatory actions, such as compliance runs, additional sensitivity runs, and 
neutralization runs contained in this model report. 
8.1 SUMMARY OF MODEL ACTIVITIES AND DESCRIPTIVE RESULTS 
Premodeling geostatistical analysis has produced a statistical and spatial continuity description of 
porosity for each of the four modeling units. Spatial correlation patterns are unit-specific and are 
relatively complex; nested variogram models were required to fit the observed continuity 
patterns. Spatial correlation is anisotropic, both vertically and in the stratigraphically horizontal 
plane. For porosity, a significant fraction of the total variability is reached within a distance of 
2,000 to 6,000 feet, although another significant fraction exhibits a maximum range of between 
10,000 and 38,000 feet. The spatial correlation of hydrous-phase mineral alteration was also 
evaluated for the lower two modeling units. Variogram patterns again are unit-specific, and the 
range of maximum correlation is observed to vary between 7,000 and 15,000 feet. 
Cross-variable correlations between porosity and the several secondary material properties have 
been analyzed on a global basis. 
The individual model components have been constructed using geostatistical simulation 
methods, conditioned to drill hole-based measurements of porosity, with the result that the 
individual stochastic models are heterogeneous, spatially correlated, and essentially 
indistinguishable statistically from the set of measured values. These simulated models of 
porosity have been provided as input to a linear coregionalization algorithm, which has been 
used to generate simulated models of the derivative material properties, such as bulk density, 
matrix hydraulic conductivity, and thermal conductivity. These derivative property models are 
also spatially correlated and heterogeneous and are close statistical replicas of the set of 
measured secondary property data. Cross-variable correlations exist, and the strength of these 
correlations is approximately that described by the input sample correlation coefficients. 
Unit-specific postprocessing steps have been used to impart additional desired property attributes 
such as microfractured hydraulic conductivity associated with very low-porosity (vitrophyric) 
welded tuffs and different hydraulic conductivities associated with altered and unaltered 
nonwelded tuffs. 
Sets of 50 simulations for each material property in each modeling unit have been postprocessed 
to provide spatially varying models of the node-by-node standard deviation of the replicate 

individual models. These representations of the spatially heterogeneous inter-simulation 
variability serve as a first-pass uncertainty model for each material property. The same 
postprocessing run has also been used to generate a summary expected-value type (E-type) 
model, defined as the arithmetic mean value of the 50 input replicate simulated models. 
Although the averaging process distorts the statistical character of the conditioning data and 
simulated models, these E-type models are useful for visualizing large-scale heterogeneities in 
material properties across the site area. 
Visual examination of block diagrams and cross-sectional views through the various model 
components indicate that there is substantial, complex internal heterogeneity in all four modeling 
units, although the E-type models reveal material properties broadly compatible with the 
conceptual model of layered volcanic stratigraphy at Yucca Mountain. Features such as 
high-porosity lithophysal intervals that correlate approximately with formally named 
lithophysal zones have been generated based solely on measured porosity values. However, it 
can be demonstrated that such high porosity intervals exist outside the formally named zones. 
Matrix porosity is also higher associated with these intervals of high lithophysal porosity. 
Major heterogeneity of matrix saturated hydraulic conductivity, which reflects principally the 
distribution of hydrous-phase mineral alteration in the CHn and Prow Pass Tuff model units, is 
internally complex. Some interfingering of altered and unaltered material is indicated. 
8.2 UNCERTAINTY AND RESTRICTIONS 
The concept of uncertainty in terms of limitation on the accuracy of the RPM does not 
particularly apply to rock properties models in the usual sense. The intent of the rock properties 
modeling activities is not to make location-specific predictions, but rather to provide an entire 
suite of equally plausible, realistic models that collectively describes the total space of 
uncertainty associated with the relevant material properties. No representation is made that the 
individual simulated models are accurate (except at data locations). However, these same 
individual models are represented as geologically reasonable and consistent with all known 
information, and collectively they have been demonstrated (through validation) to be 
near-perfect replicas of the (geo)statistical character of the data ensemble used in their 
construction. It is fully intended that the consequences of the geologic uncertainty represented 
by the suites of models be evaluated through Monte Carlo-style flow-and-transport calculations 
or other suitable analyses. 
Methodological uncertainties involved in the RPM include those inherent in any statistical study, 
and these relate principally to uncertainties (error or bias) present in the input measurements used 
to condition the modeling algorithms. Measurement errors are presumed to be small in 
comparison with spatial heterogeneity. Two instances of measurement bias have been identified, 
but both have been compensated for to the extent possible. Some increased uncertainty is 
induced in the models of derivative material properties through the use of coregionalization 
(the concept of porosity-as-a-surrogate, based solely on the correlation coefficient) as the 
modeling algorithm (in contrast to conditional cosimulation). However, because of the overall 
modeling focus on defining spaces of uncertainty rather than on locally accurate predictions, 
the variability across a given suite of coregionalized models will be larger than it would 
otherwise, and hence that increased uncertainty is already accounted for. The use of stratigraphic 
coordinates for modeling purposes can act to increase uncertainty in the presence of significant 

erosion within a modeling unit or in the presence of faulted sections. However, instances of 
these confounding geologic features are known to be relatively few in the specific rocks being 
modeled, and the increased precision gained by correlating materials formed under similar 
temperature-pressure conditions afforded by the stratigraphic-coordinate conversion overall 
appear to be well worth the local distortions caused by erosion and/or faulting. 
Restrictions on subsequent use of the rock properties models are minimal and most are 
completely consistent with the Monte Carlo modeling methodology adopted as the conceptual 
framework for this activity. It is the individual simulated models (matched by run number across 
different properties to preserve the joint spatial character) that are intended for subsequent use in 
various relevant consequence analyses. The summary E-type models are not intended for 
general use in downstream analyses, except perhaps for individually justified special cases, 
because of their known distortion of the (geo)statistical character of the measured data. 
The models are useful for the stratigraphic intervals considered. Rocks belonging to the welded 
portion of the Tiva Canyon Tuff, as well as overlying units, were not modeled. Neither were 
deeper rocks, occurring principally in the SZ at Yucca Mountain, beginning at the stratigraphic 
level of the Bullfrog Tuff. The models are not valid beyond the defined lateral limits of the 
RPM, although the (geo)statistical descriptions and relevant measured values could be used to 
create other models not subject to these lateral limits. However, such extended modeling should 
be restricted to the outflow facies (in contrast to the caldera-margin or intra-caldera facies) of the 
several ash-flow tuff units involved in the four modeling units. 
A final restriction involves the saturated hydraulic conductivity models of the Topopah Spring 
welded modeling unit (only). These hydraulic conductivity models represent matrix 
permeability only, and they do not consider the effect of large lithophysal cavities that may be 
connected by either matrix porosity or by fractures. Thus, the matrix hydraulic conductivity 
models are intended more as the upper permeability limit of the unsaturated hydraulic 
conductivity function at near-full matrix saturation, rather than as representing the permeability 
of the TSw model unit were it present within the SZ. As the TSw unit is almost invariably high 
within the SZ in the immediate vicinity of the repository (the modeled area), this restriction is of 
little practical effect. Note that the substitution of water-filled porosity (volumetric water 
content) within the upper lithophysal zone (in particular) for drill holes from which no core 
measurements are available leads to a slight underestimation of the true (fully saturated) matrix 
hydraulic conductivity in those regions. Again, however, the modeled values are reasonable 
estimates of the upper permeability limit of the unsaturated hydraulic conductivity function at the 
ambient matrix saturation. 
8.3 CONCLUSIONS FOR YMRP CRITERIA 
The following information describes how this analysis addresses the acceptance criteria in the 
Yucca Mountain Review Plan (NRC 2003 [DIRS 163274], Section 2.2.1.3.9.3). Only those 
acceptance criteria that are applicable to this report (see Section 4.2) are discussed. In most 
cases, the applicable acceptance criteria are not addressed solely by this report; rather, the 
acceptance criteria are fully addressed when this report is considered in conjunction with other 
analysis and model reports that describe radionuclide transport in the SZ. Where a subcriterion 

includes several components, only some of those components may be addressed. How these 
components are addressed is summarized below. 
Acceptance Criteria From Section 2.2.1.3.9.3, Radionuclide Transport in the Saturated Zone. 
Parameters developed in this analysis and used in the SZ radionuclide transport models are 
matrix and lithophysal porosities and bulk density. This information is provided for use as input 
to numerical physical-process modeling, such as of ground-water flow and/or radionuclide 
transport. 
Acceptance Criterion 1: System Description and Model Integration Are Adequate. 
Subcriterion (2): The description of the aspects of geology provided by the Rock Properties 
Model are adequate. This model provides a determination of the matrix and lithophysal 
porosities and bulk density of the rock types at Yucca Mountain. This information is used to 
support physical process modeling for radionuclide transport in the SZ (see Section 6.1). The 
conceptual basis, development, and validation of the model are presented in Sections 6.3, 6.4, 
and 6.7, respectively. 
Acceptance Criterion 2: Data Are Sufficient for Model Justification. 
Subcriterion (3): The matrix and lithophysal porosities and bulk density of the Yucca Mountain 
rock types provided by the Rock Properties Model were developed based on appropriate 
techniques. The techniques involved measurements of rock material properties, either in the 
laboratory or by calculation from geophysical measurements obtained in the field. These 
processes are discussed and their appropriateness justified in Sections 4.1.1 through 4.1.7 and in 
the reports referenced by those sections. The techniques for determining the lithophysal 
porosities from the borehole information are discussed in Sections 6.1, 6.3, and 6.4, and the 
results are presented in Section 6.5. 

9. INPUTS AND REFERENCES 
9.1 DOCUMENTS CITED 
The following is a list of the references cited in this document. Column 2 represents the unique 
six-digit numerical identifier (the Document Input Reference System [DIRS] number), which is 
placed in the text following the reference callout (e.g., BSC 2002 [DIRS 155950]). The purpose 
of these numbers is to assist the reader in locating a specific reference in the DIRS database. 
Within the reference list, multiple sources by the same author and date (e.g., BSC 2002) are 
sorted alphabetically by title. 
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(GWTT-95). SAND96-0819. Albuquerque, New Mexico: Sandia National 
Laboratories. ACC: MOL.19961209.0152. 
Bish, D. 2001. TWS-ESS-1-7/86-35, Brinkman Sample Grinding Logbook. 169750 
SN-LANL-SCI-015-V1. ACC: MOL.20010718.0254. 
Bish, D.L. 1989. Evaluation of Past and Future Alterations in Tuff at Yucca 101194 
Mountain, Nevada, Based on the Clay Mineralogy of Drill Cores USW G-1, G-2, and 
G-3. LA-10667-MS. Los Alamos, New Mexico: Los Alamos National Laboratory. 
ACC: NNA.19890126.0207. 
Bish, D.L. and Chipera, S.J. 1989. Revised Mineralogic Summary of Yucca Mountain, 101195 
Nevada. LA-11497-MS. Los Alamos, New Mexico: Los Alamos National 
Laboratory. ACC: NNA.19891019.0029. 
Bish, D.L. and Vaniman, D.T. 1985. Mineralogic Summary of Yucca Mountain, 101196 
Nevada. LA-10543-MS. Los Alamos, New Mexico: Los Alamos National 
Laboratory. ACC: MOL.19950412.0041. 
Brodsky, N.S. 2003. Uncertainty Estimates - Measurement Error Estimates for 164584 
Porosity, Thermal Conductivity, Bulk Density. Fax from N.S. Brodsky (SNL) to N. 
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Broxton, D. 1990. Hurricane Mesa, Absaroka Mts., Wyo., Lathrop Wells Section, 169640 
Nev., Sampling of Wt Holes Yucca Mt., Nev., Calico Hills Section in Paintbursh, Cny. 
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Company. ACC: DOC.20040128.0001. 
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ANL-NBS-HS-000042, Rev. 00. Las Vegas, Nevada: Bechtel SAIC Company. 

BSC (Bechtel SAIC Company) 2004. Drift Degradation Analysis. 
166107 
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MDL-NBS-GS-000002 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20040827.0008. 
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MDL-NBS-GS-000003, Rev. 01. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20040908.0006. 
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REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: ENG.20040721.0007. 
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Company. 
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BSC (Bechtel SAIC Company) 2004. Thermal Conductivity of Non-Repository 170033
Lithostratigraphic Layers. MDL-NBS-GS-000006, Rev. 01. Las Vegas, Nevada: 
Bechtel SAIC Company. 
BSC (Bechtel SAIC Company) 2004. Thermal Conductivity of the Potential 169854
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SAIC Company. 
Buesch, D.C.; Spengler, R.W.; Moyer, T.C.; and Geslin, J.K. 1996. Proposed 100106
Stratigraphic Nomenclature and Macroscopic Identification of Lithostratigraphic 
Units of the Paintbrush Group Exposed at Yucca Mountain, Nevada. Open-File Report 
94-469. Denver, Colorado: U.S. Geological Survey. ACC: MOL.19970205.0061. 
Burns & McDonnell Engineering. 1959. Report on Development of Wells J-11 & J12, 169662
Jackass Flats, Nye County, Nevada. Kansas City, Missouri: Burns & McDonnell 
Engineering. ACC: MOL.19961209.0118. 
Byers, F. 1982. Trip to NTS, February 19-22, RE: Holes H-3, Yucca Mountain, 169663
NTS. Office memorandum from F. Byers (LANL) to ESS-1 and -2 NNWSI Group, 
February 23, 1982, TWS-ESS-2-3/82-90. ACC: NNA.19900418.0142. 
Byers, F.M. 1983. Thin-Section Data Microprobe Data, UE25-1, Yucca Mtn. Nevada 169692
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Caporuscio, F.A. 1986. Scientific Notebook. Scientific Notebook Number S-4077. 169644 
ACC: NNA.19900404.0106. 
Chipera, S.J. 1986. Scientific Notebook. Scientific Notebook Number S-9075. 169680 
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Chipera, S.J. 1989. Scientific Notebook. Scientific Notebook Number S-10079. 169683 
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Chipera, S.J. and Bish, D.L. 1988. Mineralogy of Drill Hole UE-25p#1 at Yucca 105080 
Mountain, Nevada. LA-11292-MS. Los Alamos, New Mexico: Los Alamos National 
Laboratory. ACC: NNA.19880607.0036. 
CRWMS M&O 1996. Determining Porosity, Balanced Water Content, and Other 100632 
Rock Properties from Geophysical Logs for the Modern Borehole Data Set at Yucca 
Mountain, Nevada, June 1996. BAAA00000-01717-0200-00013 REV 00. Las Vegas, 
Nevada: CRWMS M&O. ACC: MOL.19970210.0171. 
CRWMS M&O 1999. Combined Porosity from Geophysical Logs. BAA000000-109022
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User's Guide. 2nd Edition. New York, New York: Oxford University Press. 
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Flint, L.E. 1998. Characterization of Hydrogeologic Units Using Matrix Properties, 100033
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Horton, D.G. 1990. U.S. Department of Energy (DOE) Office of Civilian 169954
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Laboratories (Los Alamos) Quality Assurance (QA) Program. Letter from D.G. 
Horton (DOE) to D.E. Shelor, December 21, 1990, OQA:NAV-1413, with enclosures. 
ACC: HQO.19910107.0034; HQO.19910107.0036. 

Journel, A.G. 1983. Nonparametric Estimation of Spatial Distributions. 
102897 
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LANL (Los Alamos National Laboratory) 1992. SMF Specimen Custody Receipt, 169718
Shipment ID 0052, Shipping Date: January 3, 1992. Los Alamos, New Mexico: 
Los Alamos National Laboratory. ACC: MOL.19980330.0458. 
LANL (Los Alamos National Laboratory) 1997. SMF Speciman Removal Logs for 169667
SRR#: 01000038 and SRR#: 01000039. [Los Alamos, New Mexico: Los Alamos 
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Levy, S.S. 1984. Petrology of Samples from Drill Holes USW H-3, H-4, and H-5, 101392
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National Laboratory. ACC: MOL.19970729.0322. 
Levy, S.S. 1991. Mineralogic Alteration History and Paleohydrology at Yucca 100053
Mountain, Nevada. High Level Radioactive Waste Management, Proceedings of the 
Second Annual International Conference, Las Vegas, Nevada, April 28-May 3, 1991. 
1, 477-485. La Grange Park, Illinois: American Nuclear Society. TIC: 204272. 
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Simulation of Coregionalization. Ph.D. dissertation. Stanford, California: Stanford 
University. TIC: 226080. 
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A.L.; and Lung, R.C. 1999. Geology of the ECRB Cross Drift - Exploratory Studies 
Facility, Yucca Mountain Project, Yucca Mountain, Nevada. 
[Deliverable SPG42GM3]. Denver, Colorado: U.S. Geological Survey. 
ACC: MOL.20000324.0614. 
Moyer, T.C. and Geslin, J.K. 1995. Lithostratigraphy of the Calico Hills Formation 101269 
and Prow Pass Tuff (Crater Flat Group) at Yucca Mountain, Nevada. Open-File 
Report 94-460. Denver, Colorado: U.S. Geological Survey. 
ACC: MOL.19941208.0003. 
Nelson, P.H. 1996. Computation of Porosity and Water Content from Geophysical 101270 
Logs, Yucca Mountain, Nevada. Open-File Report 96-078. Denver, Colorado: 
U.S. Geological Survey. ACC: MOL.19980529.0444. 

NRC (U.S. Nuclear Regulatory Commission) 2003. Yucca Mountain Review Plan, 163274
Final Report. NUREG-1804, Rev. 2. Washington, D.C.: U.S. Nuclear Regulatory 
Commission, Office of Nuclear Material Safety and Safeguards. TIC: 254568. 
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1999). Scientific Notebook SNL-SCI-011. ACC: MOL.19990623.0025. 
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Derivative Properties for Fiscal Year 1998. Scientific Notebook SNL-SCI-006. 
ACC: MOL.19981027.0187. 
Rautman, C.A. and Engstrom, D.A. 1996. Geology of the USW SD-12 Drill Hole 100642
Yucca Mountain, Nevada. SAND96-1368. Albuquerque, New Mexico: Sandia 
National Laboratories. ACC: MOL.19970613.0101. 
Rautman, C.A. and Engstrom, D.A. 1996. Geology of the USW SD-7 Drill Hole 101008
Yucca Mountain, Nevada. SAND96-1474. Albuquerque, New Mexico: Sandia 
National Laboratories. ACC: MOL.19971218.0442. 
Rautman, C.A. and McKenna, S.A. 1997. Three-Dimensional Hydrological and 100643
Thermal Property Models of Yucca Mountain, Nevada. SAND97-1730. Albuquerque, 
New Mexico: Sandia National Laboratories. ACC: MOL.19980311.0317. 
Sawyer, D.A.; Fleck, R.J.; Lanphere, M.A.; Warren, R.G.; Broxton, D.E.; and Hudson, 100075
M.R. 1994. Episodic Caldera Volcanism in the Miocene Southwestern Nevada 
Volcanic Field: Revised Stratigraphic Framework, {superscript 40}Ar/{superscript 
39}Ar Geochronology, and Implications for Magmatism and Extension. Geological 
Society of America Bulletin, 106, (10), 1304-1318. Boulder, Colorado: Geological 
Society of America. TIC: 222523. 
Schlumberger 1972. Principles. Schlumberger Log Interpretation. Volume I. 129068 
New York, New York: Schlumberger Limited. TIC: 240326. 
SNL (Sandia National Laboratory) 2002. Software Management Report, Etype Version 171419 
2.01. Software Management Report Number: 10731-SMR-2.01-00. Albuquerque, 
New Mexico: Sandia National Laboratory. ACC: MOL.20020603.0347. 
Steinborn, T.L. 2002. Data Qualification Report: Mineralogy Data for Use on the 160702
Yucca Mountain Project. TDR-NBS-HS-000005 REV 00. Las Vegas, Nevada: 
Bechtel SAIC Company. ACC: MOL.20020807.0442. 
USGS (U.S. Geological Survey) [1995]. Lithologic Log of Drill Hole USW WT-1. 169694
[Denver, Colorado]: U.S. Geological Survey. ACC: MOL.19960110.0344. 
Vaniman, D. 1983. Volcanic Hazards Studies Book 2. Notebook Unique 169665
Identification Number: TWS-G6-8/79-50. ACC: MOL.20020826.0192. 

Vaniman, D.; Bish, D.; Broxton, D.; Byers, F.; Heiken, G.; Carlos, B.; Semarge, E.; 101363 
Caporuscio, F.; and Gooley, R. 1984. Variations in Authigenic Mineralogy and 
Sorptive Zeolite Abundance at Yucca Mountain, Nevada, Based on Studies of Drill 
Cores USW GU-3 and G-3. LA-9707-MS. Los Alamos, New Mexico: Los Alamos 
National Laboratory. ACC: NNA.19870519.0043. 
Whitfield, M.S., Jr.; Thordarson, W.; and Eshom, E.P. 1984. Geohydrologic and 101366 
Drill-Hole Data for Test Well USW H-4, Yucca Mountain, Nye County, Nevada. Open-
File Report 84-449. Denver, Colorado: U.S. Geological Survey. ACC: 
NNA.19870407.0317. 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic 156605
Repository at Yucca Mountain, Nevada. Readily available. 
AP-2.22Q, Rev. 1, ICN 1. Classification Analyses and Maintenance of the Q-List. 
Washington, D.C.: U.S. Department of Energy, Office of Civilian Radioactive Waste 
Management. ACC: DOC.20040714.0002. 
AP-2.27Q, Rev. 1, ICN 4. Planning for Science Activities. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: DOC.20040610.0006. 
AP-SI.1Q, Rev. 0, ICN 0. Software Configuration Management. Washington, D.C.: 
U.S. Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: MOL.19990505.0125. 
AP-SIII.2Q, Rev. 1, ICN 2. Qualification of Unqualified Data. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
DOC.20040127.0008. 
AP-SIII.10Q, Rev. 2, ICN 5. Models. Washington, D.C.: U.S. Department of Energy, 
Office of Civilian Radioactive Waste Management. ACC: DOC.20040615.0003. 
DOE (U.S. Department of Energy) 2004. Quality Assurance Requirements and 171539 
Description. DOE/RW-0333P, Rev. 16. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. 
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QAIP 19-1, Rev. 4. Software Quality Assurance Requirements. Albuquerque, 129329 
New Mexico: Sandia National Laboratories. ACC: MOL.19981103.0547. 
QAIP 20-2, Rev. 02. Scientific Notebooks. Albuquerque, New Mexico: Sandia 109474 
National Laboratories. ACC: MOL.19990224.0508. 

9.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
GS000308314211.002. Stratigraphic Contacts for Boreholes UE-25 A#1, UE-25 A#4, 158000
USW G-1, USW G-2, USW H-5, USW SD-9, USW SD-12 and USW VH-2. Submittal 
date: 04/28/2000. 
GS021008314224.002. Lithophysal Data Study from the Tptpll in the ECRB from 161910
Stations 14+44 to 23+26. Submittal date: 01/28/2003. 
GS960708312132.002. Porosity, Water Content, Mineralogy and Other Data Derived 113584
from Geophysical Logs and Cores for 26 Boreholes. Submittal date: 07/09/1996. 
GS980708312242.010. Physical Properties of Borehole Core Samples, and Water 106752
Potential Measurements Using the Filter Paper Technique, for Borehole Samples from 
USW WT-24. Submittal date: 07/27/1998. 
GS980808312242.014. Physical Properties of Borehole Core Samples and Water 106748
Potential Measurements Using the Filter Paper Technique for Borehole Samples from 
USW SD-6. Submittal date: 08/11/1998. 
LA000000000086.002. Mineralogic Variation in Drill Core UE-25 UZ#16 Yucca 107144
Mountain, Nevada. Submittal date: 03/28/1995. 
LA9908JC831321.001. Mineralogic Model "MM3.0" Version 3.0. Submittal date: 113495
08/16/1999. 
LA9910JC831321.001. Supplementary Mineralogical Data for Mineralogic Model 113496
3.0. Submittal date: 10/29/1999. 
LA9910DB831321.001. Mineralogic Variation in Drill Holes. Submittal date: 113568 
11/18/1999. 
LADB831321AN98.002. Revised Mineralogic Summary of Yucca Mountain, Nevada. 109003 
Submittal date: 05/26/1998. 
LADV831321AQ97.001. Mineralogic Variation in Drill Holes. Submittal date: 107142 
05/28/1997. 
LADV831321AQ97.007. Geotechnical Data Report: Hazardous Minerals. Submittal 113499 
date: 01/27/1998. 
LAJC831321AQ98.005. Quantitative XRD Results for Drill Core USW SD-7, USW 109004 
SD-9, USW SD-12 and UE-25 UZ#16. Submittal date: 10/27/1998. 
MO0004QGFMPICK.000. Lithostratigraphic Contacts from 152554 
MO9811MWDGFM03.000 to be Qualified Under the Data Qualification Plan, TDP-
NBS-GS-000001. Submittal date: 04/04/2000. 

MO0010CPORGLOG.002. Calculated Porosity from Geophysical Logs Data from 155229
"Old 40" Boreholes. Submittal date: 10/16/2000. 
MO0010CPORGLOG.003. Calculated Porosity Values at Depth Derived from 155959
Qualified Geophysical Log Data from Modern Boreholes. Submittal date: 10/16/2000. 
MO0012MWDGFM02.002. Geologic Framework Model (GFM2000). Submittal 153777
date: 12/18/2000. 
MO0109HYMXPROP.001. Matrix Hydrologic Properties Data. Submittal date: 155989
09/17/2001. MO0109HYMXPROP.001. Matrix Hydrologic Properties Data. 
Submittal date: 09/17/2001. 
MO0101XRDMINAB.001. XRD Analyses of Drill Core from Boreholes UE-25B#1, 163796
USW G-1, USW G-3, USW GU-3, and USW G-4. Submittal date: 01/26/2001. 
MO0101XRDMINAB.001. XRD Analyses of Drill Core from Boreholes UE-25B#1, 
USW G-1, USW G-3, USW GU-3, and USW G-4. Submittal date: 01/26/2001. 
MO0101XRDDRILC.001. XRD Analyses of Drill Core from Boreholes UE-25 J-12, 169517
USW WT-1, USW H-3, USW H-4, USW WT-2, UE-25 P#1, AND USW H-5. 
Submittal date: 01/26/2001. MO0101XRDDRILC.001. XRD Analyses of Drill Core 
from Boreholes UE-25 J-12, USW WT-1, USW H-3, USW H-4, USW WT-2, UE-25 
P#1, AND USW H-. Submittal date: 01/26/2001. 
MO9510RIB00002.004. RIB Item: Stratigraphic Characteristics: Geologic/Lithologic 103801
Stratigraphy. Submittal date: 06/26/1996. MO9510RIB00002.004. RIB Item: 
Stratigraphic Characteristics: Geologic/Lithologic Stratigraphy. Submittal date: 
06/26/1996. 
MO9804MWDGFM03.001. An Update to GFM 3.0; Corrected Horizon Grids for 109050
Four Fault Blocks. Submittal date: 04/14/1998. MO9804MWDGFM03.001. An 
Update to GFM 3.0; Corrected Horizon Grids for Four Fault Blocks. Submittal date: 
04/14/1998. 
MO9901MWDGFM31.000. Geologic Framework Model. Submittal date: 103769
01/06/1999. MO9901MWDGFM31.000. Geologic Framework Model. Submittal 
date: 01/06/1999. 
MO9910MWDISMRP.002. ISM3.1 Rock Properties Models. Submittal date: 145731
10/06/1999. MO9910MWDISMRP.002. ISM3.1 Rock Properties Models. Submittal 
date: 10/06/1999. 
SNF40060198001.001. Unsaturated Zone Lithostratigraphic Contacts in Borehole 107239
USW WT-24. Submittal date: 10/15/1998. SNF40060198001.001. Unsaturated Zone
Lithostratigraphic Contacts in Borehole USW WT-24. Submittal date: 10/15/1998. 

SNF40060298001.001. Unsaturated Zone Lithostratigraphic Contacts in Borehole 107372
USW SD-6. Submittal date: 10/15/1998. SNF40060298001.001. Unsaturated Zone 
Lithostratigraphic Contacts in Borehole USW SD-6. Submittal Date: 10/15/1998. 
SNL01A05059301.005. Laboratory Thermal Conductivity Data for Boreholes UE25 109002
NRG-4, NRG-5; USW NRG-6 and NRG-7/7A. Submittal date: 02/07/1996. 
SNL01A05059301.005. Laboratory Thermal Conductivity Data for Boreholes UE25 
NRG-4, NRG-5; USW NRG-6 and NRG-7/7A. Submittal Date: 02/07/1996. 
SNL01A05059301.007. Calculated Porosities for Thermal Conductivity Specimens 108980
from Boreholes UE25 NRG-4, UE25 NRG-5, USW NRG-6, and USW NRG-7/7a. 
Submittal date: 10/14/1998. SNL01A05059301.007. Calculated Porosities for 
Thermal Conductivity Specimens from Boreholes UE25 NRG-4, UE25 NRG-5, USW 
NRG-6, and USW NRG-7/7a. Submittal Date: 10/14/1998. 
SN0209L01A1202.001. Thermal Conductivity Laboratory Data (Including Densities 163601
and Porosities) Generated in FY02 on the Topopah Springs Lower Lithophysal (Tptpll) 
Lithostratigraphic Unit. Submittal date: 09/23/2002. SN0209L01A1202.001. 
Thermal Conductivity Laboratory Data (Including Densities and Porosities) Generated 
in FY02 on the Topopah Springs Lower Lithophysal (Tptpll) Lithostratigraphic Unit. 
Submittal date: 09/23/2002. 
9.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
SN0004T0501399.002. Correlation of RH (Relative Humidity) Porosity and Bulk 155046 
Density. Submittal date: 04/13/2000. 
SN0004T0501399.003. Statistical Summary of Porosity Data. Submittal date: 155045 
04/13/2000. 
SN9910T0501399.001. Three-Dimensional Rock Property Models for FY99 129717 
(RPM3.1). Submittal date: 10/18/1999. 
9.5 SOFTWARE CODES 
BUD. Routine. 113651 
COORDS. Routine. 113704 
COREGPC. Routine. 113671 
EARTHVISION V4.0 Dynamic Graphics. 1997. STN: 30035 V4.0. 113625 
ETYPE V2.01, SNL (Sandia National Laboratories) PC Windows 2000 Server. 10731-159417 
2.01.00. 
GSLIB V1.4 MBACKTRV1.2. V1.2. 113642 

GSLIB V1.4 MNSCOREV1.201. STN: 10109-1.4MNSCOREV1.201-00. 113595 
GSLIB V1.4 Module BICALIB V2.0. 114227 
GSLIB V1.4 Module TRANS V1.3. 114225 
GSLIB V1.4 MSGSIMV1.40. STN: 10110-1.4MSGSIMV1.40-00. 113569 
GSLIB V2.0 MIK3DV2.0. STN: 10122-2.0MIK3DV2.0-00. 113587 
MATCH V 12/05/98. V.12/05/98. 113664 
MATCHUP Version 12/05/98. STN: 10196-12/05/98-00. 113660 
STRATC4. . 113708 
TWOFOOT 6/25/98 113653 
VITROPHYRE 9/10/98 113694 
ZEOLITE5 9/10/98 113682 

APPENDIX A 
SIGMA PLOT TRANSFORM STRATC4 


A1. SOFTWARE ROUTINE IDENTIFICATION 
Software Name and Version Number: stratc4, ver. 4: 
NOTE: The software documentation is for information only and to provide completeness and 
traceability. 
Version number was not assigned originally, but was added for documentation to be 
included in the Rock Properties Model Analysis Model Report. However, the digit 
4 in the routine name was intended to indicate that this is the fourth iteration of a 
software routine to perform a similar function. 
SRR Document Identification Number: none. 
SRR Media Number: none. 
A2. DESCRIPTION AND TESTING 
Documentation that the software routine or macro provides correct results for a specified range 
of input parameters: 
 Description and equations of mathematical models, algorithms, and numerical solution 
techniques, as applicable: 
The equation that is implemented in software routine stratc4 to convert drill hole 
measured depths to stratigraphic coordinates, more specifically to stratigraphic depths 
(StratDepth), is as follows (equation 6.4-2 in Section 6.4.3.2, of this report): 
StratDepth = h SampleDept - UnitTop 
. cknessNominalThi 
UnitBottom - UnitTop 
where SampleDepth is the depth of the relevant sample measurement in a given drill 
hole, UnitTop and UnitBottom are the measured or projected top and bottom contacts of 
relevant model unit at the drill hole location used to determine the thickness of the unit, 
and NominalThickness is the appropriate unit-specific scaling constant (Table 6.4-1 of 
this report). The conversion produces a stratigraphic depth such that the very top of 
the geologic unit in question is at a depth of zero, the very bottom of the unit is at a 
depth equal to the nominal stratigraphic thickness of the unit, and all depths in 
between are scaled proportionately to the nominal thickness. Stratigraphic coordinates 
are dimensionless. 
 Description of software routine including the execution environment: 
Software routine stratc4 is a transform implemented within the industry-standard, 
commercial statistical and graphics software package, Sigma Plot (SPSS, 
Inc.; 233 South Wacker Drive, 11th Floor; Chicago, Illinois 60606-6307). The routine 
is essentially a macro (however, see next paragraph). Sigma Plot transform syntax 
does not appear to be version specific, but this routine has been used for the current 

application within Sigma Plot, version 5.00, running under Windows NT, 
version 4. Use of the transform is intimately associated with the spreadsheet mechanics 
of Sigma Plot. 
Note that a transform in Sigma Plot parlance is different than a macro. A Sigma 
Plot macro consists of a recording of a sequence of mouse-based actions for creating or 
modifying a graph. In contrast, a transform performs one or more mathematical 
operations on a set of input data and provides essentially what amounts to an internal 
programming language for performing those operations. 
The Sigma Plot spreadsheets designed for use with software routine stratc4 are 
intended to contain various sets of core-based and geophysics-based rock property 
measurements, and to present those data values graphically as drill hole profiles 
displaying the selected measurements as a function of depth. Accordingly, the true, 
measured depths associated with each set of property values are arranged in a columnar 
manner, with four blank columns to the right of the true-depth column (to be filled in 
with stratigraphic depths); property values are typically arranged to the right of the 
four blank columns as a logical grouping. Multiple sets of such depth and property data 
may be present in a single spreadsheet. Additionally, a column of top and bottom depth 
values corresponding to the top and bottom of each geologic unit of interest is provided 
elsewhere in the spreadsheet. The numeric identifiers of the true-depth and tops 
columns are required as manual input to the software routine (involves editing the 
transform listing within Sigma Plot). 
The transform is invoked according to Sigma Plot conventions, whereupon the 
appropriate unit tops and bottoms are read from the spreadsheet, and the 
coordinate-conversion equation is computed repeatedly for each depth value present in 
the specified true-depth column, with the results being inserted into the appropriate 
output columns (presumably the four blank columns set up for this purpose). 
 Description of test cases: 
Any arbitrary set of depth values can be used to test the stratigraphic depth 
calculations performed by software routine stratc4. The only requirement is that some 
number of true-depth values be contained within the top and bottom depths of the 
geologic unit being considered. As an alternative, the following data set, which 
consists of the depths associated with selected laboratory core property measurements 
taken from drill hole USW SD-7 (the property data themselves are irrelevant and are not 
modified by the transform), may be used to validate the software routine. Note that 
within Sigma Plot, these data entries are associated with specific cells identified by row 
and column numbers. 

The column headed unit designation is not used by the transform, and it is included 
only for reader reference. 
Tops Depth Unit Designation True-Depth 
316.0000 top PTn 302.8000 
316.0000 306.1000 
308.6000 
384.3000 base PTn 312.1000 
384.3000 top TSw 320.9000 
323.7000 
1308.0000 base TSw 330.1000 
1308.0000 top CH 332.9000 
338.9000 
1621.5000 base CH 341.5000 
1621.5000 top PP 368.0000 
369.0000 
2183.9000 base Prow 371.9000 
2183.9000 top Bullfrog 386.3000 
389.7000 
392.7000 
395.8000 
399.1000 
401.4000 
404.6000 
 Description of test results: 
The following lines are copied from the Sigma Plot spreadsheet in which the 
stratigraphic coordinate conversion was computed. In the original, each entry is 
associated with a particular cell, identified by row and column numbers. 
True Depth PTn StratD TSw StratD CHn StratD Tcp StratD 
302.8000 
306.1000 
308.6000 
312.1000 
320.9000 14.3485 
323.7000 22.5476 
330.1000 41.2884 
332.9000 49.4876 
-38.6530 -88.2321 -1282.5518 -937.9090 
-28.9898 -84.6595 -1278.3413 -935.5619 
-21.6691 -81.9530 -1275.1515 -933.7838 
-11.4202 -78.1639 -1270.6858 -931.2945 
-68.6370 -1259.4577 -925.0356 
-65.6057 -1255.8852 -923.0441 
-58.6771 -1247.7193 -918.4922 
-55.6458 -1244.1467 -916.5007 

True Depth PTn StratD TSw StratD CHn StratD Tcp StratD 
338.9000 67.0571 
341.5000 74.6706 
368.0000 152.2694 
369.0000 155.1977 
371.9000 163.6896 
386.3000 
2.1652 
389.7000 
5.8461 
392.7000 
9.0939 
395.8000 
12.4499 
399.1000 
16.0225 
401.4000 
18.5125 
404.6000 
21.9768 
-49.1502 -1236.4912 -912.2333 
-46.3354 -1233.1738 -910.3841 
-17.6464 -1199.3620 -891.5363 
-16.5638 -1198.0861 -890.8250 
-13.4243 -1194.3860 -888.7624 
205.8565 -1176.0128 -878.5206 
215.8126 -1171.6746 -876.1024 
224.5974 -1167.8469 -873.9687 
233.6750 -1163.8915 -871.7639 
243.3382 -1159.6810 -869.4168 
250.0732 -1156.7464 -867.7809 
259.4436 -1152.6635 -865.5050 
Several items are of note. First, the input data (first listing, above) represent only a very 
small portion of the relevant drill hole data set. Specifically, the true depths include 
only those associated with the PTn geologic unit, plus a few extra depths both above and 
below for illustrative purposes. As expected, true depths higher in the hole than the top 
of the PTn unit (given above as 316 ft), are converted to meaningless stratigraphic 
depths (PTn StratD) less than zero. Second, depths below the bottom of the PTn unit 
(given above as 384.3 ft) are converted to equally meaningless stratigraphic depths in 
excess of the nominal stratigraphic thickness of the PTn unit, which is 
200.0 (dimensionless). Third, using similar logic, almost all stratigraphic depths for the 
TSw geologic unit (TSw StratD) are negative (those above the top of the TSw unit at 
384.3 ft), as are all of the indicated stratigraphic depths for the CHn unit and Tcp unit 
(CHn StratD and Tcp StratD), as none of the measured depths of with this 
illustrative data set are associated with those deeper units. Finally, the lowermost valid 
PTn stratigraphic depth (at a true depth of 371.9 ft) is immediately succeeded by the first 
valid TSw stratigraphic depth (at a true depth of 386.3 ft). Shaded values in the listing 
indicate numbers that are meaningless (though arithmetically correct) for one or more 
of the reasons discussed above. 
A manual calculation, conducted by calculator (a Hewlett-Packard HP-11C; Hewlett-
Packard Co.; 3000 Hanover Street; Palo Alto, CA 94304), for the first valid PTn 
true depth value (at 320.9 ft) is as follows: 
9.320 - 0.316 
StratDepth = 0.200 . = 34846266.14 
3.384 - 0.316 

which has been rounded up to 14.3485 by the display-format specified in the results 
presented above (set within Sigma Plot). 
 
Range of input parameter values for which the results were verified: 
There are no input parameter values specified by the user, other than the column 
identifiers specifying where to look for the unit tops and bottoms and for the true depths 
that are to be converted. In terms of input data values, any real number recognized by 
Sigma Plot is a mathematically valid entry for both the unit contacts (top and bottom) 
and for the true depths. Whether or not specific values are legitimate values for a 
particular drill hole or sample depth is another issue (discussion above). The user is 
responsible for providing geologically meaningful input values. 
 
Identification of any limitations on software routine applications or validity: 
There are no particular limitations to the software routine, per se. Any valid real number 
recognized by Sigma Plot is acceptable as input. However, the routine does not limit the 
calculation of the stratigraphic depth to the geologic unit of interest. Accordingly, the 
routine may generate stratigraphic depths that are outside the legitimate range of zero 
through NominalThickness. Such values, although representing arithmetically correct 
results, are meaningless. The user is responsible for identifying and dealing properly 
with such values in later applications. 
A3. SUPPORTING INFORMATION 
 Directory listing of executable and data files: 
Because software routine stratc4 is a macro designed to operate inside another 
software package, there is no executable, per se. The Sigma Plot transform capability 
operates in an interpreter mode, compiling and executing each line of the transform at 
runtime. There are also no data files, per se. All input data is contained within the 
composite Sigma Plot spreadsheet and graphics file. 
Computer listing of source code, if available. 
The following listing of the transform code has been annotated (in a different font) to 
illustrate some salient features and required user-input values. 
jsv5D;Macro to compute stratigraphic coordinates; version 4 
; note: uses contacts of t/m units in spreadsheet; bottom of one unit can be NONcoincident with top of next 
ctop = 1 ; column with tops <--User Input 
dp = 4 ; true depth column <--User Input 
nc1 = dp + 1 ; column in which to put StratZ for PTnnc2 = dp + 2 ; column in which to put StratZ for TSwnc3 = dp + 3 ; column in which to put StratZ for CHnnc4 = dp + 4 ; column in which to put StratZ for PP 

col(nc1 ) = ( col( dp ) - top1 ) / thk1 * nt1 
top1 = cell( ctop,2 )
bot1 = cell( ctop,4 )
nt1 = 200.0 
; PTn parameters 
<--NominalThickness for PTn 
top2 = cell( ctop,5 )
bot2 = cell( ctop,7 )
nt2 = 1000.0 
; TSw parameters 
<--NominalThickness for TSw 
top3 = cell( ctop,8 )
bot3 = cell( ctop,10 )
nt3 = 400.0 
; CHn parameters 
<--NominalThickness for CHn 
top4 = cell( ctop,11)
bot4 = cell( ctop,13)
nt4 = 400 
;PP parameters 
<--NominalThickness for Tcp 
thk1 = bot1 - top1
thk2 = bot2 - top2
col(nc2 ) = ( col( dp ) - top2 ) / thk2 * nt2 
thk3 = bot3 - top3
col(nc3 ) = ( col( dp ) - top3 ) / thk3 * nt3 
thk4 = bot4 - top4
col(nc4 ) = (col( dp ) - top4 ) / thk4 * nt4 
Computer listing of test data input and output, identifying software routine name and version number. 
A printout (taken from an Acrobat portable-document-format [.pdf] file) of the relevant 
parts of the test Sigma Plot spreadsheet is reproduced below in Figure A-1. No software 
names or version numbers are associated with the internal appearance of the 
spreadsheet. 

Source: Reproduced from a .pdf file. 
NOTE: Columns are numbered sequentially from the left (see col. 3), but alphanumeric titles may be entered for 
user convenience. 
Figure A-1. Printed Spreadsheet Cells from Sigma Plot File Containing the Test Case Data and Output 

INTENTIONALLY LEFT BLANK 

APPENDIX B 
GSLIB ROUTINE TRANS 


NOTE: The software documentation is for information only and to provide completeness and 
traceability. 
Software routine TRANS, Version 1.3, is used directly from the geostatistical software library, 
GSLIB (Deutsch and Journel 1992 [DIRS 100567], pp. 213 to 214). The routine is intended to 
convert any particular univariate distribution of values to match that of a different reference 
distribution. For the rock properties modeling exercise, TRANS was used to transform 
coregionalized, standard-normal (=0, s2=1) models to match the target statistics of the desired 
secondary property in order to create the full-field derivative-property models. 
Source Code Listing 
program trans 
C%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
%%%%%%%%%%%%%%%% 
C % 
C Copyright (C) 1992 Stanford Center for Reservoir Forecasting. All % 
C rights reserved. Distributed with: C.V. Deutsch and A.G. Journel. % 
C GSLIB: Geostatistical Software Library and Users Guide, Oxford % 
C University Press, New York, 1992. % 
C % 
C The programs in GSLIB are distributed in the hope that they will be % 
C useful, but WITHOUT ANY WARRANTY. No author or distributor accepts % 
C responsibility to anyone for the consequences of using them or for % 
C whether they serve any particular purpose or work at all, unless he % 
C says so in writing. Everyone is granted permission to copy, modify % 
C and redistribute the programs in GSLIB, but only under the condition % 
C that this notice and the above copyright notice remain intact. % 
C % 
C%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
%%%%%%%%%%%%%%%% 
c-----------------------------------------------------------------------
c 
c Univariate Transformation 
c ************************* 
c 
c Reads in a reference distribution and a number of other distributions 
c and then transforms the values in each of the second distributions 
c such that their histograms match that of the reference distribution. 
c 
c 
c INPUT/OUTPUT Parameters: 
c 
c distin the input file with the reference distribution 
c ivr,iwt parameters to read in reference distribution 
c tmin,tmax trimming limits 
c datafl the input file with the data to be transformed 
c ivrd,iwtd parameters to read in reference distribution 
c tmin,tmax trimming limits 
c nxyz,nsim number of points to transform at a time and the 
c number of times to expect nxyz in the file 
c outfl the output file for transformed data 
c zmin,zmax minimum and maximum data values 

c ltail,ltpar option to handle values in lower tail 
c utail,utpar option to handle values in upper tail 
c 
c 
c 
c Original: C.V. Deutsch Date: May 1991 
c----------------------------------------------------------------------- 
parameter(MAXREF=25000, MAXDAT=100000, MV=20, EPSLON=1.0e-12, 
+ 
VERSION=1.300) 
implicit real*8 (a-h,o-z) 
character distin*40,datafl*40,outfl*40,str*40 
real*8 rcdf(MAXREF),rvr(MAXREF),dcdf(MAXDAT),dvr(MAXDAT), 
+ 
indx(MAXDAT),var(MV),ltpar,utpar 
integer ltail,utail 
logical testfl 
data lin/1/,lout/2/
c
c Note VERSION number before anything else:
c 
write(*,9999) VERSION 
9999 format(/ TRANS Version: ,f5.3/) 
c 
c Get the name of the parameter file - try the default name if no input: 
c 
write(*,*) Which parameter file do you want to use? 
read (*,(a40)) str 
if(str(1:1).eq. )str=trans.par  
inquire(file=str,exist=testfl) 
if(.not.testfl) then 
write(*,*) ERROR - the parameter file does not exist, 
write(*,*)  check for the file and try again  
stop 
endif 
open(lin,file=str,status=OLD) 
c 
c Find Start of Parameters: 
c 
1 read(lin,(a4),end=97) str(1:4) 
if(str(1:4).ne.STAR) go to 1 
c 
c Read Input Parameters: 
c 
read(lin,(a40),err=97) distin 
write(*,*)  reference distribution: ,distin 
read(lin,*,err=97) ivr,iwt 
write(*,*)  columns: ,ivr,iwt 
read(lin,*,err=97) tmin,tmax 
write(*,*)  trimming limits: ,tmin,tmax 
read(lin,(a40),err=97) datafl 
write(*,*)  data file: ,datafl 
read(lin,*,err=97) ivrd,iwtd 
write(*,*)  columns: ,ivrd,iwtd 
read(lin,*,err=97) tmind,tmaxd

write(*,*)  trimming limits: ,tmind,tmaxd 
read(lin,*,err=97) nxyz,nsim 
write(*,*)  number to transform: ,nxyz,nsim 
read(lin,(a40),err=97) outfl 
write(*,*)  output file: ,outfl 
read(lin,*,err=97) zmin,zmax 
write(*,*)  data limits: ,zmin,zmax 
read(lin,*,err=97) ltail,ltpar 
write(*,*)  lower tail option: ,ltail,ltpar 
read(lin,*,err=97) utail,utpar 
write(*,*)  upper tail option: ,utail,utpar 
close(lin)
c
c Check for error situation:
c 
if(ltail.ne.1.and.ltail.ne.2) then 
write(*,*) ERROR invalid lower tail option ,ltail 
write(*,*)  only allow 1 or 2 - see manual  
stop 
endif 
if(utail.ne.1.and.utail.ne.2.and.utail.ne.4) then 
write(*,*) ERROR invalid upper tail option ,ltail 
write(*,*)  only allow 1,2 or 4 - see manual  
stop 
endif 
if(utail.eq.4.and.utpar.lt.1.0) then 
write(*,*) ERROR invalid power for hyperbolic tail,utpar 
write(*,*)  must be greater than 1.0! 
stop 
endif 
c 
c Read in the reference distribution: 
c 
inquire(file=distin,exist=testfl) 
if(.not.testfl) then 
write(*,*) ERROR: No reference distribution file 
stop 
endif 
open(lin,file=distin) 
c 
c Proceed with reading in distribution: 
c 
read(lin,(a),err=98) str 
read(lin,*,err=98) nvari 
do 2 i=1,nvari 
2 read(lin,(),err=98) 
c 
c Read as much data as possible: 
c 
ncut = 0 
nt = 0 
tcdf = 0 
3 read(lin,*,end=4,err=98) (var(j),j=1,nvari) 
c 

c Trim this data? 
c 
if(var(ivr).lt.tmin.or.var(ivr).ge.tmax) then 
nt = nt + 1 
go to 3 
endif 
if(iwt.ge.1) then 
if(var(iwt).le.EPSLON) then 
nt = nt + 1 
go to 3 
endif 
endif 
c 
c Accept this data: 
c 
ncut = ncut + 1 
if(ncut.gt.MAXREF) then 
write(*,*) ERROR: exceeded available storage for reference 
write(*,*)  have ,MAXREF, available 
stop 
endif 
rvr(ncut) = var(ivr) 
if(iwt.ge.1) then 
rcdf(ncut) = var(iwt) 
else 
rcdf(ncut) = 1.0 
endif 
tcdf = tcdf + rcdf(ncut)
c
c Go back for another data:
c 
go to 3 
4 close(lin) 
write(*,*) 
write(*,*)  TRANS: there were ,ncut, reference values 
write(*,*)  trimmed ,nt, values 
write(*,*)  the total weight ,tcdf 
c
c Sort the Reference Distribution and Check for error situation:
c 
call sortem(1,ncut,rvr,1,rcdf,c,d,e,f,g,h) 
if(ncut.le.1.or.tcdf.le.EPSLON) then 
write(*,*) ERROR: too few data or too low weight 
stop 
endif 
if(utail.eq.4.and.rvr(ncut).le.0.0) then 
write(*,*) ERROR can not use hyperbolic tail with  
write(*,*)  negative values! - see manual  
stop 
endif 
if(zmin.gt.rvr(1)) then 
write(*,*) ERROR zmin should be no larger than smallest 
write(*,*)  reference value 
write(*,*)  zmin = ,zmin, vr1 ,rvr(1) 

stop 
endif 
if(zmax.lt.rvr(ncut)) then 
write(*,*) ERROR zmax should be no less than the largest 
write(*,*)  reference value 
write(*,*)  zmax = ,zmax, vrnt ,rvr(ncut) 
stop 
endif 
c 
c Turn the (possibly weighted) distribution into a cdf that is useful: 
c 
oldcp = 0.0 
cp = 0.0 
tcdf = 1.0 / tcdf 
do 5 i=1,ncut 
cp = cp + rcdf(i) * tcdf 
rcdf(i) =(cp + oldcp) * 0.5 
oldcp = cp 
5 continue 
c 
c Get median and write some info to the screen: 
c 
half = 0.5 
call locate(rcdf,ncut,half,j) 
gmedian = xlinint(rcdf(j),rcdf(j+1),rvr(j),rvr(j+1),half) 
write(*,*)  the median ,gmedian 
write(*,*)
c
c Get the output and distribution files ready:
c 
inquire(file=datafl,exist=testfl) 
if(.not.testfl) then 
write(*,*) ERROR ,datafl, does not exist! 
write(*,*)  you need some distributions to work with 
stop 
endif 
open(lin,file=datafl) 
open(lout,file=outfl) 
read(lin,(a40),err=96) str 
read(lin,*,err=96) nvari 
do 6 i=1,nvari 
6 read(lin,(a40),err=96) str 
write(lout,110) 
110 format(Trans output,/,1,/,transformed variable) 
c 
c MAIN Loop over all the increments to transform: 
c 
do 10 isim=1,nsim 
c 
c Read in the data values: 
c 
tcdf = 0.0 
num = 0 
do 11 i=1,nxyz

read(lin,*,end=14,err=98) (var(j),j=1,nvari) 
num = num + 1 
if(num.gt.MAXDAT) then 
write(*,*) ERROR: exceeded storage for data 
write(*,*)  have ,MAXDAT, available 
stop 
endif 
dvr(num) = var(ivrd) 
indx(num) = dble(real(num)) 
if(dvr(num).ge.tmind.and.dvr(num).lt.tmaxd) then 
dcdf(num) = 1.0 
if(iwtd.ge.1) dcdf(num) = var(iwtd) 
tcdf = tcdf + dcdf(num) 
endif 
11 continue 
14 if(tcdf.le.EPSLON) then 
write(*,*) ERROR: no data 
stop 
endif 
c 
c Turn the (possibly weighted) data distribution into a useful cdf: 
c 
call sortem(1,num,dvr,2,dcdf,indx,d,e,f,g,h) 
oldcp = 0.0 
cp = 0.0 
tcdf = 1.0 / tcdf 
do 12 i=1,num 
if(dvr(i).ge.tmind.and.dvr(i).lt.tmaxd) then 
cp = cp + dcdf(i) * tcdf 
dcdf(i) =(cp + oldcp) * 0.5 
oldcp = cp 
endif 
12 continue 
c 
c Now, get the right order back: 
c 
call sortem(1,num,indx,2,dcdf,dvr,d,e,f,g,h) 
c 
c Go through all the data back transforming them to the reference CDF: 
c 
do 13 i=1,num 
if(dvr(i).ge.tmind.and.dvr(i).lt.tmaxd) then 
zval = getz(dcdf(i),ncut,rcdf,rvr,zmin,zmax, 
+ ltail,ltpar,utail,utpar) 
else 
zval = -999.0 
endif 
write(lout,101) zval 
101 format(f9.4) 
13 continue 
c 
c END Main loop: 
c 
10 continue 

stop 
96 stop ERROR in distribution file! 
97 stop ERROR in parameter file! 
98 stop ERROR in global data file! 
end 
double precision function getz(cdfval,ncut,rcdf,rvr,zmin,zmax, 
+ ltail,ltpar,utail,utpar) 
c-----------------------------------------------------------------------
c 
c Get a Z value for a give CDF 
c **************************** 
c 
c Performs specified interpolation in the lower and upper tail. 
c 
c 
c 
c 
c 
c 
c Original: C.V. Deutsch May 1991 
c----------------------------------------------------------------------- 
parameter(EPSLON=1.0e-12,UNEST=-1.0) 
implicit real*8 (a-h,o-z) 
real*8 rcdf(ncut),rvr(ncut),ltpar,utpar,lambda 
integer ltail,utail,cclow,cchigh 
zero = 0.0 
one = 1.0
c
c Figure out part of distribution 0=lower tail, 1=middle, 2=upper tail:
c 
ipart = 1 
if(cdfval.le.rcdf(1)) ipart = 0 
if(cdfval.ge.rcdf(ncut)) ipart = 2
c
c ARE WE IN THE LOWER TAIL?
c 
if(ipart.eq.0) then 
getz = rvr(1) 
if(ltail.eq.1) then
c
c Linear interpolation to lower limit zmin:
c 
getz = powint(zero,rcdf(1),zmin,rvr(1),cdfval,one) 
else if(ltail.eq.2) then 
c 
c Power model interpolation to lower limit zmin: 
c 
cpow = 1.0/ltpar 
getz = powint(zero,rcdf(1),zmin,rvr(1),cdfval,cpow) 
endif 
c 

c OR, ARE WE IN THE MIDDLE? 
c 
else if(ipart.eq.1) then 
call locate(rcdf,ncut,cdfval,cclow) 
cchigh = cclow + 1 
getz = xlinint(rcdf(cclow),rcdf(cchigh), 
+ rvr(cclow),rvr(cchigh),cdfval) 
c 
c OR, ARE WE IN THE UPPER TAIL? 
c 
else if(ipart.eq.2) then 
c 
c Linear interpolation to upper limit zmax? 
c 
if(utail.eq.1) then 
getz = xlinint(rcdf(ncut),one,rvr(ncut), 
+ 
zmax,cdfval) 
else if(utail.eq.2) then 
cpow = 1.0 / utpar 
getz = powint(rcdf(ncut),one,rvr(ncut), 
+ 
zmax,cdfval,cpow) 
else if(utail.eq.4) then 
c 
c Fit a Hyperbolic Distribution? Figure out lambda: 
c 
lambda = (rvr(ncut)**utpar)*(1.0-rcdf(ncut)) 
getz = (lambda/(1.0-cdfval))**(1.0/utpar) 
endif 
endif 
c 
c Check for bounds and return: 
c 
if(getz.lt.zmin) getz = zmin 
if(getz.gt.zmax) getz = zmax 
return 
end 
double precision function xlinint(xlow,xhigh,ylow,yhigh,xval) 
c-----------------------------------------------------------------------
c 
c Linearly interpolates the value of y between (xlow,ylow) and 
c (xhigh,yhigh) for a value of x. 
c 
c----------------------------------------------------------------------- 
parameter(EPSLON=1.0e-12) 
real*8 xlow,xhigh,ylow,yhigh,xval 
if((xhigh-xlow).le.EPSLON) then 
xlinint = xlow 
else 
xlinint = ylow + (yhigh-ylow)*(xval -xlow) / (xhigh-xlow) 
end if 
return 

end 
double precision function powint(xlow,xhigh,ylow,yhigh,xval,pow) 
c-----------------------------------------------------------------------
c 
c Power interpolate the value of y between (xlow,ylow) and (xhigh,yhigh) 
c for a value of x and a power pow. 
c 
c----------------------------------------------------------------------- 
parameter(EPSLON=1.0e-20) 
implicit real*8 (a-h,o-z) 
powint = ylow + (yhigh-ylow)* 
+ (((xval-xlow)/amax1(EPSLON,(xhigh-xlow)))**pow) 
return 
end 
subroutine locate(xx,n,x,j) 
c-----------------------------------------------------------------------
c 
c Given an array xx of length n, and given a value x, this routine 
c returns a value j such that x is between xx(j) and xx(j+1). xx 
c must be monotonic, either increasing or decreasing. j=0 or j=n is 
c returned to indicate that x is out of range. 
c 
c Bisection Concept From Numerical Recipes, Press et. al. 1986 pp 90. 
c----------------------------------------------------------------------- 
real*8 xx(n),x 
c 
c Initialize lower and upper methods: 
c 
jl = 0 
ju = n 
c 
c If we are not done then compute a midpoint: 
c 
10 if(ju-jl.gt.1) then 
jm = (ju+jl)/2 
c 
c Replace the lower or upper limit with the midpoint: 
c 
if((xx(n).gt.xx(1)).eqv.(x.gt.xx(jm))) then 
jl = jm 
else 
ju = jm 
endif 
go to 10 
endif 
c 
c Return with the array index: 
c 

j = jl 
return 
end 
subroutine sortem(ib,ie,a,iperm,b,c,d,e,f,g,h) 
c-----------------------------------------------------------------------
c 
c Quickersort Subroutine 
c ********************** 
c 
c This is a subroutine for sorting a real array in ascending order. This 
c is a Fortran translation of algorithm 271, quickersort, by R.S. Scowen 
c in collected algorithms of the ACM. 
c 
c The method used is that of continually splitting the array into parts 
c such that all elements of one part are less than all elements of the 
c other, with a third part in the middle consisting of one element. An 
c element with value t is chosen arbitrarily (here we choose the middle 
c element). i and j give the lower and upper limits of the segment being 
c split. After the split a value q will have been found such that 
c a(q)=t and a(l)<=t<=a(m) for all i<=l<q<m<=j. The program then 
c performs operations on the two segments (i,q-1) and (q+1,j) as follows 
c The smaller segment is split and the position of the larger segment is 
c stored in the lt and ut arrays. If the segment to be split contains 
c two or fewer elements, it is sorted and another segment is obtained 
c from the lt and ut arrays. When no more segments remain, the array 
c is completely sorted. 
c 
c 
c INPUT PARAMETERS: 
c 
c ib,ie start and end index of the array to be sorteda 
c a array, a portion of which has to be sorted. 
c iperm 0 no other array is permuted. 
c 1 array b is permuted according to array a 
c 2 arrays b,c are permuted. 
c 3 arrays b,c,d are permuted. 
c 4 arrays b,c,d,e are permuted. 
c 5 arrays b,c,d,e,f are permuted. 
c 6 arrays b,c,d,e,f,g are permuted. 
c 7 arrays b,c,d,e,f,g,h are permuted. 
c >7 no other array is permuted. 
c 
c b,c,d,e,f,g,h arrays to be permuted according to array a. 
c 
c OUTPUT PARAMETERS: 
c 
c a = the array, a portion of which has been sorted. 
c 
c b,c,d,e,f,g,h =arrays permuted according to array a (see iperm) 
c 
c NO EXTERNAL ROUTINES REQUIRED: 

c 
c----------------------------------------------------------------------- 
implicit real*8 (a-h,o-z) 
dimension a(*),b(*),c(*),d(*),e(*),f(*),g(*),h(*) 
c 
c The dimensions for lt and ut have to be at least log (base 2) n 
c 
integer lt(64),ut(64),i,j,k,m,p,q 
c 
c Initialize: 
c 
j = ie 
m = 1 
i = ib 
iring = iperm+1 
if (iperm.gt.7) iring=1
c 
c If this segment has more than two elements we split it 
c 
10 if (j-i-1) 100,90,15 
c 
c p is the position of an arbitrary element in the segment we choose the 
c middle element. Under certain circumstances it may be advantageous 
c to choose p at random. 
c 
15 p = (j+i)/2 
ta = a(p) 
a(p) = a(i) 
go to (21,19,18,17,16,161,162,163),iring 
163 th = h(p) 
h(p) = h(i) 
162 tg = g(p) 
g(p) = g(i) 
161 tf = f(p) 
f(p) = f(i) 
16 te = e(p) 
e(p) = e(i) 
17 td = d(p) 
d(p) = d(i) 
18 tc = c(p) 
c(p) = c(i) 
19 tb = b(p) 
b(p) = b(i) 
21 continue 
c 
c Start at the beginning of the segment, search for k such that a(k)>t 
c 
q = j 
k = i 
20 k = k+1 
if(k.gt.q) go to 60 
if(a(k).le.ta) go to 20 
c 
c Such an element has now been found now search for a q such that a(q)<t 

c starting at the end of the segment. 
c 
30 continue 
if(a(q).lt.ta) go to 40 
q = q-1 
if(q.gt.k) go to 30 
go to 50 
c
c a(q) has now been found. we interchange a(q) and a(k)
c 
40 xa = a(k) 
a(k) = a(q) 
a(q) = xa 
go to (45,44,43,42,41,411,412,413),iring 
413 xh = h(k) 
h(k) = h(q) 
h(q) = xh 
412 xg = g(k) 
g(k) = g(q) 
g(q) = xg 
411 xf = f(k) 
f(k) = f(q) 
f(q) = xf 
41 xe = e(k) 
e(k) = e(q) 
e(q) = xe 
42 xd = d(k) 
d(k) = d(q) 
d(q) = xd 
43 xc = c(k) 
c(k) = c(q) 
c(q) = xc 
44 xb = b(k) 
b(k) = b(q) 
b(q) = xb 
45 continue 
c 
c Update q and search for another pair to interchange: 
c 
q = q-1 
go to 20 
50 q = k-1 
60 continue 
c
c The upwards search has now met the downwards search:
c 
a(i)=a(q) 
a(q)=ta 
go to (65,64,63,62,61,611,612,613),iring 
613 h(i) = h(q) 
h(q) = th 
612 g(i) = g(q) 
g(q) = tg 
611 f(i) = f(q) 

f(q) = tf 
61 e(i) = e(q) 
e(q) = te 
62 d(i) = d(q) 
d(q) = td 
63 c(i) = c(q) 
c(q) = tc 
64 b(i) = b(q) 
b(q) = tb 
65 continue 
c 
c The segment is now divided in three parts: (i,q-1),(q),(q+1,j) 
c store the position of the largest segment in lt and ut 
c 
if (2*q.le.i+j) go to 70 
lt(m) = i 
ut(m) = q-1 
i = q+1 
go to 80 
70 lt(m) = q+1 
ut(m) = j 
j = q-1 
c 
c Update m and split the new smaller segment 
c 
80 m = m+1 
go to 10 
c 
c We arrive here if the segment has two elements we test to see if 
c the segment is properly ordered if not, we perform an interchange 
c 
90 continue 
if (a(i).le.a(j)) go to 100 
xa=a(i) 
a(i)=a(j) 
a(j)=xa 
go to (95,94,93,92,91,911,912,913),iring 
913 xh = h(i) 
h(i) = h(j) 
h(j) = xh 
912 xg = g(i) 
g(i) = g(j) 
g(j) = xg 
911 xf = f(i) 
f(i) = f(j) 
f(j) = xf 
91 xe = e(i) 
e(i) = e(j) 
e(j) = xe 
92 xd = d(i) 
d(i) = d(j) 
d(j) = xd
93 xc = c(i) 
c(i) = c(j)

c(j) = xc 
94 xb = b(i) 
b(i) = b(j) 
b(j) = xb 
95 continue 
c 
c If lt and ut contain more segments to be sorted repeat process: 
c 
100 m = m-1 
if (m.le.0) go to 110 
i = lt(m) 
j = ut(m) 
go to 10 
110 continue 
return 
end 
Parameter File for TRANS 
The following lines are the input parameter file for TRANS. This file must be edited using a 
standard ASCII text editor to specify the appropriate quantities for the problem under 
consideration. 
Parameters for TRANS 
******************** 
START OF PARAMETERS: 
../../data/true.dat 
1 0 
-1.0e21 1.0e21 
../../data/cluster.dat 
3 0 
-1.0e21 1.0e21 
1000 1 
trans.out 
\file with ref distribution 
\iv, iwt 
\tmin, tmax 
\file with uncorrected dists 
\ivr, iwt 
\tmin, tmax 
\nxyz, nsim 
\file for revised distributions 
0.0 250.0 \zmin, zmax 
1 1.0 \lower tail: option, parameter 
4 1.5 \upper tail: option, parameter 
Validation Exercise for TRANS 
The following activities were conducted as a validation exercise for software routine TRANS. A 
distribution of values was selected (the choice of distribution is arbitrary). Specifically, a 
standard-normal distribution was chosen, coregionalized after matrix porosity in the CHn model 
unit (number of values, N = 35,088). Note, however, that the spatial information is never used in 
the transformation process. This arbitrary univariate distribution was processed through TRANS 
using the univariate distribution of bulk density values obtained from laboratory measurements 
on core samples (N = 510) as the reference distribution. Again, the specific choice of a reference 
distribution is arbitrary. At the end of processing, the transformed distribution is virtually 
identical in form to that of the reference distribution, and the validation exercise is complete. 
Note that the maximum and minimum values (and therefore the total range) of the transformed 
distribution are not limited by the necessarily limited and incomplete set of measured values. 

The following statistical and graphical observations indicate that the transformation has been 
accomplished properly (Table B-1): 
Table B-1. Comparative Statistics for Input, Transformed, and Reference Distributions 
Input Transformed Reference 
Mean 0.0950 1.6812 1.6814 
Std.Dev. 0.8821 0.1534 0.1525 
Minimum -3.8911 0.8081 1.3590 
Maximum 3.4005 2.3987 2.3110 
N 35,088 35,088 510 
1. The transformation is one-to-one, with the lowest input value corresponding to the 
lowest transformed value, the next highest input value corresponding to the next 
highest transformed value, and so on (monotonically increasing; Figure B-1.) 
Figure B-1. Cross plot of the Transformed Variable as a Function of the Input Variable Indicating a 
One-to-One Monotonically Increasing, but Nonlinear, Relationship 
2. 
The initial input and transformed output distributions are, in fact, different 
(Figure B-2.) 
3. 
The transformed output distribution is essentially identical to the target reference 
distribution in that the two curves overlie one another exactly (Figure B-3.). 

Figure B-2. Histograms and Cumulative Distribution Functions for (a) Input Standard-Normal 
Distribution and (b) Transformed Distribution Where N = 35,088 (both figures) 
Figure B-3. (a) Histogram and Cumulative Distribution Function for the Target Reference Distribution; 
Compare with Figure B-2 (b) where N = 510. (b) Comparison of cumulative distribution 
functions for the transformed [Figure B-2(b)] and target distributions [Figure B-3(a)] 

APPENDIX C 
VALIDATION OF UNCERT PROGRAM VARIO 


NOTE: The software documentation is for information only and to provide completeness and 
traceability. 
Background 
Program VARIO, versions 1.16 and 1.20, which is part of the industry-standard geostatistical 
software package UNCERT, is intended to compute a simple experimental variogram based on 
measured data spatially distributed in (up to) three-dimensions. The program was available to 
the Yucca Mountain Project as a compiled Unix executable. Accordingly, a source listing is not 
available. 
The algebraic formula for computation of the variogram is extremely simple, and is essentially 
equivalent to that for a variance. The variogram value, gamma (.), is specified simply as 
one-half the average squared difference of all pairs of points separated by a specified vector 
distance, h: 
Nh
1 2
.h = 
2Nh 
.[Z( x ) - Z( x+h ) ] (Eq. C-1) 
n=1 
where Zx is the value at spatial location, x, Z(x+h) is the value at a location a vector h distant, and 
Nh is the number of such pairs that can be identified. The variogram value is computed for a 
number of different separation values, and the results are plotted as a function of h. 
In practice, because the input data locations are not generally located on a regular grid, the 
separation vector is typically taken as a nominal distance plus-or-minus some tolerance value, 
usually set equal to one-half the lag spacing of the nominal values. The actual average 
separation distance is the h value used in plotting the variogram. Additionally, the orientation of 
the separation vector is typically specified as some nominal direction plus-or-minus a 
half-angle tolerance value. An omnidirectional variogram computed without regard for 
orientation of the vector, h, is specified using a half-angle tolerance of 90 degrees. A 
bandwidth distance value, computed normal to the nominal vector orientation, may be used in 
conjunction with the angular tolerance value to limit the sample locations selected for inclusion 
in the computation. The search parameters in VARIO are user specified. 
Procedure 
Because of the near-infinite number of lag spacing-angular orientation and tolerance-bandwidth 
combinations that are possible for all possible pairs of values of a large data set in 
three dimensions, the trick for validating a variogram program, such as VARIO, is to design a 
synthetic data set that is computationally tractable for manual calculation. The data set for which 
the locations are shown in Figure C-1 is specifically designed to test the search capabilities of 
VARIO and to allow manual reproduction of the variogram value for one lag distance. The 
calculations are easily conducted in a spreadsheet program, such as EXCEL 97. 

Source: For illustrative purposes only 
NOTE: Individual points may overlap at the scale shown. (a) horizontal plane, (b) vertical plane 
Figure C-1. Locations of Sample Values for the Test Case Data Set 
One subset of the data set appears as the east-trending series of points shown in Figure C-1(a). 
These locations consist of 50 randomly generated data values located at 500 (arbitrary) grid 
units apart along a line with the y-coordinate equal to zero. The second subset again consists of 
another 50 randomly generated values located at positions described by x- and y-coordinates 
regularly increasing by 200 arbitrary units. This paired-coordinate scheme produces a 45-degree 
line with a sample spacing of 200 2 = 282.843 units. The third subset of data consists of 
50 additional randomly generated values equally spaced at 100-unit intervals in the z-coordinate 
direction. 
The object of the validation exercise is to specify the relevant search parameters in each different 
direction with a very small tolerance (e.g. N45E plus-or-minus 5 degrees or a bandwidth of 
plus-or-minus 5 units), such that the search routines of VARIO locate only the samples belonging 
to each subset separately. This is the step that allows manual calculation of the corresponding 
values in EXCEL. The use of markedly different lag spacings for each subset allows 
identification of mixing of samples from the different subsets, something that would invalidate 
the simplistic manual computation. For example, if VARIO identify only the 500-unit-spaced 
values located along the x-axis. Because all data spacings are 500 units, the average lag distance, 
h, for the first lag should be precisely equal to 500.0 units. If the search locates pairs of points, 
one member of which belongs to another data subset, the average lag distance reported will differ 
from 500. The same logic applies in the other two directions. 
As a further (although perhaps less-definitive) check of the search strategy portion of VARIO, the 
randomly generated data values were scaled differently for each subset of data. The 500-unit 
lag set was scaled between 0 and 1, the 200-unit lag set between 0 and 5, and the 100-unit lag set 
between 0 and 10. Although this means of checking the search results is less definitive than 
examination of the average lag spacing, the different magnitudes provide the opportunity for 
improperly identified pairs to influence the VARIO-computed gamma value disproportionately in 

comparison to the simple manual calculation. Random data values are appropriate, in that there 
is no particular spatial structure required for the validation exercise. It is sufficient to confirm 
that the algebraic calculation of Equation C-1 is being carried out correctly. 
If the search routine executed internally within VARIO properly identifies the members of each 
data subset separately, and if the mathematical computation of Equation C-1 is executed 
properly, then the gamma value returned for the first lag of each directional variogram should 
essentially be identical to the value of Equation C-1 computed manually for the pairs of the data 
set considered as the tines on a comb (Figure C-2). Such an ordered computation of squared 
differences is easily implemented using the cell-based arithmetic capabilities of Excel. 
Source: For illustrative purposes only. 
NOTE: Gamma is computed as one-half the average squared difference. 
Figure C-2. Ordered Computation of Squared Differences between Sequential Values Implemented in 
the Manual Calculation Using Excel 97 
The validation criterion is essentially identical results computed by the two methods. 
Results 
Table C-1 presents the gamma values calculated by VARIO for the first lag for each directional 
variogram, as well as the actual average lag spacing, h, corresponding to each of the three data 
subsets. Also presented in the table are the results of the manual calculation conducted using 
the arithmetic capabilities of Excel. 
Table C-1. Validation Results 
Data Subset VARIO Results Excel Results 
Orientation Nominal h Average h Gamma Gamma 
N0E 500 500.0000 0.0828 0.0828 
N45E 282.843 282.8427 11.1223 11.1222 
Vertical 100 100.0000 1.8289 1.8289 
Source: For illustrative purposes only. 

Examination of the table indicates two things: (1) The average lag spacing is precisely identical 
to the nominal spacing of the input data values. This indicates that the search algorithm has 
restricted the computation of the variogram value to only the pairs involved in each data subset. 
(2) The gamma values reported by the two calculation methods are effectively identical. 
Observed differences are limited to the fourth decimal place and are nonmeaningful. 
Accordingly, the conclusion is that VARIO is computing the experimental variogram values as 
desired. 

APPENDIX D 
VALIDATION OF UNCERT PROGRAM VARIOFIT 


NOTE: The software documentation is for information only and to provide completeness and 
traceability. 
Background 
Program VARIOFIT Version 1.16, which is part of the industry-standard geostatistical 
software package UNCERT, is a simple program intended to compute and plot any of a number 
of algebraically defined variogram models for comparison with an experimental variogram 
derived from measured data. The program plots the underlying experimental variogram 
(computed separately with program vario) to the screen and overlays the theoretical model 
calculation on that screen image. The program can also create a postscript output file containing 
the screen image for printing and write a .out file containing various information related to the 
modeled variogram. The program was available for Yucca Mountain Project use as a compiled 
Unix executable. Accordingly, a source listing is not available. 
Procedure 
The validation approach adopted is as follows: 
1. 
A model variogram with arbitrary parameters is created using a dummy input 
variogram file to initialize the program (plotting of the dummy plot is turned off, and 
the input file is never used in the variogram model calculations). 
2. 
The model variogram information is written to a .out file for plotting in a separate 
graphics package capable of simple mathematical calculations (SIGMAPLOT, ver. 5). 
3. 
A second model variogram is computed in the second graphics package using the same 
input parameters. 
4. 
The two models are plotted on the same graph using different symbols/line types and a 
visual comparison is made. 
Validation has been conducted for the spherical variogram model (Deutsch and Journel 1998 
[DIRS 102895]), which is the only of current interest. The formula for the spherical model is as 
follows: 
.
.
3
.
h 
aa 
h
..
.
...
. 
5.1 
..
. 
-
5.0
h
=
c
.
a (Eq. D-1) 
=
.
3 
.
..
c 
h
>
.
a 
where c is the sill value associated with the model, h is the lag spacing under consideration and a 
is the range of the variogram model. A nugget component, c0, may. be added to the formula for 
the spherical model. Spherical models may be nested by adding two (or more) functions of 
this form, each with its own sill and range values. 
MDL-NBS-GS-000004 REV 01 D-1 September 2004 

The validation criterion is identical results by visual comparison. 
Results 
Shown in Figure D-1, a replicate of the screen image from VARIOFIT after entry of the variogram 
parameters and calculation of the model variogram. The model consists of two nested spherical 
structures plus a nugget effect. The parameters are indicated on the figure as: c0=2, c1=10, 
a1=1000, c2= 25, and a2=5000. The units for the range are arbitrary. 
Source: For illustrative purposes only. 
Figure D-1. Reproduced Screen Image of an Arbitrary Nested Variogram Model Generated 
Using the UNCERT Program, Variofit 
Shown in Figure D-2, the replotted variogram model from VARIOFIT plus the independently 
computed model using the same parameters, generated using the graphics/spreadsheet package, 
SIGMAPLOT. Note that the two models appear to overlie one another precisely. 
Source: For illustrative purposes only. 
Figure D-2. Comparison of Model Variograms Generated by Program Variofit and by an 
Independent Computation Using Sigmaplot 

Conclusion 
Based on the nearly exact correspondence between the variogram models computed by the two 
different methods (the maximum difference in gamma is 0.0005 compared to a total sill value 
of 37.0), we conclude that VARIOFIT is validated for use with the spherical model equation. A 
similar exercise could be conducted for other variogram model types, if necessary. 
Listing of SigmaPlot Transform 
;transform to compute nested spherical variograms 
c0 = 2.0 
c1 = 10. a1 = 1000 
c2 = 25. a2 = 5000 
n = 8 ; column in which to start variogram 
col(n) = data( 0, 7000, 35) ;lag distances to compute variogram for 
; intermediate computational results: first nugget, then nest 1, then nest 2 
col(n+2) = c0 
col(n+3) = if( col(n) < a1 , c1*( 1.5*col(n) / a1 - 0.5*(col(n)^3) / (a1^3) ) , c1 ) 
col(n+4) = if( col(n) < a2 , c2*( 1.5*col(n) / a2 - 0.5*(col(n)^3) / (a2^3) ) , c2 ) 
; output model and preservation of input parameters Output is sum of 3 cols 
col(n+1) = col(n+2) + col(n+3) + col(n+4) 
cell(18,1) = c0 
cell(18,3) = c1 
cell(18,4) = a1 
cell(18,6) = c2 
cell(18,7) = a2 
; compute difference between two methods 
col(7) = col(6) - col(9) 
; preserve maximum delta gamma 
cell(18,9) = max(col(7) ) 

INTENTIONALLY LEFT BLANK 

APPENDIX E 
DATA QUALIFICATION REPORT: CUTTINGS MINERALOGY DATA 


E1. INTRODUCTION 
E1.1 PURPOSE 
The purpose of this section is to qualify the XRD mineralogic abundance data used as an 
indicator of hydrous phase mineral alteration as described in Section 4.1.4. This is contrary to 
the information provided in the TWP (BSC 2004 [DIRS 169635], Section 8.3.3) that states no 
data qualification issues affect the RPM report. This statement was made as no specific 
technical user was citing the product output representation of hydraulic conductivity as direct 
input. The hydraulic conductivity representation uses the XRD data as direct input. However, 
for completeness the data qualification report (QRD) prepared for the Mineralogic Model was 
included in this report as it qualifies all the XRD mineralogic abundance data cited by this report. 
The user is reminded that while this qualifies the XRD mineralogical data, the representation of 
hydraulic conductivity provided by this model has not been validated as discussed in Section 7. 
This DQR uses the corroboration method according to Attachments 2 and 3 of AP-SIII.2Q, 
Qualification of Unqualified Data, to evaluate the data in DTN: LA9910DB831321.001 
[DIRS 113568]. The qualification of XRD mineral abundance data is documented in 
Appendices E, F and G. However, DTN: LA9910DB831321.001 [DIRS 113568] has several 
DTN sources. Further the data contained in these DTNs has been repartitioned because of the 
qualification report of Steinborn, 2002 [DIRS 160702]. The relationship between the 
DTN: LA9910DB831321.001 [DIRS 113568] and its DTN sources and the changes caused by 
the qualification report are illustrated in Appendix H. The QRD of Steinborn, 2002 
[DIRS 160702] and the qualification activity documented by Appendices E, F, and G qualifies 
the XRD mineralogic abundances cited as direct input by this report. 
The initial scope of this qualification activity was the qualification of the 
DTN: MO0101XRDDRILC.001 [DIRS 169517]. The data addressed in this DQR originally 
were part of DTN: LADV831321AQ97.001 [DIRS 107142] or DTN: LADB831321AN98.002 
[DIRS 109003]. These DTNs were evaluated by the technical assessment methods as 
documented by Steinborn (2002 [DIRS 160702]). The data in these DTNs consisted of mineral 
abundances determined by quantitative X-ray diffraction analysis of drill core, sidewall core, and 
drill cuttings. As described by Steinborn, a decision was made to not qualify data for sidewall 
core or drill cuttings because of the uncertainty in identifying the stratigraphic location from 
which the samples were obtained. It was recommended that these samples be used only to 
corroborate assumptions or other data. However, as described in this report further examination 
of the inputs to mineralogic model input DTNs revealed that DTNs LADV831321AQ97.001 
[DIRS 107142], LADV831321AQ97.007 [DIRS 113499], and LA9910JC831321.001 
[DIRS 113496] also require inclusion in this qualification activity. 
The unqualified data considered in this DQR are direct inputs to this report. As described in 
Section 4.1.4 mineral abundance data are used as direct indicators of hydrous-phase mineral 
alteration. The unqualified sidewall core and cuttings data provide mineralogic information for 
parts of the repository block and surrounding areas that otherwise would be unrepresented. In 
particular, many of these samples are from locations that are optimal to document lateral 
transitions in mineralogic assemblages. Three-dimensional representations of mineralogy and 
associated properties would be degraded by the omission of these data. Therefore, a 

qualification activity has been undertaken to assess the status of the specified data from sidewall 
core and cuttings. 
The purpose of this DQR is to recommend data that can be cited as qualified for use in this 
report. The appropriateness and limitations (if any) of the data with respect to intended use are 
addressed in this DQR. 
E1.2 SCOPE 
This DQR evaluates the data identified in Qualification Plan for Unqualified Mineralogical 
Model (MDL-NBS-GS-000003) Data, Appendix A of TWP-NBS-GS-000003 REV 05 
(BSC 2004 [DIRS 169635]). The data qualification plan identifies one unqualified 
DTN (MO0101XRDDRILC.001 [DIRS 169517]) containing or using acquired and developed 
mineral abundance data measured by investigators at Los Alamos National Laboratory (LANL). 
The DTN: MO0101XRDDRILC.001 [DIRS 169517] was created according to the 
recommendation of Steinborn (2002 [DIRS 160702]) that data from cuttings, sidewall samples, 
and intermittent core be segregated in a new, unqualified data package (Table E-1). The 
predecessor DTN: LADB831321AN98.002 [DIRS 109003] contains data that were originally 
published in a LANL report (Bish and Chipera 1989 [DIRS 101195]), plus additional data. A 
subset of the mineral abundance data in this DTN is used directly in mapping the distribution of 
a selected number of minerals and in correlation with rock properties for specific 
lithostratigraphic intervals. 
These data are generally unqualified because the data and/or the samples were acquired before 
issuance of the OCRWM Quality Assurance Requirements and Description (DOE 2004 
[DIRS 171539]). Some of the data were acquired according to technical procedures that had not, 
at that time, been approved for Yucca Mountain Project (YMP) work. YMP approval was 
obtained for LANL in 1991 (Horton 1990 [DIRS 169954]). The current qualification effort 
relies in part on the review of methods used for the x-ray diffraction (XRD) analyses of core 
samples qualified by Steinborn (2002 [DIRS 160702]). The methods used to analyze sidewall 
core and cuttings samples typically were the same as for core samples analyzed during the same 
time period. 
Table E-1. Source DTNs of MO0101XRDDRILC.001 [DIRS 169517] and their Relevant Contents 
LADV831321AQ97.001 [DIRS 107142] Mineralogy of USW H-3 sidewall core and cuttings samples 
Mineralogy of UE-25 J-12 cuttings samples 
Mineralogy of USW H-3 sidewall core and cuttings samples 
Mineralogy of USW H-4 sidewall core and cuttings samples 
LADB831321AN98.002 [DIRS 109003] Mineralogy of USW WT-1 cuttings samples 
Mineralogy of UE-25 p#1 intermittent core, sidewall core, and 
cuttings samples 
Mineralogy of USW WT-2 intermittent core and cuttings samples 
Mineralogy of USW H-5 sidewall core and cuttings samples 

The current qualification activity also addressed whether the correct data sets were placed into 
DTN: MO0101XRDDRILC.001 [DIRS 169517]. The criterion was that all XRD data from 
sidewall core, drill cuttings, and intermittent core used as input to the Mineralogic Model 
(MM3.0) (BSC 2004 [DIRS 170031]), and excluded from qualification by Steinborn (2002 
[DIRS 160702]), should have been placed into the DTN. Identification of data used as input was 
based on the contents of DTN: LA9908JC831321.001 [DIRS 113495], which contains the input 
files of mineralogic abundance data for the model. While it is true that the purpose of this 
qualification activity is to qualify data in MO0101XRDDRILC.001 [DIRS 169517] for any use 
on the Yucca Mountain Project, the specific data requirements of the mineralogical model must 
be addressed to provide maximum benefit. Appropriate recommendations will be made to 
achieve this goal. 
In the process of this qualification activity, it was found that DTN: MO0101XRDDRILC.001 
[DIRS 169517] and its predecessor DTN: LADB831321AN98.002 [DIRS 109003] do not 
contain all of the cuttings and sidewall core mineralogical data that were inputs to 
DTN: LA9908JC831321.001 [DIRS 113495] and the Mineralogic Model (MM3.0) (BSC 2004 
[DIRS 170031]). Sources of input to the Mineralogical Model also include 
DTN: LADV831321AQ97.001 [DIRS 107142] and DTN: LADV831321AQ97.007 
[DIRS 113499], both of which are listed in the Technical Data Management System as qualified 
even though they contain data from unqualified cuttings samples. An additional data source was 
the unqualified DTN: LA9910JC831321.001 [DIRS 113496]. 
E1.3 BACKGROUND 
The DTNs in Table E-1 contain XRD data that were collected by LANL investigators beginning 
in the early 1980s and completed by 1986. Descriptions of the analytical methodologies for 
these data can be found in published LANL documents (Levy 1984 [DIRS 101392], Bish and 
Vaniman 1985 [DIRS 101196], Chipera and Bish (1988 [DIRS 105080]), and Bish and Chipera 
1989 [DIRS 101195]). Table E-2 is an excerpt from a data set in 
DTN: MO0101XRDDRILC.001 [DIRS 169517] 

Table E-2. Sample Extraction from DTN: MO0101XRDDRILC.001 [DIRS 169517], SEP 
Table S01025_003, Illustrating the Data Type and Structure 
S01025_003 Data Report 
Table Description:
Mineral Abundance data from X-Ray Diffraction analyses of sidewall and drill cuttings fromUSW H-3, 01/02/1981 to 11/01/1988. 
TDIF: 311749 
DTN: MO0101XRDDRILC.001 
FOOTNOTES: Sample Number indicates depth in feet. Mineral Abundance values are reported inweight percent. All uncertainty values are valid within two standard deviations.
Clinoptilolite Abundance represents clinoptilolite/heulandite group mineral abundance. Opal-CTpresent in zeolitic tuff is reported as cristobalite. (SW) = sidewall core sample from Levy(1984 [DIRS 101392]). Tr. = trace (less than 0.5%).  indicates not detected. Blanks are 
intended. 
PARAMETERS: 
SMECTITE ABUNDANCE 
MICA GROUP ABUNDANCE 
CLINOPTILOLITE ABUNDANCE 
MORDENITE ABUNDANCE 
TRIDYMITE ABUNDANCE 
QUARTZ ABUNDANCECRISTOBALITE/OPAL ABUNDANCEFELDSPAR ABUNDANCE 
GLASS ABUNDANCE 
HEMATITE ABUNDANCE 
HORNBLENDE ABUNDANCE 
SAMPLE 
NUMBER 
DEPTH (m) SMECTITE 
ABUNDANC 
E (%) 
SMECTITE 
UNCERTAINTY 
(+/-) 
MICA GROUP 
ABUNDANCE 
(%) 
MICA GROUP 
UNCERTAINT 
Y (+/-) 
CLINOPTILO 
-LITE 
ABUNDANCE 
(%) 
CLINOPTILO 
-LITE 
UNCERTAINT 
Y (+/-)
470-480 143.3-
146.3 
-2 1 -
520-530 158.5-
161.5 
---
1550(SW) 472.4 Tr. 1 1 2 3 
1800(SW) 548.6 2 3 1 1 60 10 
DTN=data tracking number; TDIF=technical data information form 

E2. QUALIFICATION METHODS 
The qualification method of corroboration is used in this DQR to determine the degree to which 
mineralogic data for the sidewall core and cuttings samples are consistent with qualified 
mineralogic information for the same stratigraphic units in nearby cored boreholes. The 
corroborating-data method involves comparing different data sets to evaluate the consistency of 
mineral content within lithostratigraphic units between the existing unqualified data and data that 
have been qualified under a qualification report or data with verified Q status. In addition, 
inferences of similar primary texture, mineralogy, or mineralogical alteration between cuttings 
data and the data from core samples were identified and documented wherever this information 
aided the corroboration process. 
The initial evaluation of data quality and correctness reviewed the uncertainties associated with 
collection of cuttings and sidewall-core samples. The principal uncertainty is in assigning depths 
to the cutting samples and sidewall-core samples. The existing data sets of cuttings and 
sidewall-core samples lack the visible record of continuous core needed to provide the highest 
level of confidence in the accuracy of recorded sampling depths. Uncertainties may also be 
associated with the subsampling of cuttings samples. Subsampling can be problematic because 
the samples commonly have mixed lithologies including rock types from depths shallower than 
the nominal sample depth. Records supporting the unqualified data and the adequacy of the 
procedures or methods used were reviewed and addressed in the evaluation of these data. Types 
of records examined included evidence of procedures in use when the samples were collected, 
sample collection and description records, XRD analysis records, and additional types of sample 
analyses that document sample integrity. 
E2.1 EVALUATION CRITERIA 
The types of geologic sample materials subject to qualification are identified for each borehole in 
Table E-3. The criteria of consideration in evaluating the qualification of the unqualified 
cuttings mineralogical data include the following conditions. 
 
Do samples from a particular lithostratigraphic unit have mineralogic abundances 
similar to samples of the same unit from qualified borehole data sets? 
 
Do samples from a succession of lithostratigraphic units show mineralogic changes 
between units that are consistent with changes observed in the qualified data sets? 
 
Does at least some documentation exist for sample collection, sample description, or 
X-ray diffraction analysis? 
 
Is the X-ray diffraction methodology used comparable to the methodology used to 
collect data for qualified data sets? 

Table E-3. List of Boreholes in DTN: MO0101XRDDRILC.001 [DIRS 169517], Material Type Analyzed, 
and Qualification Status of the Material 
Well Material Type Qualification Status 
UE-25 J-12 Cuttings Unqualified 
UE-25 p#1 Cuttings, Intermittent Core, Sidewall Core Unqualified 
USW H-3 Cuttings, Sidewall Core Unqualified 
USW H-4 Cuttings, Sidewall Core Unqualified 
USW H-5 Cuttings, Sidewall Core Unqualified 
USW WT-1 Cuttings Unqualified 
USW WT-2 Cuttings, Intermittent Core Unqualified 
These criteria were selected to incorporate the considerations in procedure AP-SIII.2Q 
Attachments 2 and 3, plus data-specific considerations. 
This evaluation was primarily qualitative in nature because the number of cuttings or sidewall 
samples from certain drill holes is small and many lithostratigraphic units are represented by 
only one sample in a data set. The basic statistic for data comparisons is the raw data range 
representing the upper and lower bounds of mineral abundance in a lithostratigraphic unit. 
E3. EVALUATION RESULTS 
The evaluation results are presented on a borehole-by-borehole basis in each of the 
following sections. Section E3.1 provides a comparison of samples analyzed between 
DTNs MO0101XRDDRILC.001 [DIRS 169517], LADB831321AN98.002 [DIRS 109003], 
LADV831321AQ97.001 [DIRS 107142], LADV831321AQ97.007 [DIRS 113499], 
LA9910JC831321.001 [DIRS 113496], and LA9908JC831321.001 [DIRS 113495]. 
Section E3.2 reviews the existing records pertinent to sample collection, description, and 
preparation for XRD analysis. Section E3.3 provides descriptions of the XRD methodologies 
used to analyze samples from the boreholes. Section E3.4 contains comparisons of 
mineralogical abundances between the unqualified data sets and qualified data sets from 
continuously cored boreholes. 
E3.1 DTN ANALYSIS CONTENTS 
This section provides comparisons of samples analyzed by quantitative XRD among 
DTNs MO0101XRDDRILC.001 [DIRS 169517], LADB831321AN98.002 [DIRS 109003], 
LADV831321AQ97.001 [DIRS 107142], LA9910JC831321.001 [DIRS 113496], and 
LA9908JC831321.001 [DIRS 113495]. A separate set of comparisons is made for each borehole 
data set to be evaluated. DTN: LA9908JC831321.001 [DIRS 113495] contains the input files 
for Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]), with sample locations and 
mineralogical abundances rendered in a form suitable for the model. The purpose of the 
comparisons is to confirm the identities of DTNs that are sources for the model input and assure 
that all necessary unqualified source data are included in the qualification effort. 

UE-25 J-12 
As shown in table E-4, the J-12 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived completely and exclusively from DTN: LADB831321AN98.002 
[DIRS 109003]. They are not used as input to the Mineralogic Model and therefore are not in 
the DTN that provides input files for the model. 
Table E-4. Comparison for Equivalence of J-12 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 LADB831321AN98.002 
[DIRS 169517] 
(depth interval in ft) 
[DIRS 109003] 
(depth interval in ft) 
LA9908JC831321.001 
[DIRS 113495] 
620-630 620-630 
No J-12 sample analyses as input 
650-660 650-660 
710-720 710-720 
770-780 770-780 
860-870 860-870 
905-915 905-915 
983-992 983-992 
1067-1077 1067-1077 
1093-1097 1093-1097 
1107-1110 1107-1110 
1121-1126 1121-1126 
1136-1139 1136-1139 
UE-25 p#1 
As shown in table E-5, the UE-25 p#1 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived completely from DTN: LADB831321AN98.002 [DIRS 109003]. 
Inputs to the Mineralogical Model (MM3.0) (BSC 2004 [DIRS 170031]) in 
DTN: LA9908JC831321.001 [DIRS 113495] are also derived from 
DTN: LADB831321AN98.002 [DIRS 109003]. Two numbers (shown in bold type, 
corresponding to parts of the sampling depth intervals in meters) in 
DTN: LADB831321AN98.002 [DIRS 109003] are incorrect, and the errors also are present in 
DTN: MO0101XRDDRILC.001 [DIRS 169517]. The errors are traceable to Bish and Chipera 
(1989 [DIRS 101195], p. 24, 26). These errors were fixed in DTN: LA9908JC831321.001 
[DIRS 113495] for the one erroneous sample-depth conversion included in that DTN. However, 
DTN: LA9908JC831321.001 [DIRS 113495] contains one erroneous entry for this borehole. 
The entry for elevation 131.0 m/depth 3,225 ft (shown in bold type) does not correspond to an 
analyzed sample. The mineralogic data associated with this entry correspond with data for 
3,320-3,330-ft depth as listed in Bish and Chipera (1989 [DIRS 101195], p. 25) and in 
DTNs LADB831321AN98.002 [DIRS 109003] and MO0101XRDDRILC.001 [DIRS 169517]. 
The correct elevation would be -(33251239.37)+1114=100.5 m, where 1239.37 is the 
feet-to-meters conversion factor and 1114 is the ground-level elevation in meters of the borehole 
(DTN: LA9908JC831321.001 [DIRS 113495]). 

Table E-5. Comparison for Equivalence of UE-25 p#1 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft/m) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
200-210/61.8-64.0 200-210/61.8-64.0 1051.5/205 
260-270/79.2-82.3 260-270/79.2-82.3 1033.2/265 
280-290/85.3-88.4 280-290/85.3-88.4 1027.1/285 
410-420/125.0-128.0 410-420/125.0-128.0 987.5/415 
570-580/173.7-176.8 570-580/173.7-176.8 938.7/575 
810-820/246.9-249.9 810-820/246.9-249.9 865.6/815 
900-910/274.3-277.4 900-910/274.3-277.4 838.2/905 
1040-1050/317.0-320.0 1040-1050/317.0-320.0 795.5/1045 
1240-1250/378.0-381.0 1240-1250/378.0-381.0 reassigned to 730.0/1260 
1260-1270/384.0-387.1 1260-1270/384.0-387.1 728.4/1265 
1290/393.2 (sidewall core) 1290/393.2 (sidewall core) 720.8/1290 
1340-1350/408.4-411.5 1340-1350/408.4-411.5 704.0/1345 
1400/426.7 (sidewall core) 1400/426.7 (sidewall core) 687.3/1400 
1420/432.8 (sidewall core) 1420/432.8 (sidewall core) 681.2/1420 
1470/448.1 (sidewall core) 1470/448.1 (sidewall core) reassigned to 668.0/1463 
1590-1598/484.6-487.1 1590-1598/484.6-487.1 627.8/1595 
1640-1650/499.9-502.9 1640-1650/499.9-502.9 612.6/1645 
1690-1700/515.1-518.2 1690-1700/515.1-518.2 597.4/1695 
1730-1740/527.3-530.4 1730-1740/527.3-530.4 585.2/1735 
1790-1800/545.6-548.6 1790-1800/545.6-548.6 566.9/1795 
1830-1840/557.8-560.8 1830-1840/557.8-560.8 554.7/1835 
1870-1880/570.0-573.0 1870-1880/570.0-573.0 542.5/1875 
1920-1930/585.2-588.3 1920-1930/585.2-588.3 527.3/1925 
1970-1980/600.5-603.5 1970-1980/600.5-603.5 512.0/1975 
1990-2000/606.6-609.6 1990-2000/606.6-609.6 505.9/1995 
2030-2040/618.7-621.8 2030-2040/618.7-621.8 493.7/2035 
2070-2080/630.9/634.0 2070-2080/630.9/634.0 481.5/2075 
2120-2130/646.2-649.2 2120-2130/646.2-649.2 466.3/2125 
2150-2160/655.3-658.4 2150-2160/655.3-658.4 457.2/2155 
2210-2220/673.6-676.7 2210-2220/673.6-676.7 438.9/2215 
2240-2250/682.8-685.8 2240-2250/682.8-685.8 429.7/2245 
2280-2290/694.9-698.0 2280-2290/694.9-698.0 417.5/2285 
2330-2340/710.2-713.2 2330-2340/710.2-713.2 402.3/2335 
2370-2380/722.4-725.4 2370-2380/722.4-725.4 390.1/2375 
2410-2420/734.6-737.6 2410-2420/734.6-737.6 377.9/2415 
2460-2470/749.8-752.9 2460-2470/749.8-752.9 362.7/2465 
2510-2520/765.0-768.1 2510-2520/765.0-768.1 347.4/2515 
2570-2580/783.3-786.4 2570-2580/783.3-786.4 329.1/2575 
2630-2640/801.6-804.7 2630-2640/801.6-804.7 310.9/2635 
2650-2660/807.7-810.8 2650-2660/807.7-810.8 304.8/2655 
2690-2700/819.9-823.0 2690-2700/819.9-823.0 292.6/2695 
2750-2760/838.2-841.2 2750-2760/838.2-841.2 274.3/2755 

Table E-5. Comparison for Equivalence of UE-25 p#1 Analyzed Samples Included in Data Packages 
(Continued) 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft/m) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
2790-2800/850.4-853.4 2790-2800/850.4-853.4 262.1/2795 
2840-2850/865.6-868.7 2840-2850/865.6-868.7 246.8/2845 
2890-2900/880.9-883.9 2890-2900/880.9-883.9 231.6/2895 
2940-2950/896.1-899.2 2940-2950/896.1-899.2 216.4/2945 
2980-2990/908.3-911.4 2980-2990/908.3-911.4 204.2/2985 
3030-3040/923.5-926.6 3030-3040/923.5-926.6 188.9/3035 
3080-3090/938.8-941.8 3080-3090/938.8-941.8 173.7/3085 
3130-3140/954.0-957.1 3130-3140/954.0-957.1 158.5/3135 
3160-3170/963.2-966.2 3160-3170/963.2-966.2 149.3/3165 
3230-3240/984.5-987.6 3230-3240/984.5-987.6 128.0/3235 
3270-3280/996.7-999.7 3270-3280/996.7-999.7 115.8/3275 
3320-3330/1011.9-1015.0 3320-3330/1011.9-1015.0 131.0/3225 location is in error 
3370-3380/1027.2-1030.2 3370-3380/1027.2-1030.2 85.3/3375 
3410-3420/1039.4-1042.4 3410-3420/1039.4-1042.4 73.1/3415 
3453/1052.5 (core) 3453/1052.5 (core) 61.5/3453 
3480-3490/1060.7-1063.8 3480-3490/1060.7-1063.8 51.8/3485 
3510-3520/1069.8-1072.9 3510-3520/1069.8-1072.9 42.6/3515 
3550-3560/1082.0-1085.1 3550-3560/1082.0-1085.1 30.4/3555 
3560-3570/1085.1-1088.1 3560-3570/1085.1-1088.1 27.4/3565 
3590-3600/1094.2-1097.3 3590-3600/1094.2-1097.3 18.2/3595 
3630-3640/1106.4-1109.5 3630-3640/1106.4-1109.5 6.1/3635 
3650-3660/1112.5-1115.6 3650-3660/1112.5-1115.6 0.0/3655 
3660-3670/1115.6-1118.6 3660-3670/1115.6-1118.6 -3.1/3665 
3690-3700/1124.7-1127.8 3690-3700/1124.7-1127.8 -12.2/3695 
3720-3730/1133.9-1136.9 3720-3730/1133.9-1136.9 -21.4/3725 
3750-3760/1143.0-1146.0 3750-3760/1143.0-1146.0 -30.5/3755 
3790-3800/1155.2-1158.2 3790-3800/1155.2-1158.2 -42.7/3795 
3820-3830/1164.3-1167.4 3820-3830/1164.3-1167.4 -51.9/3825 
3860-3870/1176.5-1179.6 3860-3870/1176.5-1179.6 -64.1/3865 
3913/1192.7 (core) 3913/1192.7 (core) -78.7/3913 
3916/1193.6 (core) 3916/1193.6 (core) -79.6/3916 
3928/1197.3 (core) 3928/1197.3 (core) -83.3/3928 
3940-3950/1200.9-1204.0 3940-3950/1200.9-1204.0 -88.4/3945 
3980-3990/1213.1-1216.2 3980-3990/1213.1-1216.2 -100.6/3985 
4040-4050/1231.4-1234.4 4040-4050/1231.4-1234.4 -118.9/4045 
4070-4080/1240.5-1243.6 4070-4080/1240.5-1243.6 -128.1/4075 
4080-4090/1243.6-1249.1 4080-4090/1243.6-1249.1 Paleozoic unit not in model 
4170-4180/1271.0-1274.1 4170-4180/1271.0-1274.1 Paleozoic unit not in model 
4313-4318/1314.6-1316.1 4313-4318/1314.6-1316.1 Paleozoic unit not in model 
NOTE: erroneous information is in bold type. 

USW H-3 
As shown in table E-6, the USW H-3 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived from DTN: LADB831321AN98.002 DIRS 109003], duplicated by 
data in LADV831321AQ97.001 [DIRS 107142]. It also shows that some of the data used as 
input by the Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]) were derived only from 
DTN: LADV831321AQ97.001 [DIRS 107142] and are missing from 
DTN: MO0101XRDDRILC.001 [DIRS 169517]. 
Table E-6. Comparison for Equivalence of USW H-3 Analyzed Samples Included in Data Packages 
LA9908JC831321.001 
MO0101XRDDRILC.001 
[DIRS 169517] 
(depth interval in ft/m) 
LADB831321AN98.002 
[DIRS 109003] 
(depth interval in ft/m) 
LADV831321AQ97.001 
[DIRS 107142] 
(depth interval in ft/m) 
[DIRS 113495] 
(midpoint elevation in 
m/depth in ft) 
470-480/143.3-146.6 470-480/143.3-146.6 470-480/143.3-146.6 1338.4/475 
520-530/158.5-161.5 520-530/158.5-161.5 520-530/158.5-161.5 1323.2/525 
540-550/164.6-167.6 540-550/164.6-167.6 540-550/164.6-167.6 1317.1/545 
610-620/185.9-189.0 610-620/185.9-189.0 610-620/185.9-189.0 1295.7/615 
740-750/225.6-228.6 740-750/225.6-228.6 740-750/225.6-228.6 1256.1/745 
800-810/243.8-246.9 800-810/243.8-246.9 800-810/243.8-246.9 1237.8/805 
870-880/265.2-268.2 870-880/265.2-268.2 870-880/265.2-268.2 1216.5/875 
930-940/283.5-286.5 930-940/283.5-286.5 930-940/283.5-286.5 1198.2/935 
990-1000/301.8-304.8 990-1000/301.8-304.8 990-1000/301.8-304.8 1179.9/995 
1030-1040/313.9-317.0 1030-1040/313.9-317.0 1030-1040/313.9-317.0 1167.7/1035 
1100-1110/335.3-338.3 1100-1110/335.3-338.3 1100-1110/335.3-338.3 1146.4/1105 
1160-1170/353.6-356.6 1160-1170/353.6-356.6 1160-1170/353.6-356.6 1128.1/1165 
1270-1280/387.1-390.1 1270-1280/387.1-390.1 1270-1280/387.1-390.1 1094.6/1275 
1320-1330/402.3-405.4 1320-1330/402.3-405.4 1320-1330/402.3-405.4 1079.3/1325 
1360-1370/414.5-417.6 1067.1/1365 
1440-1450/438.9-442.0 1042.8/1445 
1480-1490/451.1-454.2 1030.6/1485 
1500-1510/457.2-460.2 1024.5/1505 
1550/472.4 (sidewall core) 1550/472.4 (sidewall core) 1550/472.4 (sidewall core) 1010.8/1550 
1655/504.5 (sidewall core) 1655/504.5 (sidewall core) 1655/504.5 (sidewall core) 978.7/1655 
1700/518.2 (sidewall core) 1700/518.2 (sidewall core) 1700/518.2 (sidewall core) 965.0/1700 
1800/548.6 (sidewall core) 1800/548.6 (sidewall core) 1800/548.6 (sidewall core) 934.6/1800 
1900/579.1 (sidewall core) 1900/579.1 (sidewall core) 1900/579.1 (sidewall core) 904.1/1900 
2400/731.5 (sidewall core) 2400/731.5 (sidewall core) 2400/731.5 (sidewall core) 751.7/2400 
2440/743.7 (sidewall core) 2440/743.7 (sidewall core) 2440/743.7 (sidewall core) 739.5/2440 
2490/759 (sidewall core) 2490/759 (sidewall core) 2490/759.0 (sidewall core) 724.2/2490 
NOTE: Blank boxes are intentional. 

USW H-4 
As shown in table E-7, the USW H-4 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived completely and exclusively from DTN: LADB831321AN98.002 
[DIRS 109003]. There is a one-to-one correspondence between the 
DTN: MO0101XRDDRILC.001 [DIRS 169517] data set and the data set in 
DTN: LA9908JC831321.001 [DIRS 113495] that is input to the Mineralogic Model (MM3.0) 
(BSC 2004 [DIRS 170031]). 
Table E-7. Comparison for Equivalence of USW H-4 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft/m) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
310-320/94.5-97.5 310-320/94.5-97.5 1152.5/315 
390-400/118.9-121.9 390-400/118.9-121.9 1128.1/395 
440-450/134.1-137.2 440-450/134.1-137.2 1112.9/445 
490-500/149.4-152.4 490-500/149.4-152.4 1097.6/495 
640-650/195.1-198.1 640-650/195.1-198.1 1051.9/645 
830-840/253.0-256.0 830-840/253.0-256.0 994.0/835 
910-920/277.4-280.4 910-920/277.4-280.4 969.6/915 
940-950/286.5-289.6 940-950/286.5-289.6 960.5/945 
1040-1050/317.0-320.0 1040-1050/317.0-320.0 930.0/1045 
1150-1160/350.5-353.6 1150-1160/350.5-353.6 896.5/1155 
1190-1200/362.7-365.8 1190-1200/362.7-365.8 884.3/1195 
1230-1240/374.9-378.0 1230-1240/374.9-378.0 872.1/1235 
1312/399.9 (sidewall core) 1312/399.9 (sidewall core) 848.6/1312 
1320-1330/402.3-405.4 1320-1330/402.3-405.4 844.6/1325 
1350-1360/411.5-414.5 1350-1360/411.5-414.5 833.5/1355 
1410-1420/429.8-432.8 1410-1420/429.8-432.8 817.2/1415 
1420/432.8 (sidewall core) 1420/432.8 (sidewall core) 815.7/1420 
1455/443.5 (sidewall core) 1455/443.5 (sidewall core) 805.0/1455 
1540-1550/469.4-472.4 1540-1550/469.4-472.4 777.6/1545 
1550/472.4 (sidewall core) 1550/472.4 (sidewall core) 776.1/1550 
1600-1610/487.7-490.7 1600-1610/487.7-490.7 770.0/1570* 
1640-1650/499.9-502.9 1640-1650/499.9-502.9 747.1/1645 
1656/504.8 (sidewall core) 1656/504.8 (sidewall core) 741.0/1665* 
1710-1720/521.2-524.3 1710-1720/521.2-524.3 725.8/1715 
1790-1800/545.6-548.6 1790-1800/545.6-548.6 701.4/1795 
1900-1910/579.1-582.2 1900-1910/579.1-582.2 667.9/1905 
1980-1990/603.5-606.6 1980-1990/603.5-606.6 643.5/1985 
* These samples were reassigned to different depths, as shown here and explained in Table F-3). 

USW H-5 
Comparison of the data contents of DTNs MO0101XRDDRILC.001 [DIRS 169517] and 
LA9908JC831321.001 [DIRS 113495] indicated that there must be additional sources of input to the 
Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]) beside DTN: LADB831321AN98.002 
[DIRS 109003] (the precursor of DTN: MO0101XRDDRILC.001 [DIRS 169517]). Two other DTNs 
were found to be input sources. DTN: LADV831321AQ97.007 [DIRS 113499] is listed in the 
Technical Data Management System as a qualified DTN even though it contains unqualified data 
from the USW H-5 cutting samples. This DTN is identified in the Mineralogic Model (MM3.0) 
(BSC 2004 [DIRS 170031]) as an input source. DTN: LA9910JC831321.001 [DIRS 113496] is 
listed as unqualified in the Technical Data Management System and is not identified as an input 
source in the Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]). Table E-8 shows how 
the three DTNs, with some duplication, comprise the input to the Mineralogical Model. 
Table E-8. Comparison for Equivalence of USW H-5 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 
[DIRS 169517] 
and LADB831321AN98.002 
[DIRS 109003] 
(depth interval in ft/m) 
LA9910JC831321.001 
[DIRS 113496] 
(depth interval in 
ft/depth interval 
midpoint in m) 
LADV831321AQ97.007 
[DIRS 113499] 
(depth interval in ft/m) 
LA9908JC831321.001 
[DIRS 113495] 
(midpoint elevation in 
m/depth in ft) 
40-50/13.7 1464.9/45 
50/15.2 1463.3/50 
110-12035.1 1443.5/115 
160-170/50.3 1428.3/165 
190/57.9 1420.7/190 
230-240/71.6 1407.0/235 
320-33099.1 1379.5/325 
380-390117.3 1361.2/385 
420/128 reassigned to 
1353.0/412 
450/137.2 1341.4/450 
460-470/141.7 1336.8/465 
620-630/189.0-192.0 1288.1/625 
700/213.4 700/213.4 1265.2/700 
720-730/219.5-222.5 1257.6/725 
750-760/228.6-231.6 1248.5/755 
800-810/243.8-246.9 800-810/243.8-246.9 1233.2/805 
830-840/253.0-256.0 830-840/253.0-256.0 1224.1/835 
860-870/262.1-265.2 1214.9/865 
920-930/280.4-283.5 920-930/280.4-283.5 1196.6/925 
970-980/295.7-298.7 970-980/295.7-298.7 1181.4/975 
990-1000/301.8-304.8 1175.3/995 
1050/320 1050/320.0 1158.5/1050 
1090-1100/332.2-335.3 1144.8/1095 
1150-1160/350.3-353.6 1150-1160/350.5-353.6 1126.5/1155 
1200-1210/365.8-368.8 1111.3/1205 
1230-1240/374.9-378.0 1230-1240/374.9-378.9 1102.2/1235 

Table E-8. Comparison for Equivalence of USW H-5 Analyzed Samples Included in Data Packages 
(Continued) 
MO0101XRDDRILC.001 LA9910JC831321.001 
[DIRS 169517] 
and LADB831321AN98.002 
[DIRS 113496] 
(depth interval in LADV831321AQ97.007 
LA9908JC831321.001 
[DIRS 113495] 
[DIRS 109003] 
(depth interval in ft/m) 
ft/depth interval 
midpoint in m) 
[DIRS 113499] 
(depth interval in ft/m) 
(midpoint elevation in 
m/depth in ft) 
1290-1300/393.2-396.2 1290-1300/393.2-396.2 1083.9/1295 
1350-1360/411.5-414.5 1065.6/1355 
1380-1390/420.6-423.7 1380-1390/420.6-423.7 1056.4/1385 
1450-1460/442.0-445.0 1450-1460/442.0-445.0 1035.1/1455 
1490-1500/454.2-457.2 1490-1500/454.2-457.2 1022.9/1495 
1530-1540/466.3-469.4 1010.7/1535 
1590-1600/484.6-487.7 1590-1600/484.6-487.7 992.4/1595 
1610/490.7 1610/490.7 987.9/1610 
1630-1640/496.8-499.9 980.2/1635 
1650-1660/502.9-506.0 974.1/1655 
1666/507.8 (sidewall core) 970.8/1666 
1710-1720/521.2-524.3 955.8/1715 
1750/533.4 945.2/1750 
1762/537.1 (sidewall core) 941.5/1762 
1760-1770/538.0 940.6/1765 
1800/548.6 (sidewall core) 929.9/1800 
1820-1830/556.3 922.3/1825 
1852/564.5 (sidewall core) 914.1/1852 
1875/571.5 (sidewall core) 907.1/1875 
1890-1900/577.6 901.0/1895 
1900-1910/580.6 897.9/1905 
1910-1920/583.7 894.9/1915 
1917/584.3 (sidewall core) 894.3/1917 
1920-1930/586.7 891.8/1925 
1930/588.3 890.3/1930 
1950-1960/595.9 882.7/1955 
1966/599.2 (sidewall core) 879.3/1966 
1990-2000/608.1 reassigned to 
879.2/1966.5 
2070-2080/632.5 846.1/2075 
2140-2150/653.8 Reassigned to 
830.0/2128 
2200/670.6 808.0/2200 
2230-2240/681.2 797.4/2235 
2260-2270/690.4 788.2/2265 
NOTE: Blank boxes are intentional. 
USW WT-1 
As shown in Table E-9, the USW WT-1 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived completely and exclusively from DTN: LADB831321AN98.002 
MDL-NBS-GS-000004 REV 01 E-13 September 2004 

[DIRS 109003]. Inputs to the Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]) in 
DTN: LA9908JC831321.001 [DIRS 113495] also are derived from DTN: DB831321AN98.002 
[DIRS 109003]. 
Table E-9. Comparison for Equivalence of USW WT-1 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
440-450/134.1-137.2 440-450 1065.4/445 
500-510/152.4-155.4 500-510 1047.1/505 
550-560/167.6/170.7 550-560 1031.8/555 
640-650195.1/198.1 640-650 1004.4/645 
690-700/210.3-213.4 690-700 989.2/695 
780-790/237.7-240.8 780-790 961.7/785 
840-850/256.0-259.1 840-850 943.4/845 
930-940/283.5-286.5 930-940 916.0/935 
1000-1010/304.8-307.8 1000-1010 894.7/1005 
1090-1100/332.2-335.3 1090-1100 867.2/1095 
1160-1170/353.6-356.6 1160-1170 845.9/1165 
1220-1230/371.9-374.9 1220-1230 827.6/1225 
1300-1310/396.2-399.3 1300-1310 * 
1320-1330/402.3-405.4 1320-1330 * 
1340-1350/408.4-411.5 1340-1350 * 
1380-1390/420.6-423.7 1380-1390 778.9/1385 
1410-1420/429.8-432.8 1410-1420 769.7/1415 
1470-1480/448.1-451.1 1470-1480 * 
1510-1520/460.2-463.3 1510-1520 * 
1550-1560/472.4-475.5 1550-1560 * 
1570-1580/478.5-481.6 1570-1580 * 
*Exclusion of these analyses is documented in Table F-5. 
USW WT-2 
As shown in table E-10, the USW WT-2 sample analyses in DTN: MO0101XRDDRILC.001 
[DIRS 169517] are derived completely and exclusively from DTN: LADB831321AN98.002 
[DIRS 109003]. Inputs to the Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]) in 
DTN: LA9908JC831321.001 [DIRS 113495] are also derived from 
DTN: LADB831321AN98.002 [DIRS 109003]. One sample number (shown in bold type, 
corresponding to the sampling depth interval in feet) in DTN: LADB831321AN98.002 
[DIRS 109003] is incorrect, and the error is also present in DTN: MO0101XRDDRILC.001 
[DIRS 169517]. The sample number, 420-450, does not conform to the typical 10 foot 
sampling interval for drill cuttings. Notebook entries documenting the collection, description, 
and receipt of the USW WT-2 samples from the USGS Core Library confirm that the correct 
footage for the sample is 420-430 ft (Broxton 1990 [DIRS 169640], p. 58; Caporuscio 1986 

[DIRS 169644], p. 128). The substitution of 450 for 430 appears to have been derived from 
a typographical error in Bish and Vaniman (1985 [DIRS 101196], p. 29) that was repeated in 
Bish and Chipera (1989 [DIRS 101195], p. 49). The conversion from depth in feet to depth in 
meters in the source documents and in the DTNs reflects the correct footage. The midpoint 
elevation for the sample used in DTN: LA9908JC831321.001 [DIRS 113495] also is correct. 
Table E-10. Comparison for Equivalence of USW WT-2 Analyzed Samples Included in Data Packages 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
250-260/76.2-79.2 250-260/76.2-79.2 1223.2/255 
260-270/79.2-82.3 260-270/79.2-82.3 1220.1/265 
290-300/88.4-91.4 290-300/88.4-91.4 1211.0/295 
370-380/112.8-115.8 370-380/112.8-115.8 1186.6/375 
420-4501/128.0-131.0 420-4501/128.0-131.0 1171.4/425 
510-520/155.4-158.5 510-520/155.4-158.5 1143.9/515 
570-580/173.7-176.8 570-580/173.7-176.8 1125.6/575 
650-660/198.1-201.1 650-660/198.1-201.1 1101.3/655 
720-730/219.5-222.5 720-730/219.5-222.5 1079.9/725 
780-790/237.7-240.8 780-790/237.7-240.8 1061.6/785 
850-860/259.1-262.1 850-860/259.1-262.1 1040.3/855 
930-940/283.5-286.5 930-940/283.5-286.5 1015.9/935 
990-1000/301.8-304.8 990-1000/301.8-304.8 997.6/995 
1060-1070/323.1-326.1 1060-1070/323.1-326.1 976.3/1065 
1130-1140/344.4-347.5 1130-1140/344.4-347.5 955.0/1135 
1190-1200/362.7-365.8 1190-1200/362.7-365.8 936.7/1196 
1200-1210/365.8-368.8 1200-1210/365.8-368.8 933.6/1205 
1250-1260/381.0-384.0 1250-1260/381.0-384.0 918.4/1255 
1300-1310/396.2-399.3 1300-1310/396.2-399.3 903.1/1305 
1360-1370/414.5-417.6 1360-1370/414.5-417.6 884.8/1365 
1420-1430/432.8-435.9 1420-1430/432.8-435.9 866.6/1425 
1450-1460/442.0-445.0 1450-1460/442.0-445.0 857.4/1455 
1470-1480/448.1-451.1 1470-1480/448.1-451.1 851.3/1475 
1520-1530/463.3-466.3 1520-1530/463.3-466.3 836.1/1525 
1570-1580/478.5-481.6 1570-1580/478.5-481.6 820.8/1575 
1640-1650/499.9-502.9 1640-1650/499.9-502.9 799.5/1645 
1710-1720/521.2-524.3 1710-1720/521.2-524.3 778.2/1715 
1750-1760/533.4-536.4 1750-1760/533.4-536.4 766.0/1755 
1820-1830/554.7-557.8 1820-1830/554.7-557.8 744.6/1825 
1910-1920/582.2-585.2 1910-1920/582.2-585.2 717.2/1915 
2000-2010/609.6-612.6 2000-2010/609.6-612.6 689.8/2005 
2050.25/624.9 (core) 2050.25/624.9 (core) 2 

Table E-10. Comparison for Equivalence of USW WT-2 Analyzed Samples Included in Data Packages 
(Continued) 
MO0101XRDDRILC.001 LADB831321AN98.002 LA9908JC831321.001 
[DIRS 169517] 
(depth interval in ft/m) 
[DIRS 109003] 
(depth interval in ft) 
[DIRS 113495] 
(midpoint elevation in m/depth in ft) 
2053.7/626 (core) 2053.7/626 (core) 2 
2059.3/627.7 (core) 2059.3/627.7 (core) 2 
1The original collection and sample receipt notebook entries identify the footage of this sample as 420-430 ft. See 
2text for further explanation. 
These analyses from MO0101XRDDRILC.001 [DIRS 169517] were also included in LA9908JC831321.001 
[DIRS 113495] but were assigned to slightly different depths, as explained Table F-6). 
E3.2 SAMPLE COLLECTION AND PREPARATION FOR XRD ANALYSIS 
E3.2.1 Samples from J-12 
The J-12 water well was spudded on August 4, 1957, and completed to a depth of 887 ft on 
October 9, 1957 (Fenix and Scisson, Inc., 1988 [DIRS 169661]). Records from 
Burns & McDonnell Engineering Company (1959 [DIRS 169662]) indicate that the hole was 
cased for the full 887 ft depth. The hole was recompleted in August 1968, to a depth of 1,139 ft. 
Fenix and Scisson, Inc. (1988 [DIRS 169661]) found that drilling records were not available for 
the deepening of the hole. Of the 12 sample analyses for J-12 samples contained in 
DTN: MO0101XRDDRILC.001 [DIRS 169517], 5 samples are from depths corresponding to 
the initial well completion, and the remaining 7 samples are from depths associated with the 
deepening of the well. 
In the course of this qualification activity, no records were found to document LANL sampling 
of the J-12 cuttings, sample descriptions, or sample preparation for XRD analysis. Based on the 
lack of documentation and the fact that the J-12 data were not used in the Mineralogic Model 
(MM3.0) (BSC 2004 [DIRS 170031]), a decision was made to not qualify this data set. 
E3.2.2 Samples from Other Drill Holes 
Records of sample collection, sample description, and sample preparation and analysis by XRD 
are partial to complete for samples from drill holes USW H-3, USW H-4, USW H-5, USW 
WT-1, USW WT-2, and UE-25 p#1. Table E-11 summarizes the records that have been 
identified. As noted by Steinborn (2002 [DIRS 160702]), it is likely that more records exist. 

Table E-11. Summary of Records for Sample Collection and XRD Analysis 
Drill Hole Sample Collection and Description Records 
Sample Preparation and XRD 
Analysis Records 
USW H-3 Byers 1982 [DIRS 169663]; Vaniman 1983 [DIRS 
169665], p. 84-85; Levy 1996 [DIRS 106676], p. 
1,4,21-23; LANL 1997 [DIRS 169667] 
Levy 1996 [DIRS 106676], p. 4,10,23; Bish 
2001 [DIRS 169750], p. 46 
USW H-4 Vaniman 1983 [DIRS 169665], p. 85-87; Levy 
1996 [DIRS 106676], p. 23; Caporuscio, 1986 
[DIRS 169644], p. 135 
Levy 1996 [DIRS 106676], p. 4,10 
USW H-5 Vaniman 1988 [DIRS 169665], p. 67-70,72-75,87-
88; Levy 1996 [DIRS 106676], p. 25; Caporuscio 
1986 [DIRS 169644], p. 135; LANL 1992 [DIRS 
169718] 
Levy 1996 [DIRS 106676], p. 10; Bish 2001 
[DIRS 169750], p. 45-46 
USW WT-1 Broxton 1990 [DIRS 169640], p.61-63; 
Caporuscio 1986 [DIRS 169644], p. 127 
no records found 
USW WT-2 Broxton 1990 [DIRS 169640], p. 58-60; 
Caporuscio 1986 [DIRS 169644], p. 128 
no records found 
UE-25 p#1 Caporuscio 1986 [DIRS 169644], p. 131-133; 
Chipera 1986 [DIRS 169680], p.100 
Chipera 1986 [DIRS 169680], 
p.34,36,37,38,39,40,48,49,50, 
52,53,55,56,57,58,59,60,62,63,64,65,66,68,6 
9, 
101,102; Bish 2001 [DIRS 169750], p. 2-5; 
Chipera et al. 1989 [DIRS 169683], p. 2,3 
E3.3 X-RAY DIFFRACTION METHODOLOGY 
X-ray diffraction analysis of the cutting samples, sidewall core samples, and intermittent core 
samples was conducted in the same time period as XRD analysis of samples from the 
continuously cored boreholes. The same analytical equipment and software were used. 
Documentation and evaluation of technical procedures found to be acceptable by Steinborn 
(2002 [DIRS 160702]) for qualification of XRD mineralogic data from core samples apply to the 
data under consideration here as well. E-12 identifies specific published sources of information 
about XRD analytical techniques for the borehole data sets considered for qualification. 
Table E-12. Published Sources on XRD Mineralogy for Unqualified Data Sets 
Borehole Published Information Source on XRD Methodology 
UE-25 p#1 Chipera and Bish (1988 [DIRS 105080]) for all samples 
USW H-3 Levy (1984 [DIRS 101392]) for samples 1550, 1655, 1700, 1800, 1900, 2400, 2440, 2490; 
USW H-4 Levy (1984 [DIRS 101392]) for sidewall core samples 
USW H-5 Levy (1984 [DIRS 101392]) for samples 50, 190, 420, 450, 700, 1050, 1610, 1666, 1750, 
1762, 1800, 1852, 1875, 1917, 1930; 
USW WT-1 Bish and Chipera (1989 [DIRS 101195]) for all samples 
USW WT-2 Bish and Chipera (1989 [DIRS 101195]) for all samples 

E3.4 DATA CORROBORATION ANALYSIS 
The comparison of mineral abundance data between boreholes with unqualified cuttings and 
boreholes with qualified core was conducted with restrictions imposed by the nature of 
mineralogic variation within the study area and by certain characteristics of the data sets. 
Variations in mineralogy across the Yucca Mountain area reflect primary compositional 
differences between consecutive ash flows. Lateral and vertical variability in pyroclastic 
constituents of ash flows and in postdepositional cooling and crystallization history also affects 
the mineralogy. Superimposed upon the variations in primary pyroclastic and syngenetic 
(high-temperature postdepositional crystallization) mineralogy are mineralogic changes due to 
alteration. The most important alteration was zeolitic alteration of originally glassy tuff. 
Distribution of zeolitic alteration was affected by multiple episodes of tectonic tilting and past 
variations in the position of the water table. A conceptual model that explains the 
interrelationship between these factors and the distribution of zeolitization is given by Levy 
(1991 [DIRS 100053]). 
Paired borehole mineralogy data sets of qualified core data and unqualified cuttings (plus 
sidewall core and intermittent core) data were selected on the basis of physical proximity plus 
comparable ground-level elevations of boreholes. These criteria were applied to minimize the 
effects of lateral and vertical mineralogical variability on the comparisons of mineralogy data 
sets. The comparisons of mineralogical data were made for individual lithostratigraphic units or 
successions of units, based on unit contacts from DTN MO0004QGFMPICK.000 
[DIRS 152554]. This qualified DTN contains unit contacts for both the boreholes with qualified 
core and those with unqualified cuttings. Unit descriptions and abbreviations are listed in E-13. 
The locations of unit contacts in boreholes without continuous core were determined more from 
downhole logs than from cuttings data. Most of the contacts are located within ten-foot intervals 
represented by cuttings samples. These samples would therefore contain material from two 
adjacent lithostratigraphic units. In addition, some cuttings, sidewall, or intermittent core 
samples located near unit boundaries have mineralogic affinities to an adjacent unit rather than 
the unit to which they would belong simply on the basis of sample footage location. This 
phenomenon was recognized by the authors of the BSC report (2004 [DIRS 170031]), who 
omitted some sample data from their model or adjusted the footage of some samples to avoid 
placing mineralogic data in an inappropriate lithostratigraphic unit. Unit contacts in cored holes 
are not free of these potential discrepancies because contacts in cored holes may have been 
selected more on the basis of rock texture than of mineralogy. In this report, such discrepancies 
are addressed on an individual basis. 
Mineral-abundance data for lithostratigraphic units are presented as ranges bounded by the 
highest and lowest values. In general, neither the qualified nor the unqualified data sets contain 
enough analyses from any single lithostratigraphic unit to justify statistical comparisons of mean 
or standard deviation between borehole data sets. Borehole cuttings data sets typically contain 
four or fewer analyses for each unit. 
The listings of individual samples used for comparison between unqualified and qualified data 
sets are contained in Appendices F (unqualified data) and G (qualified data). Analyses of cavity 
fillings, fracture fillings, and inclusions were omitted from the comparisons because these 
samples do not represent the bulk mineralogy of the lithostratigraphic units in which they were 

collected. Calcite-rich samples in the qualified data sets were omitted from the comparisons. In 
tables of this section where data are presented as ranges of mineral content, zeroes are substituted 
for not detected symbols present in the original DTN tables and in the appendices of this 
report. Symbols for approximate mineral content (e.g., ~1), also present in some of the original 
DTN tables in this section. In some comparison tables, mineral abundances for feldspar and 
silica minerals (tridymite, quartz, cristobalite, and opal) from the appendix have been combined 
to simplify the comparisons. 
Lithostratigrahic units and their abbreviations, as used in this report, are listed in Table E-13. 
The unit names and boundaries are taken from DTN: MO0004QGFMPICK.000 [DIRS 152554]. 
The units are listed in order from lesser to greater depths and from younger to older ages. The 
term, crystallized, as used in the lithostratigraphic unit descriptions, has a meaning equivalent 
to the term devitrified, which is in more common usage in the petrographic descriptions of 
core and cutting samples cited in this report. Devitrification refers here to the high-temperature 
crystallization of glassy tuffs to mineral assemblages dominated by anhydrous silica minerals 
and feldspar. 
Table E-13. Lithostratigraphic Unit Descriptions and Abbreviations 
Unit 
Abbreviation Lithostratigraphic Unit Description 
Tpc_un Tiva Canyon Tuff nondivided 
Tpcpv3 Tiva Canyon Tuff, crystal-poor vitric densely welded subzone 
Tpcpv2 Tiva Canyon Tuff, crystal-poor vitric moderately welded subzone 
Tpcpv1 Tiva Canyon Tuff, crystal-poor vitric nonwelded to partially welded subzones 
Tpbt4 Pre-Tiva Canyon Tuff bedded tuff 
Tpy Yucca Mountain Tuff nondivided 
Tpbt3 Pre-Yucca Mountain Tuff bedded tuff 
Tpp Pah Canyon Tuff nondivided 
Tpbt2 Pre-Pah Canyon Tuff bedded tuff 
Tptrv3 Topopah Spring Tuff crystal-rich vitric nonwelded to partially welded zones 
Tptrv2 Topopah Spring Tuff crystal-rich vitric moderately welded zone 
Tptrv1 Topopah Spring Tuff crystal-rich vitric densely welded zone 
Tptrn Topopah Spring Tuff crystal-rich nonlithophysal zone 
Tptrl Topopah Spring Tuff crystal-rich lithophysal zone 
Tptpul Topopah Spring Tuff crystal-poor upper lithophysal zone 
Tptpmn Topopah Spring Tuff crystal-poor middle nonlithophysal zone 
Tptpll Topopah Spring Tuff crystal-poor lower lithophysal zone 
Tptpln Topopah Spring Tuff, crystal-poor lower nonlithophysal zone 
Tptpv3 Topopah Spring Tuff, crystal-poor vitric densely welded subzone 
Tptpv2 Topopah Spring Tuff, crystal-poor vitric moderately welded subzone 
Tptpv1 Topopah Spring Tuff, crystal-poor vitric nonwelded to partially welded subzones 
Tpbt1 Pre-Topopah Spring Tuff bedded tuff 
Tac Calico Hills Formation undifferentiated 

Table E-13. Lithostratigraphic Unit Descriptions and Abbreviations (Continued) 
Unit 
Abbreviation Lithostratigraphic Unit Description 
Tacbt Pre-Calico Hills Formation bedded tuff 
Tcpuv Prow Pass Tuff upper vitric (zeolitic) nonwelded to partially welded zones 
Tcpuc Prow Pass Tuff, upper crystallized nonwelded to partially welded zones 
Tcpm Prow Pass Tuff, crystallized moderately to densely welded zones 
Tcplc Prow Pass Tuff, lower crystallized nonwelded to partially welded zones 
Tcplv Prow Pass Tuff, lower vitric (zeolitic) nonwelded to partially welded zones 
Tcpbt Pre-Prow Pass Tuff bedded tuff 
Tcbuv Bullfrog Tuff upper vitric (zeolitic)nonwelded to partially welded zones 
Tcbuc Bullfrog Tuff, upper crystallized nonwelded to partially welded zones 
Tcbm Bullfrog Tuff, crystallized moderately to densely welded zones 
Tcblc Bullfrog Tuff, lower crystallized nonwelded to partially welded zones 
Tcblv Bullfrog Tuff, lower vitric (zeolitic) nonwelded to partially welded zones 
Tcbbt Pre-Bullfrog Tuff bedded tuff 
Tctuv Tram Tuff, upper vitric (zeolitic) nonwelded to partially welded zones 
Tctuc Tram Tuff, upper crystallized nonwelded to partially welded zones 
Tctm Tram Tuff, crystallized moderately to densely welded zones 
Tctlc Tram Tuff, lower crystallized nonwelded to partially welded zones 
Tctlv Tram Tuff, lower vitric (zeolitic) nonwelded to partially welded zones 
Tctbt Pre-Tram Tuff bedded tuff 
Tund lower Tertiary units undifferentiated 
Pz Paleozoic and older units 
Source: DTN: MO0004QGFMPICK.000 [DIRS 152554]. 
UE-25 p#1 Compared with UE-25 UZ#16, USW SD-12, USW G-4, and USW G-1 
For purposes of corroboration, the mineralogical data from the upper part of p#1 were compared 
with qualified data from UE-25 UZ#16. This is the borehole closest to p#1 that has qualified 
data. The data are compared in Table E-14. Mineralogic data from the lower part of p#1 are 
compared with qualified data from USW G-1. 
For the devitrified units of the Topopah Spring Tuff, ranges of mineralogic abundance in both 
boreholes generally overlap. The absence of either glass or major hydrous alteration products 
(smectite or zeolites) in the devitrified units is a common characteristic of both data sets. 
One cutting sample from the 1240-1250 ft depth in p#1 straddles the boundary between Tptpv2 
and Tptpv1 at 1243 ft. Data for this sample are listed in Table E-14 as Tptpv2/1. A downward 
transition from vitric to zeolitic tuff is located within the Tptpv2 + Tptpv1 interval in both 
boreholes. In UZ#16, the transition lies within Tptpv2. The transition cannot be specified as 
lying within Tptpv2 or Tptpv1 in p#1, but the absence of glass in the sample suggests the 
transition is within Tptpv2, in agreement with the UZ#16 data. 

Rock Properties Model 
MDL-NBS-GS-000004 REV 01 E-21 September 2004 
Table E-14. Mineral-Abundance Ranges, UE-25 p#1 and UE-25 UZ#16 (Weight Percent) 
Unit 
Smectite 
 Mordenite Clinoptilolite Tridymite Quartz 
Cristobalite 
 Opal Feldspar Glass 
p#1 UZ16 p#1 UZ16 p#1 UZ16 p#1 UZ16 p#1 UZ16 p#1 UZ16 p#1 UZ16 
Tptrn 0 Tr.-5 0 0-0 14 3-22 0 0-0 6 5-13 76 64-81 0 0-0 
Tptpul 2-3 4-6 0-0 0-0 Tr.-4 1-16 3-9 2-11 24-25 17-29 54-71 54-58 0-0 0-0 
Tptpmn 2 3-4 0 0-0 Tr. Tr.-4 30 6-23 3 13-27 67 54-57 0 0-0 
Tptpll 1-2 1-6 0-0 0-0 0-0 1-7 27-36 0-36 1-5 5-26 59-61 50-58 0-0 0-0 
Tptpln 0 2-6 0 0-0 0 0-5 27 14-31 5 8-23 67 45-57 0 0-0 
Tptpv2 2 10 0 2 20 17 49 
Tptpv2/1 1 54 0 Tr. 20 24 0 
Tptpv1 1 1 62 80 0 0 Tr. 3 23 13 14 8 0 0 
Tac Tr.-10 1-19 34-56 38-72 0-0 0-0 2-17 3-9 0-22 10-26 21-39 6-19 0-9 0-0 
Tacbt 12 5-9 0 40-42 0 0-0 24 8-11 0 20-27 40 18-27 22 0-0 
Tcpuc 39 3-8 0 0-0 0 0-0 25 17-41 0 0-20 40 48-58 0 0-0 
Tcpm 0 3-4 3 0-0 0 0-0 39 24-39 0 0-14 60 58-62 0 0-0 
Tcplc 5 6-7 0 0-0 0 0-0 37 11-20 0 15-27 58 55-56 0 0-0 
Tcplv 3-7 3 40-60 42 0-0 0 4-11 5 14-17 28 17-25 28 0-0 0 

A p#1 sidewall sample from 1,470 ft falls within the devitrified Tcpuc lithostratigraphic unit two 
feet below the boundary with the overlying vitric Tcpuv unit. This sample contains 39 percent 
mordenite plus minor smectite and therefore must have been partly vitric prior to the 
hydrous-mineral alteration. The mineralogy of the sample therefore represents a transition 
between Tcpuv and Tcpuc. The uppermost Tcpuc sample from UZ#16 contains 8 percent 
mordenite, which is the upper end of the smectite + mordenite range for UZ#16 samples in the 
Tcpuc and the only sample in that lithostratigraphic unit that contains mordenite. This similarity 
is corroborative of the location and mineralogy of the p#1 sample. 
The single p#1 sample analysis from the Tptpmn unit falls outside the range of UZ#16 values for 
the major constituents quartz, cristobalite, and feldspar. In Table E-15, this analysis is compared 
to mineral-abundance ranges for tridymite, quartz, cristobalite, and feldspar in the Tptpmn unit 
of three qualified borehole data sets in addition to UZ#16. The feldspar content of the p#1 
sample is similar to values in USW G-4 but the cristobalite content falls far below the ranges of 
the other boreholes and the quartz content is slightly to substantially higher. Byers (1983 
[DIRS 169692]) described a thin section of this sample (570-580 ft depth, identified in his 
unpaginated notebook as P1-580) as a partially welded, devitrified ash-flow tuff. The Tptpmn 
unit is described by Buesch et al. (1996 [DIRS 100106]) as densely welded, devitrified tuffs. 
Based on the welding difference, it appears that the 570-580 ft sample does not represent the 
lithology of the Tptpmn unit. No documentation or sample remainder was found that could 
explain the origin of this atypical material. Therefore, it will be recommended that the data for 
this material be excluded from qualification. 
Table E-15. Ranges of Mineral Content in the Tptpmn of p#1, UZ#16, SD-12, and G-4 (Weight Percent) 
Borehole Tridymite Quartz Cristobalite/Opal Feldspar 
UE-25 p#1 Tr. 30 3 67 
UE-25 UZ#16 Tr.-4 6-23 13-27 54-57 
USW SD-12 2-4 3-11 26-34 52-54 
USW G-4 0-17 3-4 13-28 62-68 
There is a substantial difference in the values for quartz and cristobalite/opal between the single 
p#1 sample and the UZ#16 samples in the Tcplc unit. The p#1 1,640-1,650-ft sample values 
(Table E-15) fall within the ranges for quartz and cristobalite/opal in the overlying Tcpm unit of 
both p#1 and UZ#16. It is noteworthy that the lowest analyzed sample in the UZ#16 Tcpm unit 
has quartz and cristobalite/opal values that are more typical of the underlying Tcplc unit. The 
larger UZ#16 data set shows a downward change from quartz much greater than cristobalite/opal 
to quartz less than or equal to cristobalite/opal that is offset by about 20 ft above the unit 
boundary between Tcpm and Tcplc. In p#1, the same mineralogic change is present but is offset 
by about 10 ft below the unit boundary. Therefore, this particular discrepancy between the two 
mineralogy data sets exists because the lithostratigraphic unit boundaries were not picked on the 
basis of the mineralogic change. The p#1 data for the Tcpm and Tcplc units are corroborated by 
the UZ#16 data. 

Mineralogic data for the lower part of p#1, with the exception of Paleozoic-section samples, are 
compared with data from USW G-1 in Table E-16. Common aspects of both data sets are the 
presence of only small amounts of clay and zeolite in the devitrified units Tcbuc, Tcbm, Tcblc 
(not recognized in USW G-1), Tctuc, and Tctm. The originally vitric units Tcpbt, Tcblv, Tcbbt, 
Tctuv, and Tctlv have clay and zeolites as major constituents. 
Sample p#1 2030-2040, in the Tcbm unit, is listed as containing seven percent glass. The 
presence of glass in the middle of approximately equal to 300 ft thick devitrified interval 
(Tcbuc-Tcbm-Tcblc) is unlikely. No thin section of this sample is available for examination. In 
the absence of information to support the presence of glass in this sample, it will be 
recommended that the sample not be qualified. 
The lowermost Tertiary unit, Tund, combines multiple pyroclastic deposits. In p#1, the Tund 
unit is 1,217 ft thick, whereas in G-1 it is 2,442 ft from the top of the unit to the depth of the 
lowest analyzed sample. This large difference of thickness precludes the use of a side-by-side 
mineralogic-abundance comparison between data sets for this unit. Instead, the comparison is 
based on general mineralogic similarities and on the vertical variations in illite/smectite 
interstratification in p#1 and G-1. 
Tund samples in p#1 and G-1 are similar in the lack of tridymite and cristobalite/opal. The only 
exception is one sample from near the top of Tund in G-1 that contains 6 percent 
cristobalite/opal. Chlorite is generally absent from rocks above the Tund and the upper Tund, 
but is a common constituent in trace to minor quantities in the lower Tund of both borehole 
data sets. 
Investigations of illite/smectite interstratification in p#1 and G-1 are documented in Chipera and 
Bish (1988 [DIRS 105080]) and Bish (1989 [DIRS 101194]). These references describe the 
increasing proportions of illite with depth and its inferred correlation with increasing 
temperature. Ordering of the interstratifications changes with depth from random to more highly 
ordered. Chipera and Bish (1988 [DIRS 105080], p. 17) compared the suites of illite/smectite 
samples for p#1 and G-1 (also G-2), most of which represent the Tund, and found similar trends 
with increasing depth. They attributed the similarities to similar alteration histories. In 
particular, an inferred early higher-temperature alteration event with temperatures increasing 
with depth was recognized in both data sets. This suggests that the p#1 sample set reflects good 
depth control. 
No qualified data sets exist for comparison with the three lowermost samples from 
p#1 representing the Paleozoic section. Therefore, these samples cannot be qualified by 
corroboration with qualified data. The Paleozoic section is not part of the Mineralogic Model 
(3.0) (BSC 2004 [DIRS 170031]) and the data for these three samples are not used as input to the 
model. 

Rock Properties Model 
Table E-16. Mineral-Abundance Ranges, UE-25 p#1 and USW G-1 (Weight Percent) 
Unit 
Smectite 
 Mordenite 
Clinoptilolite 
 Analcime Tridymite Quartz 
Cristobalite 
Opal Feldspar Glass 
p#1 G-1 p#1 G-1 p#1 G-1 p#1 G-1 p#1 G-1 p#1 G-1 p#1 G-1 
Tcpbt 24 9 45 33 0 0 12 2 0 8 18 41 0 0 
Tcbuc Tr.-5 1-3 0-9 0-0 0-0 0-0 27-45 32-35 0-0 Tr.-1 39-64 62-67 0-0 0-0 
Tcbm Tr.-2 Tr.-1 0-0 0-0 0-0 0-0 31-39 32-36 0-0 0-Tr. 59-65 63-67 0-7 0-0 
Tcblc 3 * Tr. * 0 * 29 * 0 * 61 * 0 * 
Tcblv 31 27-39 14 40-48 0 0-0 28 Tr.-3 0 2-10 33 12-20 0 0-0 
Tcbbt 10 14-26 8 26-42 0 0-0 29 12-21 0 1-1 52 17-30 0 0-0 
Tctuv 6-11 4-15 6-35 15-40 0-0 0-0 22-32 4-18 0-0 4-8 32-45 33-52 0-0 0-0 
Tctuc 7 1-3 0 0-0 0 0-0 40 31-36 0 0-1 49 62-64 0 0-0 
Tctm 0-2 1-2 0-0 0-2 0-0 0-0 24-43 30-36 0-0 0-Tr. 51-77 61-67 0-0 0-0 
Tctlv 4-25 4-18 0-13 4-41 0-0 0-0 29-42 18-38 0-0 0-0 34-57 20-43 0-0 0-0 
*Tcblc is not recognized in USW G-1. 
MDL-NBS-GS-000004 REV 01 E-24 September 2004 

USW H-3 Compared with USW GU-3 
Two samples within two feet or less of a lithostratigraphic unit boundary, one from each 
borehole, were reassigned to an adjacent unit based on strong mineralogic affinity to that unit. 
Sample GU-3 1,598.5 falls within the devitrified Tcpuc unit (upper boundary at 1,597 ft depth) 
but contains 45 percent glass. This sample was moved to the overlying vitric Tcpuv for the 
purpose of unit-to-unit mineralogic comparison. Sample H-3 1,700 falls within the devitrified 
Tcplc unit two feet above the boundary with the underlying vitric Tcplv unit. The sample 
contains 15 percent glass and therefore was moved to the underlying unit Tcplv. 
The succession of lithostratigraphic units shown in Table E-17 includes distinctive 
lithologic-unit transitions that correspond to mineralogic transitions. Mineralogic features 
common to both USW H-3 and USW GU-3 data sets include: 
 
Restriction of detectable hornblende to the phenocryst-rich Tacbt. 
 
Transition from Tcpuv to Tcpuc corresponds to a transition from glass bearing to 
nonglass-bearing mineral assemblages. 
 
Transition from Tcplc to Tcplv corresponds to a transition from nonglass-bearing 
mineral assemblages to assemblages containing either glass or clinoptilolite derived 
from the alteration of glass. 
The raw-value ranges of mineral abundances within lithostratigraphic units are generally similar 
in USW H-3 and USW-GU-3. One difference is observed in the Tptpln unit. The H-3 samples 
have cristobalite heavily predominant over quartz, whereas the GU-3 samples show the opposite 
pattern. Both Tptpln sample sets are small, with two H-3 samples and three GU-3 samples. The 
cristobalite greater than quartz pattern in the Tptpln unit is uncommon within the qualified data 
sets, but similar patterns are present in two samples from the lower Tptpln in USW SD-12 where 
this unit was very well sampled. Nonoverlapping mineral-abundance ranges exist in the 
Tptpv3 data, but this unit typically shows high variability in mineral abundances. The 
mineralogic differences between the single H-3 sample and the GU-3 samples are attributable to 
a higher glass content in the H-3 sample. 

Rock Properties Model 
Table E-17. Mineral-Abundance Ranges, USW H-3 and USW GU-3 (Weight Percent) 
Unit Horn-blende Clinoptilolite Tridymite Quartz 
Cristobalite 
Opal Feldspar Glass 
H-3 GU-3 H-3 GU-3 H-3 GU-3 H-3 GU-3 H-3 GU-3 H-3 GU-3 H-3 GU-3 
Tptrn 0-0 0-0 0-0 0-0 Tr.-13 4-20 0-0 0-4 8-24 5-20 76-77 70-80 0-0 0-0 
Tptpul 0-0 0-0 0-0 0-0 Tr.-21 5-12 0-0 0-2 5-29 22-22 71-73 65-70 0-0 0-0 
Tptpmn 0-0 0-0 0-0 0-0-0-Tr. 0-6 2-4 1-6 26-27 17-22 68-69 65-70 0-0 0-0 
Tptpll 0-0 0-0 0-0 0-0 0-4 0-10 Tr.-29 4-17 3-24 12-27 66-72 65-75 0-0 0-0 
Tptpln 0-0 0-0 0-0 0-0 0-Tr. 0-0 2-2 17-35 23-29 3-10 67-74 60-70 0-0 0-0 
Tptpv3 0 0-0 0 0-0 0 0-0 2 3-8 7 17-27 19 35-35 70 30-40 
Tptpv2 0 0 0 0 0 0 2 4 5 12 23 40 70 45 
Tptpv1 0 0-0 0 0-0 0 0-0 Tr. 4-7 Tr. 6-7 3 25-30 97 55-65 
Tacbt 0-1 0-2 0-1 0-2 0-0 0-0 Tr-3 5-17 1-5 4-6 11-47 25-50 34-81 20-65 
Tcpuv 0 0-0 1 0-0 4 0-0 12 4-5 20 2-5 50 45-45 10 45-45 
Tcpuc 0 0-0 2 0-0 7 0-15 10 2-20 7 2-30 70 65-70 0 0-0 
Tcpm 0 1 2 0 7 0 7 12 7 12 75 70 0 0 
Tcplv 0-0 0-0 2-60 30-70 0-0 0-0 7-10 4-7 0-15 4-7 30-60 19-40 0-15 0-38 
Tcpbt 0 0 75 60 2 0 2 4 2 10 15 25 0 0 
Tcblv 0-1 0-0 65-75* 40-50 0-0 0-0 2-2 2-5 2-2 5-7 15-30 35-45 0-0 0-0 
Tctuv 0 0-0 70* 30-40* 0 0-0 2 5-7 0 8-10 20 40-50 0 0-0 
*includes mordenite. 
MDL-NBS-GS-000004 REV 01 E-26 September 2004 

USW H-4 Compared with USW SD-12, UE-25 UZ#16, and USW G-4 
Examination of the mineralogical data for H-4 and the three closest drill holes with qualified data 
revealed considerable variability. Because of this variability, it was determined that comparisons 
of data for the purpose of corroboration would have to include some input from all 
three qualified data sets. 
Preliminary examination of H-4 mineralogical data organized by lithostratigraphic unit identified 
one potential outlier sample analysis. The XRD analysis of Calico Hills Formation (Tac) sample 
H-4 1320-1330 indicates that it is composed exclusively of crystalline silica (dominantly quartz) 
and feldspar. This mineralogic composition closely matches the compositions of devitrified tuffs 
in the Prow Pass Tuff (e.g., H-4 1710-1720) and other devitrified tuffs. The Calico Hills 
Formation is not known to contain any fully devitrified intervals at Yucca Mountain, but it 
typically contains lithic clasts of densely welded, devitrified tuff in a matrix of glassy clasts or 
zeolitized glassy clasts. Moyer and Geslin (1995 [DIRS 101269]) specify the lithic-clast content 
of the upper part of the Calico Hills Formation as less than or equal to 5 percent, with 
presumably higher contents in localized lithic-clast swarms. 
At the time the H-4 1320-1330 sample was collected, the variations in lithology and mineralogy 
of the Calico Hills Formation were poorly known. The USGS drill hole report for USW H-4 
describes the Tuffaceous beds of Calico Hills (informal usage) as nonwelded to partly welded, 
devitrified and zeolitized, with possible silicification of the upper 11 m. The tuff is described as 
containing abundant rhyolitic lithic fragments (Whitfield et al. 1984 [DIRS 101366], p. 7). The 
notebook entry for the 1,320-1,330 ft sampling interval made at the time the LANL sample was 
collected from materials at the USGS Core Library describes it as silicified tuff of unknown 
origin (Vaniman 1983 [DIRS 169665]). The remnant of this sample was examined under the 
binocular microscope at LANL for the current investigation. It contained subequal portions of 
lithic clasts (dark densely welded, devitrified tuff) and zeolitic nonwelded tuff plus vitric 
nonwelded tuff. The aliquot of this sample submitted for XRD apparently contained little or 
none of the nonwelded tuff because the analysis shows no zeolite or glass content. Existing 
documentation of the dominant lithology of this rock unit suggests it is very unlikely that the 
source rock could have been composed only of devitrified tuff. Even a lithic-clast swarm, 
sampled over a 10 ft interval, would not yield a sample that is exclusively devitrified tuff. Based 
on the information cited here, H-4 1320-1330 data are omitted from the comparison of H-4 data 
and qualified mineralogical data and a recommendation will be made that the data for this 
sample not be qualified. 
One sidewall sample, H-4 1656, nominally falls within Tcpuv but was reassigned to a lower unit 
whose boundary is six feet below the sample collection depth. The boundary separates vitric 
Prow Pass Tuff (Tcpuv) from underlying devitrified tuff (Tcpuc). Sample H-4 1656 is 
devitrified, but its cryptocrystalline texture differs from the coarser texture of a sample lower in 
the Tcpuc (Levy 1984 [DIRS 101392], p. 21) in ways that suggest it must be close to the 
vitric_-devitrified boundary. 

Table E-18 compares the contents of the most abundant rock-forming minerals in the Tptpln of 
H-4 and three qualified borehole data sets. This lithostratigraphic unit consists of densely 
welded, devitrified tuff. The Tptpln intervals in all four boreholes are similar in containing little 
or no tridymite. The intervals are most notably different in their feldspar contents. H-4 and G-4 
have very similar ranges of feldspar content. SD-12 and UZ#16 also have very similar ranges of 
feldspar content, but these ranges do not overlap with the ranges for H-4 and G-4. The 
differences in feldspar content are matched by greater or lesser amounts of quartz or 
cristobalite/opal, although the abundance ranges for the two silica minerals show substantial 
overlap among all four data sets. This comparison show that the abundance ranges of tridymite, 
quartz, and cristobalite/opal in the three qualified borehole data sets are corroborative of the data 
from H-4. The feldspar data show that genuine variability exists among the qualified data sets 
but that at least one of the data sets is a good corroborative match to the H-4 data. 
Table E-18. Ranges of Mineral Content in the Tptpln of H-4, SD-12, UZ#16, and G-4 (Weight Percent) 
Borehole Tridymite Quartz Cristobalite/Opal Feldspar 
USW H-4 0-0 6-15 18-23 64-70 
USW SD-12 1-3 7-24 15-30 53-57 
UE-25 UZ#16 0-5 14-31 8-23 45-57 
USW G-4 0-0 2-25 13-24 60-69 
Mineralogic changes across unit boundaries are documented and compared in Table E-19. 
Characteristic mineralogic changes across boundaries, observed in H-4 plus at least one of SD-12 
and UZ#16, include the change from assemblages dominated by feldspar plus silica in the 
Tptprn-Tptpln to an assemblage with glass plus minor smectite and clinoptilolite in the Tptpv3. 
The Tpbt1-Tac-Tacbt sequence in the three drill holes consists of originally vitric tuffs in which 
the glass has been slightly to completely altered to clinoptilolite and smectite, with or without 
mordenite. The data for SD-12 and UZ#16 show that there is considerable lateral variability in 
the degree of alteration within specific units. The H-4 data for Tpbt1 more closely resemble 
SD-12 (persistence of glass), whereas the H-4 data are more similar to UZ#16 data in the Tac 
(complete alteration of glass). The single Tacbt sample from H-4 has alteration (presence of 
clinoptilolite, absence of glass) that generally falls within or close to the highly variable ranges 
of alteration in SD-12 and UZ#16. The values for feldspar+silica that are generally higher in the 
Tacbt than in the Tac for all three drill holes reflect the higher phenocryst and lithic-clast content 
of the Tacbt (Vaniman et al. 1984 [DIRS 101363], p. 24-25). 
The mineralogic changes from Tcpuv downward to the devitrified sequence Tcpuc-Tcpm-Tcplc 
are the disappearance of clinoptilolite and the appearance of a mineral assemblage dominated by 
feldspar plus silica. For these changes, the H-4 data are consistent with the data from SD-12 and 
UZ#16. The downward change from the devitrified units to the Tcplv is marked in all three drill 
holes by the reappearance of clinoptilolite. 

Rock Properties Model 
MDL-NBS-GS-000004 REV 01 E-29 September 2004 
Table E-19. Mineral-Abundance Ranges, USW H-4, USW SD-12, and UE-25 UZ#16 (Weight Percent) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
H-4 SD-12 UZ#16 H-4 SD-12 UZ#16 H-4 SD-12 UZ#16 H-4 SD-12 UZ#16 
Tptprn 0 0-2 Tr.-5 0 0-0 0-0 96 95-100 94-103 0 0-0 0-0 
Tptpul 2-2 3-5 4-6 0-0 0-0 0-0 97-98 92-98 90-96 0-0 0-0 0-0 
Tptpmn 2 3-5 3-4 0 0-0 0-0 98 93-96 91-99 0 0-0-0-0 
Tptpll 1-3 2-5 1-6 0-0 0-0 0-112 95-99 93-98 88-100 0-0 0-0 0-0 
Tptpln 1-2 0-4 2-6 0-0 0-1 0-142 97-99 92-98 84-97 0-0 0-0 0-0 
Tptpv3 0-20 0-2 0-73 0-5 0-0 0-4 30-49 11-18 8-23 30-70 72-85 0-82 
Tptpv2 No samples in H-4 data set 
Tptpv1 No samples in H-4 data set 
Tpbt1 2 2 81 5 1 83 42 12 11 50 85 0 
Tac 0-251 0-3 1-191 29-78 1-10 38-72 18-52 9-35 22-54 0-0 58-89 0-0 
Tacbt 0 3-111 5-91 31 12-68 40-42 61 22-78 53-58 0 0-62 0-0 
Tcpuv 0 7 10 58 90 39 0 0 
Tcpuc 0-3 Tr.-5 3-81 0-0 0-0 0-0 96-102 89-99 85-100 0-0 0-0 0-0 
Tcpm 
Tcplc 
Tcplv 0-231 2-151 3 11-22 10-65 42 63-77 38-84 61 0-0 0-0 0 
NOTE: ------ = no samples analyzed from this unit. 
Tr. = trace amount present. 
Blanks are intentional. 
1 
2The zeolite present is stellerite. 
Includes mordenite in at least one sample. 

USW H-5 Compared with UE-25 UZ#16 and USW GU-3 
The dominant mineralogy of the Tpc_un unit of the Tiva Canyon Tuff in USW H-5 is compared 
with the mineralogy of two qualified data sets in Table E-20. For this devitrified-rock interval, 
there is a very close similarity in the raw-data ranges of mineral abundance between H-5 and 
GU-3. Comparison of H-5 data with data from UZ#16 shows a general similarity of mineralogic 
content, although UZ#16 has higher cristobalite/opal content and lower feldspar content. 
Table E-20. Ranges of Mineral Content in the Tpc_un of H-5, UZ#16, and GU-3 (Weight Percent) 
Borehole Tridymite Quartz Cristobalite/Opal Feldspar 
USW H-5 2-31 0-6 0-29 66-80 
UE-25 UZ#16 Tr.-7 Tr.-4 25-33 57-62 
USW GU-3 0-25 0-2 6-30 70-75 
The H-5 data set contains no samples from Tpcpv3 or Tpcpv2. Mineralogic comparisons of 
units from the Tpcpv1 downward are complicated by large variations in secondary-mineral 
alteration, both within and between boreholes. For units affected by variable alteration, the 
corroboration of H-5 data is based on similarities of primary mineralogic or textural 
characteristics. 
Three samples from H-5 were recognized as having mineralogic compositions similar to the 
units directly above their nominal unit assignments based on depth. These reassignments are 
equivalent to changes made in the Mineralogic Model (MM3.0) (BSC 2004 [DIRS 170031]). 
Sample H-5 1666 falls within the Tptpv2 unit beginning at 1659 ft. The published petrographic 
description of the highly altered sample indicates that it has a relict densely welded texture 
characteristic of the Tptpv3 (Levy 1984 [DIRS 101392], p. 27). It was reassigned to Tptpv3 for 
the purpose of this qualification activity. The same reassignment was made by BSC (2004 
[DIRS 170031]). 
The XRD analysis for sample H-5 1990-2000 contains 59 percent glass even though the recorded 
sample depth falls within the devitrified Tcpuc unit extending from 1,967-2,085 ft depth. This 
discrepancy was addressed in the BSC report (2004 [DIRS 170031]) by reassigning the sample 
to the overlying vitric Tcpuv. Examination of the sample remainder shows it to be composed of 
vitric tuff and devitrified tuff, with the vitric tuff predominant. Given that the nominal sample 
depth is more than 20 ft below the top of the devitrified Tcpuc unit, it is likely that this sample 
contains a very large impurity of vitric tuff from the overlying unit. It will be recommended that 
this sample and its associated XRD analysis be excluded from qualification. 
Sample H-5 2140-2150 was moved from the vitric Tcplv unit to the devitrified Tcplc unit whose 
lower boundary is located at 2,130 ft depth. The sample remainder contains a very small amount 
of residual glassy clasts, but the XRD analysis of the sample shows no glass content. Therefore, 
the sample was moved to Tcplc. 

The comparison of Tpcpv1 mineralogy for H-5 and UZ#16 shows similar smectitic alteration of 
still partly glassy tuff (Table E-21). The Tpy samples differ between the two borehole data sets 
in degree of smectitic alteration, but the samples share high glass content and very low content of 
feldspar and silica phenocrysts. Data for the Tpbt3 in H-5, UZ#16, and GU-3 all show smectitic 
alteration of vitric tuff with highly variable contents of feldspar and silica phenocrysts. The 
Tptrn, Tptrl, Tptpul, and Tptpmn are all devitrified tuff units with minor smectitic alteration. 
Samples from the Tptpll and Tptpln units of UZ#16 are unusual in containing modest amounts of 
the zeolite stellerite but otherwise are similar to the H-5 and GU-3 samples with slight smectitic 
alteration of devitrified tuff. Alteration in the Tptpv3 of both H-5 and UZ#16 is highly variable. 
In both holes, some samples contain substantial smectite and minor clinoptilolite and other 
samples contain mostly unaltered glass. The GU-3 samples are less altered but fall within the 
ranges of alteration for H-5 and UZ#16. 
Samples of Tac from H-5 and GU-3 are predominantly vitric with phenocrysts or lithic 
inclusions of silica+feldspar composition and minor alteration to smectite and clinoptilolite. 
Alteration of the Tacbt unit in H-5 is intermediate between UZ#16 and GU-3, but a higher 
content of silica+feldspar phenocrysts and lithic inclusions is a common attribute of all three 
borehole data sets. Mineralogic proportions in the Tcpuv are slightly different between H-5 and 
GU-3 samples, although both sets are partly vitric. The higher silica+feldspar content of the 
GU-3 samples reflects their locations lower in the Tcpuv unit, probably transitional to the 
underlying devitrified tuff. 
USW WT-1 Compared with UE-25 UZ#16 and USW GU-3 
Mineral abundances in WT-1 are compared with UE-25 UZ#16 and USW GU-3 in Table E-22. 
For the devitrified units Tptrn, Tptrl, Tptpul, Tptpmn, Tptpll, and Tptpln, the three data sets have 
very similar ranges of mineral abundances except for the presence of stellerite in the Tptpll and 
Tptpln of UZ#16. The vitric tuff of Tpbt3 has minor smectitic alteration and residual glass in all 
three data sets. The Tptpv3 samples from all three data sets are partly devitrified with variable 
contents of smectite and clinoptilolite. 
Sample WT-1 1570-1580 spans the interval from six to sixteen feet below the base of the Tac, 
which is the lowest recognized unit in WT-1 (see Table G-5). This makes it unclear which unit 
from a qualified data set should be used for comparison. The largest cuttings in the sample 
remainder have the appearance of a densely welded tuff, and the XRD analysis is consistent with 
this lithology. USGS (1995 [DIRS 169694], p. 6) stated that bit cutting samples from 1560 to 
1610 ft are contaminated with densely welded tuff fragments of Tiva Canyon Tuff that are larger 
than the other cuttings. USGS (1995 [DIRS 169694], p. 6) also proposed that a fault exists from 
1540 to 1564 ft and that the unit below the Tac is the Bullfrog Tuff (Tcb). Because of the 
uncertainties in unit identification and sample integrity, it will be recommended that this sample 
be excluded from qualification. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 E-32 September 2004 
Table E-21. Mineral-Abundance Ranges, USW H-5, UE-25 UZ#16, and USW GU-3 (Weight Percent) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
H-5 UZ#16 GU-3 H-5 UZ#16 GU-3 H-5 UZ#16 GU-3 H-5 UZ#16 GU-3 
Tpcpv1 15 8-14 0 0-0 45 4-29 40 63-82 
Tpy 0 9-12 0 0-0 3 0-3 96 85-91 
Tpbt3 14 6-18 4 0 0-0 0 14 11-29 58 69 59-78 40 
Tptrn Tr.-1 Tr.-5 0-1 0-0 0-0 0-0 90-100 94-103 95-99 0-0 0-0 0-0 
Tptrl 3 7 0 0 97 104 0 0 
Tptpul 0-5 4-6 0-0 0-0 94-100 90-96 0-0 0-0 
Tptpmn Tr.-3 3-4 0-0 0-0 91-97 91-99 0-0 0-0 
Tptpll 2-4 1-6 0-1 0-0 0-11* 0-0 95-100 88-100 96-100 0-0 0-0 0-0 
Tptpln Tr.-2 2-6 0-1 0-0 0-14* 0-0 95-99 84-97 97-98 0-0 0-0 0-0 
Tptpv3 0-50 0-73 2-2 0-10 0-4 0-0 10-57 17-23 60-65 0-90 0-82 30-40 
Tptpv2 
No samples in H-5 data set Tptpv1 
Tpbt1 
Tac 0-3 1-19 0-1 0-6 38-72 0-3 7-30 22-54 36-46 70-95 0-0 55-65 
Tacbt 0-3 5-9 3-3 10-52 40-42 0-2 12-75 53-58 36-71 0-78 0-0 20-65 
Tcpuv 3-5 2-4 0-51 0-0 15-17 52-54 31-75 45-45 

Table E-21. Mineral-Abundance Ranges, USW H-5, UE-25 UZ#16, and USW GU-3 (Weight Percent) (Continued) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
Tcpuc 1 3-8 0-1 0 0-0 0-0 90 85-97 88-97 0 0-0 0-0 
Tcpm No samples in H-5 data set 
Tcplc 1 6-7 0 0 0-0 0 103 90-94 101 0 0-0 0 
Tcplv Tr.-10 3 0-3 60-64 42 30-70 28-39 61 30-52 0-0 0 0-38 
Tcpbt 13 0 56 60 34 39 0 0 
NOTE: ------ = no samples analyzed from this unit. 
Tr. = trace amount present. 
Blanks are intentional. 
*The zeolite present is stellerite. 
Rock Properties Model 
MDL-NBS-BS-000004 REV 01 E-33 September 2004 

Rock Properties Model 
MDL-NBS-GS-000004 REV 01 E-34 September 2004 
Table E-22. Mineral-Abundance Ranges, USW WT-1, UE-25 UZ#16, and USW GU-3 (Weight Percent) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
WT-1 UZ#16 GU-3 WT-1 UZ#16 GU-3 WT-1 UZ#16 GU-3 WT-1 UZ#16 GU-3 
Tpbt3 5 6-18 4 0 0-0 0 24 11-29 58 70 59-78 40 
Tptrn 0-0 Tr.-5 0-1 0-0 0-0 0-0 95-100 94-103 95-99 0-0 0-0 0-0 
Tptrl No samples in WT-1 data set 
Tptpul 1-1 4-6 0-0 0-0 98-99 90-96 0-0 0-0 
Tptpmn 1-1 3-4 0-0 0-0 98-99 91-99 0-0 0-0 
Tptpll 1-3 1-6 0-1 0-1 0-11* 0-0 95-98 88-100 96-100 0-0 0-0 0-0 
Tptpln 0 2-6 0-1 0 0-14* 0-0 99 84-97 97-98 0 0-0 0-0 
Tptpv3 1-2 0-73 2-2 2-14 0-4 0-0 83-98 17-23 60-65 0-0 0-82 30-40 
Tptpv2 1 2 2 29 10 0 70 39 56 0 49 45 
Tptpv1 
No samples in WT-1 data set 
Tpbt1 
Tac 8-18 1-19 0-1 25-40 38-72 0-3 46-56 22-54 36-46 0-0 0-0 55-65 
Below 
Tac 3 5 92 0 
NOTE: ------ = no samples analyzed from this unit. 
Tr. = trace amount present. 
Blanks are intentional. 
*The zeolite present is stellerite. 

USW WT-2 compared with USW SD-12 and UE-25 UZ#16 
The upper boundary of unit Tcplv at 1,794 ft is the lowest recognized unit boundary in WT-2. In 
SD-12, about half a kilometer away (BSC 2004 [DIRS 170031], p. 27), the Tcplv is 268 ft thick. 
Therefore, it is likely that the WT-2 samples from 1,820-1,830, 1,910-1,920, and 2,000-2,010 ft 
belong to the Tcplv unit. The WT-2 Tcplv unit in Table E-23 is represented by the summarized 
mineralogic data from these three samples. Mineralogic data for three WT-2 core samples from 
the lowermost ten feet of the borehole are compared to data for Tcb samples in SD-12. 
Mineral abundances in the Tptrn, Tptpul, Tptpmn, Tptpll, Tptpln, Tacbt, Tcpuc, Tcpm, Tcplv, 
and Tcb are similar in all three borehole data sets. Samples from the Tpbt2, Tptpv3, Tptpv2, and 
Tptpv1 units in WT-2 tend to have higher feldspar+silica and lower glass contents than the 
SD-12 and UZ#16 samples from the corresponding units. Some similar higher feldspar+silica 
values have been measured in qualified borehole data sets. For example, feldspar+silica values 
in the Tptpv3 of GU-3 are in the 60-65 weight percent range and are comparable to the 
48-69 weight percent values for WT-2. The feldspar+silica content of a Tptpv2 sample from 
GU-3 is 56 weight percent, comparable to 57 weight percent in WT-2. The sample remainders 
of the questioned analyses were examined for impurities and were estimated to contain less than 
5 volume percent of devitrified tuff impurities. Therefore, the differences among data sets are 
considered acceptable. 
E4. CONCLUSIONS 
The data sets for Boreholes UE-25 p#1, USW H-3, USW H-4, H-5, WT-1, and WT-2 are 
recommended for qualification with the exceptions noted below. Mineral abundances in these 
data sets generally show very good correspondence with abundances in the same 
lithostratigraphic units of qualified data sets. These data are qualified for use the for the Rock 
Properties Model report. 
Data for UE-25 J-12 should remain unqualified because no records were found to document 
collection, description, or analysis of the samples. These data are not input to the Rock 
Properties Model (RPM3.1) (BSC 2004 [DIRS 170031]). 
The Qualified status of LADV831321AQ97.001 [DIRS 107142] recommended by Steinborn 
(2002 [DIRS 160702]) is incorrect because the DTN contains cuttings data from Borehole USW 
H-3 that remained unqualified. The qualification recommendation made by Steinborn (2002 
[DIRS 160702]) was based on the qualification of the software POWD10. The qualification 
activity in this report includes cuttings data from Borehole USW H-3, and the results qualify the 
DTN for use in this report. 
Similarly, the Qualified status of LADV831321AQ97.007 [DIRS 113499] is incorrect because 
the DTN contains cuttings data from borehole USW H-5 that are still unqualified. The 
qualification activity in this report includes cuttings data from the borehole USW H-5, and the 
results qualify the DTN for use in this report. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 E-36 September 2004 
Table E-23. Mineral-Abundance Ranges, USW WT-2, USW SD-12, and UE-25 UZ#16 (Weight Percent) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
WT-2 SD-12 UZ#16 WT-2 SD-12 UZ#16 WT-2 SD-12 UZ#16 WT-2 SD-12 UZ#16 
Tpbt2 4-7 8-16 0-0 0-0 47-59 19-22 30-50 58-71 
Tptrn 1-1 0-2 Tr.-5 0-0 0-0 0-0 97-97 95-100 95-99 0-0 0-0 0-0 
Tptpul 1-3 3-5 4-6 0-0 0-0 0-0 98-99 92-98 90-96 0-0 0-0 0-0 
Tptpmn 1-2 3-5 3-4 0-0-0-0 0-0 96-98 93-96 91-99 0-0 0-0 0-0 
Tptpll 1-2 2-5 1-6 0-0 0-0 0-11* 96-99 93-98 88-100 0-0 0-0 0-0 
Tptpln 1-3 0-4 2-6 0-0 0-1 0-14* 96-98 92-98 84-97 0-0 0-0 0-0 
Tptpv3 1-2 0-2 0-73 1-1 0-0 0-4 48-69 11-18 8-23 30-50 72-85 0-82 
Tptpv2 2 Tr.-2 2 0 0-0 10 57 18-35 39 40 65-80 49 
Tptpv1 1 0-8 1 1 0-3 80 58 7-22 24 40 74-93 0 
Tpbt1 No samples analyzed in WT-2 
Tac 1-1 0-3 1-19 1-5 1-10 38-72 59-66 9-35 22-54 30-40 58-89 0-0 
Tacbt 0-3 3-11 5-9 19-24 12-68 40-42 71-78 22-78 53-58 0-0 0-62 0-0 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 E-37 September 2004 
Table E-23. Mineral-Abundance Ranges, USW WT-2, USW SD-12, and UE-25 UZ#16 (Weight Percent) (Continued) 
Unit 
Smectite 
 Mordenite Clinoptilolite Feldspar+Silica Glass 
Tcpuv No samples analyzed in WT-2 
Tcpuc 1 2-3 3-8 2 0-0 0-0 96 89-96 85-97 0 0-0 0-0 
Tcpm 1-1 Tr.-5 3-4 1-1 0-0 0-0 97-98 91-99 97-100 0-0-0-0 0-0 
Tcplc No samples analyzed in WT-2 
Tcplv 0-4 2-15 3 19-42 10-65 42 58-78 38-84 61 0-0 0-0 0 
Tcb 1-1 0-2 0-0 0-0 96-96 95-98 0-0 0-0 
NOTE: ------ = no samples analyzed from this unit. 
Tr. = trace amount present. 
Blanks are intentional. 
*The zeolite present is stellerite. 

The following errors were noted in these data in this report: 
 
Data from sample p#1 570-580 are excluded from qualification due to the questionable 
representativeness of the sample material. 
 
Data for sample p#1 2030-2040, in the Tcbm unit, are excluded from qualification 
because the XRD analysis shows the sample containing seven percent glass. The 
presence of glass in the middle of approximately 300 ft thick devitrified interval 
(Tcbuc-Tcbm-Tcblc) is unlikely. 
 
Data from samples p#1 4080-4090, 4170-4180, and 4313-4318 are excluded from 
qualification because they represent the Paleozoic section and no qualified data from 
Paleozoic samples exist for corroboration. 
 
USW H-4 1320-1330 XRD data are not qualified due to unrepresentative subsampling 
for XRD analysis. 
 
USW H-5 1990-2000 XRD data are not qualified due to the likely abundant presence of 
impurities from an overlying unit 
 
The XRD data for sample USW WT-1 1570-1580 are not qualified due to the 
uncertainties in unit identification and sample integrity. 
There are several thousand mineralogic abundance analyses used to as input to the Rock 
Properties Model. The eight errors noted above are a very small portion of the data. The impact 
of these eight errors to the analysis performed to determine the total hydrous (altered 
mineralogy) is insignificant. Therefore, there is no significant impact to the model. 

APPENDIX F 
SELECTED MINERALOGIC-ABUNDANCE DATA 
FROM DATA SETS TO BE QUALIFIED 


Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-1 September 2004 
Table F-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 p#1 (Weight Percent) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
p#1 Tptrn 150-228 
p#1 200-210 -/--/-14 -6 76 -
p#1 Tptrl 228-248 
not analyzed 
p#1 Tptpul 248-493 
p#1 260-270 2/--/-Tr. 3 24 71 - 
p#1 280-290 2/--/-Tr. 5 25 68 - 
p#1 410-420 3/--/-4 9 24 59 -
p#1 Tptpmn 493-640 
p#1 570-580 2/--/-Tr. 30 3 67 -
p#1 Tptpll 640-958 
p#1 810-820 2/--/--27 5 59 - 
p#1 900-910 1/--/--36 1 61 -
p#1 Tptpln 958-1090 
p#1 1040-1050 -/--/--27 5 67 -
p#1 Tptpv3 1090-1200 
not analyzed 
p#1 Tptpv2 1200-1243 
partly sampled, see below 
p#1 Tptpv1 1243-1270 
p#1 1240-1250 1/-54/--Tr. 20 24 - 
p#1 1260-1270 1/-62/--Tr. 23 14 -
p#1 Tac 1270-1390 
p#1 1290 (sidewall core) 2/-37/--3 22 39 - 
p#1 1340-1350 Tr./- 56/--2 20 21 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-2 September 2004 
Table F-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 p#1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
p#1 1400 (sidewall core) 10/-34/--17 -29 9 
p#1 Tacbt 1390-1441 
p#1 1420 (sidewall core) 12/--/--24 -40 22 
p#1 Tcpuv 1441-1468 
not analyzed 
p#1 Tcpuc 1468-1535 
p#1 1470 (sidewall core) 5/34 -/--25 -40 -
p#1 Tcpm 1535-1630 
p#1 1590-1598 -3/--39 -60 -
p#1 Tcplc 1630-1680 
p#1 1640-1650 5/--/--37 -58 -
p#1 Tcplv 1680-1790 
p#1 1690-1700 7/-40/--11 17 25 - 
p#1 1730-1740 3/-60/--4 14 17 -
p#1 Tcpbt 1790-1826 
p#1 1790-1800 1/23 45/--12 -18 -
p#1 Tcbuv not present 
p#1 Tcbuc 1826-1953 
p#1 1830-1840 Tr./- -/--45 -39 - 
p#1 1870-1880 3/-9/--27 -55 - 
p#1 1920-1930 5/--/--28 -64 -
p#1 Tcbm 1953-2130 
p#1 1970-1980 Tr./- -/--35 -61 - 
p#1 1990-2000 1/--/--39 -60 - 
p#1 2030-2040 2/--/--31 -59 7 
p#1 2070-2080 Tr./- -/--39 -59 - 
p#1 2120-2130 Tr./- -/--36 -65 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-3 September 2004 
Table F-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 p#1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
p#1 Tcblc 2130-2162 
p#1 2150-2160 3/-Tr./- -29 -61 -
p#1 Tcblv 2160-2240 
p#1 2210-2220 3/28 -/14 -28 -33 -
p#1 Tcbbt 2240-2262 
p#1 2240-2250 7/3 -/8 -29 -52 -
p#1 Tctuv 2262-2340 
p#1 2280-2290 6/-31/4 -22 -32 - 
p#1 2330-2340 11/-2/4 -32 -45 -
p#1 Tctuc 2340-2395 
p#1 2370-2380 7/--/--40 -49 -
p#1 Tctm 2395-2595 
p#1 2414-2420 Tr./- -/--43 -51 - 
p#1 2460-2470 2/--/--32 -61 - 
p#1 2510-2520 --/--24 -77 - 
p#1 2570-2580 2/--/--30 -71 -
p#1 Tctlc 2595-2616 
not analyzed 
p#1 Tctlv 2616-2863 
p#1 2630-2640 7/--/--34 -57 - 
p#1 2650-2660 15/--/--31 -51 - 
p#1 2690-2700 4/--/--34 -56 - 
p#1 2750-2760 13/-13/--32 -35 - 
p#1 2790-2800 21/-4/--29 -39 - 
p#1 2840-2850 25/--/Tr. -42 -34 -
p#1 Tctbt not recognized 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-4 September 2004 
Table F-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 p#1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
p#1 Tund 2863-4080 
p#1 2890-2900 15/--/--34 -47 - 
p#1 2940-2950 4/--/2 -36 -55 - 
p#1 2980-2990 6/--/6 -29 -55 - 
p#1 3030-3040 11/--/1 -30 -53 - 
p#1 3080-3090 15/--/--44 -34 - 
p#1 3130-3140 6/-2/5 -34 -50 - 
p#1 3160-3170 13/-3/11 -37 -39 - 
p#1 3230-3240 5/-Tr./19 -38 -37 - 
p#1 3270-3280 5/-2/13 -36 -43 - 
p#1 3320-3330 25/-4/--38 -25 - 
p#1 3370-3380 22/-2/--35 -35 - 
p#1 3410-3420 7/-3/6 -35 -44 - 
p#1 3453 (core) 6/-2/13 -38 -39 - 
p#1 3480-3490 7/-7/4 -40 -42 - 
p#1 3510-3520 11/--/--37 -49 - 
p#1 3550-4560 4/--/--43 -45 - 
p#1 3560-3570 7/--/--42 -49 - 
p#1 3590-3600 9/--/--42 -48 - 
p#1 3630-3640 19/--/--32 -41 1 
p#1 3650-3660 19/--/--29 -42 1 
p#1 3660-3670 16/--/--30 -51 1 
p#1 3690-3700 15/--/3 -36 -38 1 
p#1 3720-3730 20/--/2 -27 -41 1 
p#1 3750-3760 15/-Tr./- -33 -36 - 
p#1 3790-3800 14/--/--35 -23 1 
p#1 3820-3830 9/-Tr./- -44 -19 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-5 September 2004 
Table F-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 p#1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
p#1 3860-3870 10/--/--52 -33 - 
p#1 3913 (core) 37/--/--53 --- 
p#1 3916 (core) 45/--/--49 --Tr. 
p#1 3928 (core) 40/--/--18 --- 
p#1 3940-3950 9/--/Tr. -39 -44 - 
p#1 3980-3990 13/-2/2 -32 -48 - 
p#1 4040-4050 16/-2/Tr. -29 -29 - 
p#1 4070-4080 29/-1/Tr. -30 -38 -
p#1 Pz 4080 to bottom 
p#1 4080-4090 These samples contain 80-93 % dolomite, 1-9 % smectite, 4-8 % quartz, and 3-4 % calcite. 
p#1 4170-4180 
p#1 4313-4318 
Sources: DTNs MO0101XRDDRILC.001 [DIRS 169517], MO0004QGFMPICK.000 [DIRS 152554] 
- = not detected;Tr. = trace amount much less than 1%. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-6 September 2004 
Table F-2. Selected Mineralogic-Abundance Data for Drill Hole USW H-3 (Weight Percent) 
Hornblende/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range 
in feet 
H-3 Tptrn 449-526.9 
H-3 470-480 -/--13 -8 77 - 
H-3 520-530 -/--Tr. -24 76 -
H-3 Tptpul 540-680.1 
H-3 540-550 -/--21 -5 73 - 
H-3 610-620 -/--Tr. -29 71 -
H-3 Tptpmn 680.1-848.1 
H-3 740-750 -/---4 26 68 - 
H-3 800-810 -/--Tr. 2 27 69 -
H-3 Tptpll 848.1-1049.9 
H-3 870-880 -/---29 3 66 - 
H-3 930-940 -/---14 16 69 - 
H-3 990-1000 -/--4 Tr. 23 72 - 
H-3 1030-1040 -/--Tr. 3 24 72 -
H-3 Tptpln 1049.9-1194 
H-3 1100-1110 -/--Tr. 2 23 74 - 
H-3 1160-1170 -/---2 29 67 -
H-3 Tptpv3 1194-1308 
H-3 1270-1280 -/---2 7 19 70 
H-3 Tptpv2 1308-1341 
H-3 1320-1330 -/---2 5 23 70 
H-3 Tptpv1 1341-1392 
H-3 1360-1370 -/---Tr. Tr. 3 97 
H-3 Tacbt 1437-1495 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-7 September 2004 
Table F-2. Selected Mineralogic-Abundance Data for Drill Hole USW H-3 (Weight Percent) (Continued) 
Hornblende/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
H-3 1440-1450 -/---Tr. 5 11 81 
H-3 1480-1490 1/-1 -3 1 47 34 
H-3 Tcpuv 1495-1518 
H-3 1500-1510 -/-1 4 12 20 50 10 
H-3 Tcpuc 1518-1640 
H-3 1550 (sidewall core) -/-2 7 10 7 70 -
H-3 Tcpm 1640-1690 
H-3 1655 (sidewall core) -/-2 7 7 7 75 -
H-3 Tcplc 1690-1702 
sample moved to Tcplv 
H-3 Tcplv 1702-1899.9 
H-3 1700 (sidewall core) 
moved to Tcplv 
-/-2 -10 15 60 15 
H-3 1800 (sidewall) -/-60 -7 -30 -
H-3 Tcpbt 1899.9-1907.1 
H-3 1900 (sidewall core) -/-75 2 2 2 15 -
H-3 Tcblv 2397-2449.1 
H-3 2400 (sidewall core) 1/-75 -2 2 15 - 
H-3 2440 (sidewall core) -/30 35 -2 2 30 -
H-3 Tctuv 2477-2567 
H-3 2490 (sidewall core) -/25 45 -2 -20 -
Sources: DTNs LADV831321AQ97.001 [DIRS 107142], MO0101XRDDRILC.001 [DIRS 169517], MO0004QGFMPICK.000 [DIRS 152554] 
- = not detected; Tr. = trace amount much less than 1%. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-8 September 2004 
Table F-3. Selected Mineralogic-Abundance Data for Drill Hole USW H-4 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
H-4 Tptrn 254-376 
H-4 310-320 -/--11 -10 75 -
H-4 Tptpul 376-576 
H-4 390-400 2/--14 2 13 68 - 
H-4 440-450 2/--19 2 13 64 - 
H-4 490-500 2/--11 ~1 18 67 -
H-4 Tptpmn 576-703 
H-4 640-650 2/---4 26 68 -
H-4 Tptpll 703-987 
H-4 830-840 3/--12 ~1 14 68 - 
H-4 910-920 ~1/- --11 20 67 - 
H-4 940-950 ~1/- --7 21 71 -
H-4 Tptpln 987-1185 
H-4 1040-1050 ~1/- --6 23 70 - 
H-4 1150-1160 2/---15 18 64 -
H-4 Tptpv3 1185-1209 
H-4 1190-1200 -/-5 --15 15 70 
H-4 1230-1240 20/---4 20 25 30 
H-4 Tptpv2-1 1209-1312 
not sampled 
H-4 Tpbt1 1312-1317 
H-4 1312 (sidewall core) 2/-5 -10 7 25 50 
H-4 Tac 1317-1572 
H-4 1350-1360 -78 -3 9 10 - 
H-4 1410-1420 -/5 52 -7 9 31 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-9 September 2004 
Table F-3. Selected Mineralogic-Abundance Data for Drill Hole USW H-4 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
H-4 1420 (sidewall core) 2/-70 -2 2 25 - 
H-4 1455 (sidewall core) -/25 45 -2 2 20 - 
H-4 1540-1550 3/16 29 -11 10 31 - 
H-4 1550 (sidewall core) 2/-75 -1 2 15 -
H-4 Tacbt 1572-1626.9 
H-4 1600-1610 -/-31 -17 9 35 -
H-4 Tcpuv 1626.9-1662 
H-4 1640-1650 -/-10 -6 24 60 -
H-4 Tcpuc 1662-1746 
H-4 1656 (sidewall core, 
moved from Tcpuv) 
-/---2 35 65 - 
H-4 1710-1720 ~1/- --33 3 61 -
H-4 Tcpm 1746-1820 
H-4 1790-1800 3/---15 17 64 -
H-4 Tcplc 1820-1840 
not sampled 
H-4 Tcplv 1840-2263.1 
H-4 1900-1910 -/-22 -5 16 56 - 
H-4 1980-1990 -/23 11 -2 6 55 -
Sources: DTNs MO0101XRDDRILC.001 [DIRS 169517], MO0004QGFMPICK.000 [DIRS 152554]. 
- = not detected; Tr. = trace amount much less than 1%. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-10 September 2004 
Table F-4. Selected Mineralogic-Abundance Data for Drill Hole USW H-5 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
H-5 Tpc_un 0-404 
H-5 40-50 -/--31 Tr. -66 -
H-5 50 -/--17 -2 80 - 
H-5 110-120 Tr./- -17 -13 68 - 
H-5 160-170 Tr./- -13 -20 70 -
H-5 190 -/--2 -23 75 
H-5 230-240 Tr./- -10 1 22 69 - 
H-5 320-330 1/--3 6 22 69 - 
H-5 380-390 -/--2 -29 68 -
H-5 Tpcpv3 404-404 
H-5 Tpcpv2 404-420 
H-5 Tpcpv1 420-437.5 
H-5 420 15/---1 12 32 40 
H-5 Tpbt4 437.5-438 
not sampled 
H-5 Tpy 438-457 
H-5 450 ---1 -2 96 
H-5 Tpbt3 457-471 
H-5 460-470 14/----2 12 69 
H-5 Tpp-Tptrv1 471-564 
not sampled 
H-5 Tptrn 564-700 
H-5 620-630 1/--4 1 9 76 -
H-5 700 Tr./- -20 -15 65 -
H-5 Tptrl 700-741 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-11 September 2004 
Table F-4. Selected Mineralogic-Abundance Data for Drill Hole USW H-5 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
H-5 720-730 3/--23 -9 65 -
H-5 Tptpul 741-988 
H-5 750-760 5/--17 1 17 62 - 
H-5 800-810 3/--Tr. 2 24 72 - 
H-5 830-840 -/--Tr. -24 76 - 
H-5 860-870 4/--8 4 23 59 - 
H-5 920-930 2/--Tr. 3 24 71 - 
H-5 970-980 2/--Tr. 2 26 71 -
H-5 Tptpmn 988-1088 
H-5 990-1000 3/--3 1 28 59 -
H-5 1050 Tr./- --2 40 55 -
H-5 Tptpll 1088-1450 
H-5 1090-1100 4/--4 4 26 61 - 
H-5 1150-1160 2/---26 10 62 - 
H-5 1200-1210 3/--3 22 11 61 - 
H-5 1230-1240 2/---7 23 69 - 
H-5 1290-1300 2/---10 18 71 - 
H-5 1350-1360 2/--3 15 16 61 - 
H-5 1380-1390 2/---15 20 65 -
H-5 Tptpln 1450-1582 
H-5 1450-1460 Tr./- --20 8 71 - 
H-5 1490-1500 2/---9 21 68 - 
H-5 1530-1540 1/---21 14 60 -
H-5 Tptpv3 1582-1659 
H-5 1590-1600 35/-10 --17 40 -
H-5 1610 -/---1 2 7 90 
H-5 1630-1640 Tr./- --1 2 9 88 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-12 September 2004 
Table F-4. Selected Mineralogic-Abundance Data for Drill Hole USW H-5 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
H-5 1650-1660 -/---2 5 4 89 
H-5 1666 (sidewall core) 
(moved from Tptpv2) 
50/-10 -1 12 25 -
H-5 Tptpv2-Tpbt1 1659-1705 
not sampled 
H-5 Tac 1705-1879.9 
H-5 1710-1720 3/---5 5 20 70 
H-5 1750 -/-Tr.? -2 5 5 85 
H-5 1762 (sidewall core) -/---1 1 5 95 
H-5 1760-1770 -/-6 -3 4 6 81 
H-5 1800 -/---2 2 10 85 
H-5 1820-1830 Tr./- 4 -4 2 10 80 
H-5 1852 (sidewall core) -/---2 -7 90 
H-5 1875 (sidewall core) -/-Tr.? -2 Tr.? 5 92 
H-5 Tacbt 1879.9-1944.9 
H-5 1890-1900 1/-11 -4 3 6 75 
H-5 1900-1910 Tr./- 10 -3 1 8 78 
H-5 1910-1920 Tr./- 18 -5 5 7 65 
H-5 1917 (sidewall core) -/-25 -30 10 35 - 
H-5 1920-1930 3/-52 -14 7 16 6 
H-5 1930 2/-50 -15 -30 -
H-5 Tcpuv 1944.9-1967 
H-5 1950-1960 3/-51 -2 5 8 31 
H-5 1966 (sidewall core) 5/---2 -15 75 
H-5 Tcpuc 1967-2085 
H-5 1990-2000 excluded from 
corroboration 
1/-10 -Tr. 9 21 59 
H-5 2070-2080 1/---29 3 68 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-13 September 2004 
Table F-4. Selected Mineralogic-Abundance Data for Drill Hole USW H-5 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
H-5 Tcpm 2085-2113 
not sampled 
H-5 Tcplc 2113-2130 
H-5 2140-2150 moved from Tcplv 1/---27 7 69 -
H-5 Tcplv 2130-2240.1 
H-5 2200 Tr./- 60 -2 2 35 - 
H-5 2230-2240 2/8 64 -4 16 8 -
H-5 Tcpbt 2240.1-2263.1 
H-5 2260-2270 6/7 56 -3 20 11 -
H-5 Tcbuv 2263.1-2310 
not sampled 
Sources: DTNs MO0101XRDDRILC.001 [DIRS 169517], LADV831321AQ97.007 [DIRS 113499], LA9910JC831321.001 [DIRS 113496], MO0004QGFMPICK.000 
[DIRS 152554] 
- = not detected; Tr. = trace amount much less than 1%. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-14 September 2004 
Table F-5. Selected Mineralogic-Abundance Data for Drill Hole USW WT-1 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in 
feet 
WT-1 Tpbt3 435-446 
WT-1 440-450 5/--8 -4 12 70 
WT-1 Tpbt2 446-477 
not sampled 
WT-1 Tptrv3-Tptrv1 477-492 
not sampled 
WT-1 Tptrn 492-575 
WT-1 500-510 -/--11 -12 72 - 
WT-1 550-560 -/--19 -10 71 -
WT-1 Tptrl 575-593 
not sampled 
WT-1 Tptpul 593-733 
WT-1 640-650 1/--11 9 16 62 - 
WT-1 690-700 1/---19 19 61 -
WT-1 Tptpmn 733-888 
WT-1 780-790 1/--3 25 9 61 - 
WT-1 840-850 1/--6 20 16 56 -
WT-1 Tptpll 888-1187 
WT-1 930-940 3/--5 26 7 57 - 
WT-1 1000-1010 1/---22 16 60 - 
WT-1 1090-1100 1/---24 15 58 - 
WT-1 1160-1170 1/-1 11 10 18 58 -
WT-1 Tptpln 1187-1299 
WT-1 1220-1230 -/--5 27 8 59 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-15 September 2004 
Table F-5. Selected Mineralogic-Abundance Data for Drill Hole USW WT-1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
WT-1 Tptpv3 1299-1337 
WT-1 1300-1310 1/-2 -35 4 59 - 
WT-1 1320-1330 2/-14 -20 13 50 -
WT-1 Tptpv2 1337-1368 
WT-1 1340-1350 1/-29 -10 9 51 -
WT-1 Tptpv1 1368-1380 
not sampled 
WT-1 Tpbt1 1380-1384 
partly sampled, 1380-1390 
WT-1 Tac 1384-1564 
WT-1 1380-1390 -/12 40 -8 7 31 - 
WT-1 1410-1420 -/8 40 -3 8 40 - 
WT-1 1470-1480 -/18 25 -10 7 39 - 
WT-1 1510-1520 -/10 43 -8 5 33 - 
WT-1 1550-1560 -/12 40 -14 7 26 -
WT-1 no units defined below 
1564 
WT-1 1570-1580 -/3 5 -26 9 57 -
Sources: DTNs MO0101XRDDRILC.001 [DIRS 169517], MO0004QGFMPICK.000 [DIRS 152554]. 
- = not detected; Tr. = trace amount much less than 1% 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-16 September 2004 
Table F-6. Selected Mineralogic-Abundance Data for Drill Hole USW WT-2 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
WT-2 Tpbt2 247-271 
WT-2 250-260 4/---2 11 34 50 
WT-2 260-270 7/---1 7 51 30 
WT-2 Tptrv3-1 271-285 
not analyzed 
WT-2 Tptrn 285-380 
WT-2 290-300 1/--10 -9 78 - 
WT-2 370-380 <1/- -19 1 5 72 -
WT-2 Tptpul 421-590 
WT-2 420-450 1/--12 2 11 73 - 
WT-2 510-520 1/--13 3 21 62 - 
WT-2 570-580 3/--13 5 21 59 -
WT-2 Tptpmn 590-727 
WT-2 650-660 2/--10 6 22 58 - 
WT-2 720-730 1/--10 8 19 61 -
WT-2 Tptpll 727-1014 
WT-2 780-790 1/--6 21 10 59 - 
WT-2 850-860 1/--13 14 16 56 - 
WT-2 930-940 1/--13 11 17 57 - 
WT-2 990-1000 2/---14 21 62 -
WT-2 Tptpln 1014-1179 
WT-2 1060-1070 3/---16 19 61 - 
WT-2 1130-1140 <1/- --13 15 70 -
WT-2 Tptpv3 1179-1223 
WT-2 1190-1200 2/-1 -4 9 56 30 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-17 September 2004 
Table F-6. Selected Mineralogic-Abundance Data for Drill Hole USW WT-2 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
WT-2 1200-1210 1/-1 -7 15 26 50 
WT-2 Tptpv2 1223-1264 
WT-2 1250-1260 2/---9 12 36 40 
WT-2 Tptpv1 1264-1315 
WT-2 1300-1310 <1/- 1 -10 12 36 40 
WT-2 Tptbt1 1315-1319 
not analyzed 
WT-2 Tac 1319-1521 
WT-2 1360-1370 <1/- <1 -11 8 40 40 
WT-2 1420-1430 <1/- 4 -11 4 50 30 
WT-2 1450-1460 1/-<1 -14 8 44 30 
WT-2 1470-1480 <1/- 5 -8 8 49 30 
WT-2 Tacbt 1521-1594 
WT-2 1520-1530 3/-24 -11 12 48 - 
WT-2 1570-1580 -/-19 -18 7 53 -
WT-2 Tcpuv not recognized 
WT-2 Tcpuc 1594-1706 
WT-2 1640-1650 1/-2 -22 7 67 -
WT-2 Tcpm 1706-1776 
WT-2 1710-1720 1/-<1 -22 7 69 - 
WT-2 1750-1760 1/-1 -14 17 66 -
WT-2 Tcplc 1776-1794 
not analyzed 
WT-2 Tcplv 1794-not defined 
WT-2 1820-1830 4/-19 -5 14 59 - 
WT-2 1910-1920 -/-42 -5 6 47 - 
WT-2 2000-2010 -/4 30 -3 3 55 -

Table F-6. Selected Mineralogic-Abundance Data for Drill Hole USW WT-2 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
WT-2 Tcb? 
WT-2 2050.25 (core) 1/---18 9 69 - 
WT-2 2053.7 (core) 1/---19 10 67 - 
WT-2 2059.5 (core) <1/- --21 9 66 -
Sources: DTNs MO0101XRDDRILC.001 [DIRS 169517], MO0004QGFMPICK.000 [DIRS 152554]. 
- = not detected; Tr. = trace amount much less than 1%. 
Rock Properties Model 
MDL-NBS-BS-000004 REV 01 F-18 September 2004 

APPENDIX G 
SELECTED QUALIFIED MINERALOGIC-ABUNDANCE DATA 


Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-1 September 2004 
Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
UZ#16 Tpc_un 39.5-140.8 
UZ#16 54.8-55.0 2/--4 2 28 61 - 
UZ#16 64.7-64.9 3/--7 4 25 62 - 
UZ#16 78.6-78.9 3/--3 1 30 59 - 
UZ#16 88.7-88.8 2/--3 1 31 59 - 
UZ#16 102.1-102.2 3/--3 Tr. 32 57 - 
UZ#16 112.5-112.7 3/--2 Tr. 32 58 - 
UZ#16 126.0-126.2 1/-1 Tr. Tr. 33 59 - 
UZ#16 140.2-140.3 1/--Tr. 2 32 58 -
UZ#16 Tpcpv2 140.8-153 
not sampled 
UZ#16 Tpcpv1 153-160.7 
UZ#16 154.2-154.5 8/----12 17 63 
UZ#16 156.1-156.2 14/---Tr. Tr. 4 82 
UZ#16 Tpbt4 160.7-165.9 
UZ#16 165.1-165.3 6/---5 2 16 71 
UZ#16 Tpy 165.9-173.4 
UZ#16 167.4-167.5 12/-----3 85 
UZ#16 171.3-171.5 9/------91 
UZ#16 Tpbt3 173.4-188.8 
UZ#16 174.9-175.1 11/---1 2 8 78 
UZ#16 179.3-179.4 6/---7 2 20 65 
UZ#16 183.9-184.1 18/---2 1 19 59 
UZ#16 188.5-188.7 7/---Tr. 1 20 70 
UZ#16 Tpbt2 188.8-217 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-2 September 2004 
Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
UZ#16 190.4-190.5 16/---2 1 19 58 
UZ#16 201.8-202.2 9/---3 Tr. 19 64 
UZ#16 211.4-211.6 8/---Tr. Tr. 19 71 
UZ#16 Tptrv3 217-228.1 
UZ#16 223.7-224.2 9/-18 --15 55 - 
UZ#16 224.1-224.7 6/-18 -2 24 48 - 
UZ#16 227.0-227.2 5/-17 -1 34 41 -
UZ#16 Tptrv2 228.1-229.4 
not sampled 
UZ#16 Tptrv1 229.4-238.9 
UZ#16 230.1-230.3 1/---Tr. 1 21 73 
UZ#16 234.1-234.2 Tr./- --Tr. 2 38 58 
UZ#16 Tptrn 238.9-357.8 
UZ#16 249.8-250.0 1/--3 -13 78 - 
UZ#16 269.2-269.8 1/--7 -11 81 - 
UZ#16 290.8-291.2 1/--12 -8 77 - 
UZ#16 309.1-309.5 Tr./- -13 -8 77 - 
UZ#16 331.7-332.2 1/--22 -5 76 - 
UZ#16 348.7-349.7 5/--22 -8 64 -
UZ#16 Tptrl 357.8-371 
UZ#16 370.2-370.7 7/--22 3 8 61 -
UZ#16 Tptpul 371-545 
UZ#16 391.6-392.0 6/--11 4 21 54 - 
UZ#16 412.7-413.0 4/--16 2 19 56 - 
UZ#16 435.1-435.5 4/--7 3 28 58 - 
UZ#16 458.6-458.9 4/--10 11 17 56 - 
UZ#16 484.4-485.7 4/--4 5 29 55 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-3 September 2004 
Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
UZ#16 509.6-510.0 4/--3 9 28 54 - 
UZ#16 533.3-533.6 4/--1 11 25 55 -
UZ#16 Tptpmn 545-669 
UZ#16 552.9-553.3 3/--Tr. 21 16 57 - 
UZ#16 577.3-577.6 3/--1 23 13 57 - 
UZ#16 602.8-603.4 3/--3 12 27 57 - 
UZ#16 627.9-628.2 3/--1 10 26 56 - 
UZ#16 648.8-649.3 4/--4 6 27 54 -
UZ#16 Tptpll 669-935 
UZ#16 674.2-674.7 5/--1 27 13 58 - 
UZ#16 701.5-701.9 3/--1 36 5 58 - 
UZ#16 726.1-726.4 1/-2* 7 23 10 57 - 
UZ#16 751.4-751.7 3/--1 29 10 56 - 
UZ#16 773.0-773.4 1/-3* 3 17 19 56 - 
UZ#16 797.0-797.2 1/-1* 1 14 23 57 - 
UZ#16 820.6-820.9 3/-1* 1 26 11 56 - 
UZ#16 843.0-843.5 3/-Tr.* 6 18 16 55 - 
UZ#16 862.4-862.7 2/-5* 5 9 22 55 - 
UZ#16 876.8-877.1 3/-11* 4 10 21 53 - 
UZ#16 896.2-896.6 4/--6 9 24 54 - 
UZ#16 910.2-910.4 6/-2* 3 18 18 56 - 
UZ#16 925.4-925.7 4/-5* 4 12 26 50 -
UZ#16 Tptpln 935-1107.5 
UZ#16 940.0-940.4 4/-1* Tr. 27 13 57 - 
UZ#16 954.3-954.7 3/-2* 3 18 20 52 - 
UZ#16 970.9-971.4 5/-2* 2 17 21 51 - 
UZ#16 984.2-984.8 4/-4* 2 18 21 52 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-4 September 2004 
Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
UZ#16 1001.0-1001.6 5/-4* 1 22 16 50 - 
UZ#16 1014.9-1015.0 6/--5 14 21 53 - 
UZ#16 1029.2-1029.5 5/-14* 3 15 21 45 - 
UZ#16 1030.4-1030.6 4/-8* 2 20 18 50 - 
UZ#16 1047.0-1047.3 4/-2* 2 24 13 55 - 
UZ#16 1059.6-1059.8 5/--3 15 23 54 - 
UZ#16 1074.8-1075.1 4/-1* 2 21 17 54 - 
UZ#16 1090.4-1090.6 4/-2* 1 24 15 53 - 
UZ#16 1104.1-1104.4 2/---31 8 57 -
UZ#16 Tptpv3 1107.5-1165.2 
UZ#16 1113.1-1113.2a 73/-4 -Tr. 10 10 - 
UZ#16 1113.1-1113.2b 5/---1 7 14 73 
UZ#16 1135.3-1135.6 3/---1 9 7 80 
UZ#16 1149.2-1149.5 -/---1 10 7 82 
UZ#16 1157.4-1157.6 1/---1 12 10 75 
UZ#16 Tptpv2 1165.2-1178 
UZ#16 1166.5-1167.0 2/-10 -2 20 17 49 
UZ#16 Tptpv1 1178-1190 
UZ#16 1179.8-1180.5 1/-80 -3 13 8 -
UZ#16 Tpbt1 1190-1197 
UZ#16 1190.2-1191.2 6/2 83 -1 6 4 -
UZ#16 Tac 1197-1455.4 
UZ#16 1202.8-1203.1 1/12 66 -3 14 6 - 
UZ#16 1215.7-1216.0 1/11 72 -3 12 7 - 
UZ#16 1235.5-1235.7 1/7 69 -3 18 7 - 
UZ#16 1256.6-1256.9 8/-72 -3 10 12 - 
UZ#16 1261.0-1261.2a 1/-66 -3 22 11 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-5 September 2004 
Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
UZ#16 1275.7-1276.0 3/8 54 -6 18 13 - 
UZ#16 1300.2-1300.3 9/5 58 -5 20 10 - 
UZ#16 1305.0-1305.3 2/9 60 -5 19 11 - 
UZ#16 1312.0-1312.4 3/12 54 -4 18 11 - 
UZ#16 1335.4-1336.2 1/11 38 -9 26 19 - 
UZ#16 1342.7-1343.0 3/16 55 -3 19 10 - 
UZ#16 1362.3-1362.7 3/13 52 -4 17 11 - 
UZ#16 1379.3-1379.5 3/9 60 -4 21 9 - 
UZ#16 1417.5-1418.0 2/8 58 -4 19 11 - 
UZ#16 1441.3-1441.8 2/12 65 -3 17 6 -
UZ#16 Tacbt 1455.4-1485 
UZ#16 1460.2-1460.7 5/4 42 -8 27 18 - 
UZ#16 1476.7-1477.0 5/-40 -11 20 27 -
UZ#16 Tcpuv 1485-1497.7 
not sampled 
UZ#16 Tcpuc 1497.7-1571 
UZ#16 1498.4-1498.9 -/8 --17 20 48 - 
UZ#16 1521.7-1522.2 3/---41 -54 - 
UZ#16 1539.3-1539.7 4/---41 -56 - 
UZ#16 1561.2-1561.7 4/---39 -58 -
UZ#16 Tcpm 1571-1638 
UZ#16 1579.1-1579.9 3/---38 -59 - 
UZ#16 1600.2-1600.4 4/---39 -58 - 
UZ#16 1618.7-1619.2 3/---24 14 62 -

Table G-1. Selected Mineralogic-Abundance Data for Drill Hole UE-25 UZ#16 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
UZ#16 Tcplc 1638-1669.2 
UZ#16 1638.7-1639.2 7/---20 15 55 - 
UZ#16 1655.5-1656.0 6/---11 27 56 -
UZ#16 Tcplv 1669.2-bottom 
UZ#16 1683.5-1684.0 3/-42 -5 28 28 -
Sources: LA000000000086.002 [DIRS 107144], MO0004QGFMPICK.000 [DIRS 152554]. 
- = not detected; Tr. = trace amount much less than 1%. 
*Stellerite fracture fillings. 
Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-6 September 2004 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-7 September 2004 
Table G-2. Selected Mineralogic-Abundance Data for Drill Hole USW G-1 (Weight Percent) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
G-1 Tcpbt 2154.9-2173.3 
G-1 2173 -/9 33 (c) -2 8 41 -
G-1 Tcbuv 2173.2-2337 
G-1 2190 4/-51 (c) -4 23 19 -
G-1 2198 1/2 48 (c) -4 20 20 -
G-1 2256 -/44 Tr. (c) -3 22 26 -
G-1 2279 Tr./38 29 (c) -3 8 21 -
G-1 2290 2/43 31 (c) -3 2 18 -
G-1 2316 -/23 30 (c) -19 -27 -
G-1 Tcbuc 2337-2461 
G-1 2401 3/---35 Tr. 62 -
G-1 2456 1/---32 1 67 -
G-1 Tcbm 2461-2547 
G-1 2486 1/---33 -67 -
G-1 2499 Tr./- --32 Tr. 66 -
G-1 2506 Tr./- --32 Tr. 63 -
G-1 2544 Tr./- --36 Tr. 64 -
G-1 Tcblc not recognized 
G-1 Tcblv 2547-2601.6 
G-1 2564 Tr./27 40 (c) -3 10 20 -
G-1 2600 -/39 48 (c) -Tr. 2 12 -
G-1 Tcbbt 2601.6-2639.4 
G-1 2606 Tr./26 42 (c) -12 1 17 -
G-1 2607 -/21 26 (c) -20 1 29 -
G-1 2622 Tr./14 30 (c) -21 1 30 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-8 September 2004 
Table G-2. Selected Mineralogic-Abundance Data for Drill Hole USW G-1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
G-1 Tctuv 2639.4-2800 
G-1 2641 2/6 30/--4 7 44 -
G-1 2663 Tr./15 40/--5 4 33 -
G-1 2734 Tr./4 31/--16 8 40 -
G-1 2765 4/Tr. 15/--18 8 52 -
G-1 Tctuc 2800-2840 
G-1 2804 1/--/--31 1 62 -
G-1 2805 3/--/--33 1 62 -
G-1 2820 1/--/--36 Tr. 65 -
G-1 2838 2/--/--31 -64 -
G-1 Tctm 2840-2956 
G-1 2868 1/--/--36 Tr. 63 -
G-1 2884 2/--/--34 -64 -
G-1 2915 2/--/--36 -61 -
G-1 2932 1/--/--36 -64 -
G-1 2937 2/--/--35 -63 -
G-1 2948 2/-2/--30 -67 -
G-1 Tctlc 2956-3005 
G-1 2966 1/--/--36 -67 -
G-1 2981 3/--/--37 -58 -
G-1 Tctlv 3005-3522 
G-1 3018 18/--/4 -33 -34 -
G-1 3079 11/-13/5 -36 -34 -
G-1 3167 10/-19/5 -31 -31 -
G-1 3288 17/-40/1 -18 -20 -
G-1 3401 4/-10/2 -38 -43 
G-1 Tctbt 3522-3558.2 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-9 September 2004 
Table G-2. Selected Mineralogic-Abundance Data for Drill Hole USW G-1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
G-1 3523 7/-13/1 -32 -46 -
Tund 3558.2-bottom of hole 
G-1 3621 24/--/--3 6 59 -
G-1 3810 65/--/----37 -
G-1 3940 48/-2/2 -19 -23 2 
G-1 4246 17/--/6 -38 -38 -
G-1 4400 7/--/13 -39 -42 -
G-1 4503 4/--/19 -39 -37 -
G-1 4555 21/--/2 -22 -47 -
G-1 4612 13/--/7 -34 -41 -
G-1 4626 4/--/--42 -52 -
G-1 4652 7/--/11 -40 -38 -
G-1 4700 6/--/7 -46 -40 -
G-1 4750 8/--/10 -37 -44 -
G-1 4805 8/--/9 -38 -43 -
G-1 4848 6/--/11 -41 -38 -
G-1 4876 4/--/4 -45 -45 -
G-1 4912 7/--/8 -43 -37 -
G-1 4941 27/--/7 -27 -25 -
G-1 4958 8/--/17 -46 -30 -
G-1 4998 30/--/3 -39 -27 -
G-1 5026 5/--/2 -45 -51 -
G-1 5049 52/--/--5 -28 -
G-1 5093 5/--/2 -39 -47 Tr. 
G-1 5126 2/--/15 -50 -33 Tr. 
G-1 5167 2/--/14 -46 -40 Tr. 
G-1 5212 1/--/16 -48 -38 Tr. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-10 September 2004 
Table G-2. Selected Mineralogic-Abundance Data for Drill Hole USW G-1 (Weight Percent) (Continued) 
Smectite/ 
Mordenite 
Clinoptilolite/ 
Analcime Tridymite Quartz CristobaliteOpal Feldspar Glass 
G-1 5253 1/--/27 -36 -35 -
G-1 5296 2/--/19 -37 -39 Tr. 
G-1 5310 29/--/--23 -52 -
G-1 5311 6/-1/--46 -45 -
G-1 5329 11/-12/--40 -40 Tr. 
G-1 5338 41/-1/--28 -26 -
G-1 5348 9/-Tr./- -35 -44 -
G-1 5378 46/-15/--17 -24 -
G-1 5412 5/-12/--40 -40 Tr. 
G-1 5433 35/-20/--9 -27 Tr. 
G-1 5458 26/-25/--18 -28 -
G-1 5477 5/--/12 -44 -36 -
G-1 5498 5/--/17 -38 -41 -
G-1 5534 38/-20/4 -8 -24 -
G-1 5560 5/23 23/--21 -33 Tr. 
G-1 5596 2/-1/7 -34 -50 Tr. 
G-1 5637 3/--/16 -38 -42 1 
G-1 5679 20/--/13 -24 -36 Tr. 
G-1 5699 5/--/9 -33 -50 Tr. 
G-1 5746 12/--/10 -32 -46 1 
G-1 5803 10/--/--29 -54 1 
G-1 5847 13/--/--29 -54 1 
G-1 5898 7/--/12 -42 -34 -
G-1 5947 12/-2/15 -28 -34 1 
G-1 5980 14/-4/--29 -40 2 
Sources: MO0101XRDMINAB.001 [DIRS 163796], MO0004QGFMPICK.000 [DIRS 152554] 
- = not detected; Tr. = trace amount much less than 1% 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-11 September 2004 
Table G-3. Selected Mineralogic-Abundance Data for Drill Hole USW GU-3 (Weight Percent) 
Smectite/ 
Hornblende Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
GU-3 Tpc_un 0-348.1 
GU-3 45.0 -/--6 -17 75 -
GU-3 79.0 -/--25 2 6 70 -
GU-3 103.1 -/--7 2 20 70 -
GU-3 196.3 -/----25 75 -
GU-3 245.7 2/--3 2 25 70 -
GU-3 303.6 <1/- ---25 75 -
GU-3 316.8 -/----22 75 -
GU-3 341.5 3/----30 70 -
GU-3 Tpbt3 375.5-391.7 
GU-3 376.1 4/---3 5 50 40 
GU-3 Tptrn 427.8-542 
GU-3 429.0 -/--4 4 20 70 -
GU-3 430.5 -/--4 -12 80 -
GU-3 465.5 -/--10 -9 80 -
GU-3 482.0 -/--20 -7 70 -
GU-3 520.3 -/--20 -5 70 -
GU-3 525.3 ~1/- -20 1 6 70 -
GU-3 Tptpul 548-688 
GU-3 579.0 ~1/- -12 -22 65 -
GU-3 633.4 ~1/- -7 2 22 70 -
GU-3 674.7 ~1/- -5 2 22 70 -
GU-3 Tptpmn 688-830 
GU-3 702.5 ~1/- --6 17 70 -
GU-3 769.1 ~1/- -6 ~1 22 65 -
GU-3 Tptpll 830-1044 
GU-3 849.4 ~1/- --4 17 75 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-12 September 2004 
Table G-3. Selected Mineralogic-Abundance Data for Drill Hole USW GU-3 (Weight Percent) (Continued) 
Smectite/ 
Hornblende Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
GU-3 910.5 ~1/- --4 27 65 -
GU-3 924.3 ~1/- -10 12 12 65 -
GU-3 951.1 ~1/- -5 8 17 70 -
GU-3 954.8 ~1/- --17 12 70 -
GU-3 1027 -/---6 17 75 -
GU-3 Tptpln 1044-1186.7 
GU-3 1061.0 -/---20 7 70 -
GU-3 1130.3 -/---17 10 70 -
GU-3 1175.0 ~1/- --35 3 60 -
GU-3 Tptpv3 1186.7-1280 
GU-3 1195.7 2/---3 27 35 30 
GU-3 1227 2/---8 17 35 40 
GU-3 Tptpv2 1280-1317 
GU-3 1302.4 2/---4 12 40 45 
GU-3 Tptpv1 1317-1406.3 
GU-3 1322.0 -/---4 7 30 65 
GU-3 1344.8 -/---7 7 30 55 
GU-3 1369.6 -/---4 6 25 65 
GU-3 1394.5 -/---4 6 30 65 
GU-3 1394.6 -/---4 6 30 65 
GU-3 Tac 1412.5-1506.3 
GU-3 1415.5 -/---5 6 35 55 
GU-3 1439.2 Tr./- --20 6 20 55 
GU-3 1439.5 ~1/- --5 6 35 55 
GU-3 1468.5 -/---5 6 35 55 
GU-3 1493.7 ~1/- 2 -5 6 25 65 
GU-3 1498.3 -/-3 -7 6 25 65 
GU-3 Tacbt 1506.3-1553.9 
GU-3 1510.7 3/---5 6 25 65 

Rock Properties Model 
Table G-3. Selected Mineralogic-Abundance Data for Drill Hole USW GU-3 (Weight Percent) (Continued) 
Smectite/ 
Hornblende Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
GU-3 1537.5 3/2 2 -17 4 50 20 
GU-3 Tcpuv 1553.9-1597 
GU-3 1571.6 2/---4 5 45 45 
GU-3 1598.5 moved to 
Tcpuv 
4/---5 2 45 45 
GU-3 Tcpuc 1597-1663 
GU-3 1603.0 -/---2 30 65 -
GU-3 1624.2 -/--7 20 2 70 -
GU-3 1653.3 ~1/- -15 15 3 70 -
GU-3 Tcpm 1663-1744 
GU-3 1709.0 ~1/- --12 12 70 -
GU-3 Tcplc 1744-1755 
GU-3 1744.0 -/---6 25 70 -
GU-3 Tcplv 1755-1992.3 
GU-3 1827.2 2/-30 -4 7 19 38 
GU-3 1874 -/-50 -7 5 40 -
GU-3 1935.8 3/-60 -4 4 30 -
GU-3 1986 -/-70 -4 6 20 -
GU-3 Tcpbt 1992.3-1998.7 
GU-3 1993.1 -/-60 -4 10 25 -
GU-3 Tcblv 2550.8-2617 
GU-3 2577.4 3/-50 -2 5 35 -
GU-3 2615.3 2/-40 -5 7 45 -
GU-3 Tctuv 2637-2719 
GU-3 2656.6 4/-30 -5 8 50 -
GU-3 2695.7 ~1/- 30 -7 10 40 -
Sources: MO0101XRDMINAB.001 [DIRS 163796], MO0004QGFMPICK.000 [DIRS 152554] 
- = not detected; Tr. = trace amount much less than 1% 
MDL-NBS-BS-000004 REV 01 G-13 September 2004 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-14 September 2004 
Table G-4. Selected Mineral-Abundance Data for Drill Hole USW G-4 (Weight Percent) 
Smectite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
G-4 Tptpmn 674-774 
G-4 676 3 -4 4 23 66 -
G-4 694 3 -17 4 13 62 -
G-4 746 ~1 --3 28 68 -
G-4 Tptpln 1127.9-1316.5 
G-4 1163 ---16 15 69 -
G-4 1190 1 --25 13 60 -
G-4 1244 1 --17 15 67 -
G-4 1282 1 --16 18 65 - 
G-4 1283-1293E 1 --6 23 69 -
G-4 1299 2 5 -8 23 62 -
G-4 1301 1 5 -9 20 65 -
G-4 1310 1 3 -5 24 65 -
G-4 1314* 45 28 -2 14 11 
Sources: MO0101XRDMINAB.001 [DIRS 163796], MO0004QGFMPICK.000 [DIRS 152554]. 
*This sample is excluded from the comparison with Tptpln in other drill hole samples because the presence of abundant smectite and clinoptilolite 
means it is from a localized alteration zone. 
- = not detected; Tr. = trace amount much less than 1%. 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-15 September 2004 
Table G-5. Selected Mineral-Abundance Data for Drill Hole USW SD-12 (Weight Percent) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
Unit or Sample Depth Range in feet 
SD-12 Tptrn 330.7-436.4 
SD-12 331.1 Tr./- -4 5 25 63 -
SD-12 337.8 1/--6 -15 79 -
SD-12 376.3 Tr./- -13 -9 77 -
SD-12 395.9 -/--14 Tr. 7 74 -
SD-12 419.8 2/--16 Tr. 10 74 -
SD-12 Tptpul 470.2-663.7 
SD-12 489.6 4/--14 3 21 55 -
SD-12 517.4 3/--9 1 27 56 -
SD-12 534.7 4/--7 3 30 55 -
SD-12 561.9 5/--7 3 28 54 -
SD-12 580.7 5/--6 9 25 55 -
SD-12 602.6 4/--8 12 23 55 -
SD-12 629.7 4/--5 6 29 54 -
SD-12 654.4 3/--7 15 19 56 -
SD-12 Tptpmn 663.7-786.9 30 
SD-12 679.5-679.9 4/--2 8 53 - 
SD-12 706.0-706.1 5/--4 11 26 52 - 
SD-12 733.0-733.3 5/--3 5 34 54 - 
SD-12 759.8-760.1 4/--4 9 28 54 - 
SD-12 785.5-785.8 3/--4 3 34 54 -
SD-12 Tptpll 786.9-1065.5 
SD-12 807.0 4/--3 10 27 53 -
SD-12 831.4 4/--3 33 6 56 -
SD-12 852.9 4/--14 13 16 55 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-16 September 2004 
Table G-5. Selected Mineral-Abundance Data for Drill Hole USW SD-12 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
SD-12 881.1 3/--3 23 17 54 -
SD-12 903.1 5/--4 12 25 54 -
SD-12 929.9 4/--7 24 10 55 -
SD-12 954.7 3/--4 16 22 54 -
SD-12 979.7 2/--3 11 26 54 -
SD-12 Tptpln 1065.5-1278.1 
SD-12 1160.8-1161.1 4/--1 21 20 55 - 
SD-12 1180.9-1181.1 4/--1 24 15 54 - 
SD-12 1200.2-1200.6 3/--2 17 24 54 - 
SD-12 1219.2-1220.2 4/--2 9 27 54 - 
SD-12 1240.4-1240.6 2/--2 16 23 57 - 
SD-12 1261.9-1262.1 3/--2 19 22 53 - 
SD-12 1271.1-1271.3 3/--3 7 30 53 - 
SD-12 1273.4-1273.45 -/-1 2 7 29 56 -
SD-12 Tptpv3 1278.1-1308 
SD-12 1278.5-1278.8 2/---1 -10 72 
SD-12 1291.4-1291.8 -/---1 8 6 85 
SD-12 1301.0-1301.4 Tr./- --Tr. 10 8 82 
SD-12 Tptpv2 1308-1337.5 
SD-12 1309.3 Tr./- --1 20 14 65 
SD-12 1319.0 Tr./- --1 18 15 66 
SD-12 1320.6 Tr./- --1 18 13 68 
SD-12 1333.2 2/---1 8 9 80 
SD-12 Tptpv1 1337.5-1408.1 
SD-12 1342.1 Tr./- --1 4 4 91 
SD-12 1351.9 Tr./- --1 6 5 88 
SD-12 1361.9 -/---1 3 4 92 

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-17 September 2004 
Table G-5. Selected Mineral-Abundance Data for Drill Hole USW SD-12 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
SD-12 1371.4 Tr./- --1 2 4 93 
SD-12 1381.1 1/-2 -1 4 5 87 
SD-12 1388.0 Tr./- 2 -3 5 8 82 
SD-12 1399.2 1/-3 -5 5 12 74 
SD-12 1408.0 8/-1 -2 3 9 77 
SD-12 Tpbt1 1408.1-1411.5 
SD-12 1410.5-1410.6 2/-1 -2 3 7 85 
SD-12 Tac 1411.5-1599.5 
SD-12 1439.9-1440.1 -/-1 -2 2 6 89 
SD-12 1458.7-1458.9 Tr./- 4 -2 2 5 87 
SD-12 1480.2-1480.5 Tr./- 4 -4 4 9 79 
SD-12 1500.8-1501.0 1/-5 -4 4 7 79 
SD-12 1519.8-1520.1 2/-5 -6 12 17 58 
SD-12 1540.9-1541.3 2/-10 -5 9 10 64 
SD-12 1561.1-1561.3 3/-3 -3 5 8 78 
SD-12 1581.3-1581.6 2/-9 -4 5 7 73 
SD-12 Tacbt 1599.5-1648.4 
SD-12 1600.0-1600.3 4/-12 -7 5 10 62 
SD-12 1600.0-1600.3 7/4 51 -2 30 8 - 
SD-12 1601.5-1601.8 5/-68 -4 15 6 - 
SD-12 1609.9-1610.4 3/-30 -11 7 11 34 
SD-12 1620.4-1620.6 4/-59 -8 18 14 - 
SD-12 1641.4-1641.7 9/-14 -8 25 45 -
SD-12 Tcpuv 1648.4-1677 
SD-12 1656.3-1656.8 7/-58 -3 23 13 - 
SD-12 Tcpuc 1677-1787 
SD-12 1680.7-1680.9 2/---35 -54 -

Rock Properties Model 
MDL-NBS-BS-000004 REV 01 G-18 September 2004 
Table G-5. Selected Mineral-Abundance Data for Drill Hole USW SD-12 (Weight Percent) (Continued) 
Smectite/ 
Mordenite Clinoptilolite Tridymite Quartz CristobaliteOpal Feldspar Glass 
SD-12 1700.3-1700.6 3/--2 34 4 55 - 
SD-12 1720.1-1720.4 2/--3 32 2 56 - 
SD-12 1739.8-1740.2 2/--3 32 2 58 - 
SD-12 1760.0-1760.4 3/--3 30 2 60 - 
SD-12 1780.2-1780.4 2/---26 10 60 -
SD-12 Tcpm 1787-1842 
SD-12 1799.4-1800.0 2/--3 31 4 57 - 
SD-12 1821.2-1821.6 Tr./- --21 18 60 - 
SD-12 1840.6-1840.8 5/---9 27 55 -
SD-12 Tcplc 1842-1865 
SD-121860.0-1860.3 2/---5 31 57 -
SD-12 Tcplv 1865-2133 
SD-12 1874.4-1874.5 6/5 10 -4 45 34 - 
SD-12 1891.6-1891.9 5/10 50 -6 11 22 - 
SD-12 1911.8-1911.9 5/4 48 -5 21 19 - 
SD-12 1929.7-1929.9 3/6 51 -4 21 19 - 
SD-12 1950.0-1950.7 3/5 56 -4 16 21 - 
SD-12 1972.1-1972.4 2/-65 -4 15 19 -
SD-12 Tcb 2137.8-bottom 
SD-12 2148.6 2/---26 13 56 - 
SD-12 2163.0 -/---33 6 59 -
Sources: LADV831321AQ97.001 [DIRS 107142], LAJC831321AQ98.005 [DIRS 109004], MO0004QGFMPICK.000 [DIRS 152554] 
- = not detected; Tr. = trace amount much less than 1%. 

APPENDIX H 
SOURCE DATA TRACKING NUMBERS FOR LA9910DB831321.001 


The following text illustrates the source relationship for the source or X-Ray diffraction 
indicators of mineralogical alteration for the RPM, DTN: LA9910DB831321.001. This also 
demonstrates that all data provided by the source DTNs are qualified by Steinborn, T.L. (2002 
[DIRS 160702]), and the qualification activities contained in Appendices E, F, and G of this 
report. 
Rock Properties Model Section 4.1.4 - X-Ray Diffraction Indicators of Mineral Alteration 
LA9910DB831321.001 [DIRS 113568] (TBV #3682) 
1. 
LA000000000086.002 Qualified, Verified using AP-3.15Q Procedure, Developed, 
TBV #3074 removed 
2. 
LA9908JC831321.001 Product Output, Mineralogic Model (MM 3.0) 
3. 
LADB831321AN98.002 Unqualified (TBV #5854) 
4. 
LADV831321AQ97.001 Qualified by TDR-NBS-HS-000005, REV 00 
5. 
LAJC831321AQ98.005 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3066 removed 
6. 
LASC831321AQ96.002 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3067 removed 
7. 
LASL831322AQ97.001 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3071 removed 
LA9908JC831321.001 [DIRS 113495] 
Product Output Data Tracking Number from the Mineralogic Model (MM 3.0) REV 00 
Listing of DTNs from Table 4-1. Data Input. 
1. 
LA000000000086.002 Qualified, Verified using AP-3.15Q Procedure, Developed, 
TBV 3074 removed 
2. 
LA9910JC831321.001 Unqualified (TBV #3657) 
3. 
LADB831321AN98.002 Unqualified (TBV #3065) 
4. 
LADV831321AQ97.001 Unqualified (TBV #0868) 
5. 
LADV831321AQ97.007 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3658 removed 
6. 
LADV831321AQ99.001 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3069 removed 
7. 
LAJC831321AQ98.005 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3066 removed 
8. 
LASC831321AQ96.002 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3067 removed 
9. 
LASC831321AQ98.001 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3072 removed 
10. 
LASC831321AQ98.003 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3068 removed 
11. 
LASL831322AQ97.001 Qualified, Verified using AP-3.15Q Procedure, Acquired, 
TBV #3071 removed 

Steinborn, T.L. 2002 [DIRS 160702], Table 10, Data Qualification Report: Mineralogy Data 
for Use on the Yucca Mountain Project. TDR-NBS-HS-000005, REV 00 
3. 
LADB831321AN98.002 (a) MO0101XRDMINAB.001, Qualified by 
TDR-NBS-HS-000005, REV 00 
Boreholes: UE-25b#1, USW G-1, USW G-3, USW GU-3, 
and USW G-4 
(b) 
MO0101XRDDRILC.000, Not qualified by 
TDR-NBS-HS-000005, REV 00 
Borehole: UE-25 J-13 (not an input to MM3.0) 
(c) 
MO0106XRDDRILC.003, Qualified by 
TDR-NBS-HS-000005, REV 00 
Borehole: USW H-6 
(d) 
MO0101XRDDRILC.002, Qualified by 
TDR-NBS-HS-000005, REV 00 
Boreholes: UE-25 a#1 and USW G-2 
(e) 
MO0101XRDDRILC.001, Not qualified by 
TDR-NBS-HS-000005, REV 00 
Cuttings samples from boreholes. 
4. 
LADV831321AQ97.001 Qualified by TDR-NBS-HS-000005, REV 00 
The qualification activity contained in this report MDL-NBS-GS-000004, REV 01 
(e) 
MO0101XRDDRILC.001Qualified by the activity documented in this report. 
Cuttings, sidewall samples, and intermittent core samples. 
LADV831321AQ97.001  USW H-3 sidewall core and 
cuttings samples 
LADB831321AN98.002  UE-25 J-12 cuttings samples 
USW H-3 sidewall core and cuttings samples 
USW H-4 sidewall core and cuttings samples 
USW WT-1 cuttings samples 
UE-25 p#1 intermittent core, sidewall core, and cuttings 
samples 
USW WT-2 intermittent core and cuttings samples 
USW H-5 sidewall core and cuttings samples. 
(I) 
LADV831321AQ97.001 Qualified by the activity documented in this report. 
TDR-NBS-HS-000005 qualification of software POWD10 
did not qualify cuttings samples in DTN. 
(II) 
LADV831321AQ97.007 Qualified by the activity documented in this report 
(III) LA9910JC831321.001 Qualified by the activity documented in this report. 

APPENDIX I 
ETYPE


I1. JUSTIFICATION FOR CHANGE IN ETYPE CITATION 
For this model report the citation of the software code ETYPE has been changed from the routine 
documented in Geostatistical Modeling of Porosity and Derivative Properties for Fiscal Year 
1998 (Rautman and McKenna 1998 [DIRS 107442], pages 1134-1142), to the baselined version 
(STN: 10731-2-2.01-00 [DIRS 159417]). Justification for this change is that the test case results 
for the ETYPE routine on page 1138 of Rautman and McKenna were duplicated using the 
baselined version of the code. 
Documentation of this justification is provided in three parts. First, a description of the software 
code ETYPE is provided in Sections I1.1 and I1.2. The basis of this description is the 
ETYPE Software Management Report (SNL 2002 [DIRS 171419]). Second, documentation of 
the ETYPE routine from Rautman and McKenna (1998 [DIRS 107442]) is provided in 
Section I1.3. Third, the result for the ETYPE routine test case (Rautman and McKenna 1998 
[DIRS 107442] p. 1138 and reproduced on page I-7) are provided in Section I1.4. 
I1.1 BACKGROUND 
In many instances, multiple data sets (or files) are identically formatted. The position of each 
data component in the set is usually meaningful. For example, row i and column j (for each 
data file) might stand for temperature (parameter j) at a certain well at a certain depth (parameter 
i). Each individual file/data set might represent a particular date. When it is desirable to obtain 
statistical measures for properties such as these, the analyst must be able to enter all of the data 
sets or files, then access and develop the data accordingly. 
The ETYPE program computes the mean and standard deviation of multiple realizations saved in 
separate files, along the lines of the above example. 
I1.2 FORMULAS AND EQUATIONS 
ETYPE calculates the standard deviation of a set of input numbers and therefore also calculates 
the mean of those numbers. 
The equation for the mean,  is: 
N 
. Xi 
= i =1 
(Eq. I-1) 
N 
where, 
Xi = the elements or variables for which the mean is being calculated. 
N = the number of elements or variables 

The equation of the standard deviation s , is: 
N 
2
. (- X )
i 
i= 1
s= 
(Eq. I-2) 
N - 1 
where, 
= the calculated mean value 
I1.3 DOCUMENTATION OF THE ETYPE SOFTWARE ROUTINE 
This section reproduces the documentation of the software routine ETYPE in Geostatistical 
Modeling of Porosity and Derivative Properties for Fiscal Year 1998. The test case input values 
and results are documented on page 1138 of the document (Rautman and McKenna 1998 
[DIRS 107442], pages 1134-1142). 










I1.4 DOCUMENTATION OF THE DUPLICATION OF ETYPE ROUTINE TEST 
CASE RESULTS 
The following is a description of the ETYPE parameter file: 
****** e-type paramter file ****** 
4 /number of simulations 
etype_test_ /base file name 
.inp /file name extension 
1 /column number 
5 /nxnynz (number of data) 
etype_test.out /output file name 
etype test results /title to appear in output file 
The parameter file was used to create the input test files 1 through 4 that duplicate the test case 
described on page 1138 of Geostatistical Modeling of Porosity and Derivative Properties for 
Fiscal Year 1998 (Rautman and McKenna 1998 [DIRS 107442]). 
e-type test file #1 e-type test file #2 e-type test file #3 e-type test file #4 
1 1 1 1 
data #1 data #1 data #1 data #1 
0.3375 0.007 0.2680 0.3030 
0.3375 0.007 0.2680 0.3030 
0.3375 0.007 0.2680 0.3030 
0.3375 0.007 0.2680 0.3030 
0.3375 0.007 0.2680 0.3030 
The ETYPE test results are provided below: 
etype test results 
2 
Mean 
Standard Deviation 
0.229 0.151 
0.229 0.151 
0.229 0.151 
0.229 0.151 
0.151 
The test results documented on page 1138 of Geostatistical Modeling of Porosity and Derivative 
Properties for Fiscal Year 1998 provide a mean value of 0.2289 and a standard deviation of 
0.1506 (Rautman and McKenna 1998 [DIRS 107442]). Therefore, the test results from the 
ETYPE routine documented in Rautman and McKenna and the baselined ETYPE 
(STN: 10731-2-2.01-00 [DIRS 159417]) are identical, with the exception that the baselined code 
provides results to three significant figures after the decimal point. 

In conclusion, this change in significant figures between the two versions of the code has no 
impact on the product output. The input parameters documented in Section 4.1 (porosity, bulk 
density, thermal conductivity, and mineral alteration indicators) all provide data with two 
significant figures after the decimal point. Therefore, calculation and ETYPE representation of 
product outputs as shown throughout the document are similarly restricted to two significant 
figures after the decimal point. Calculation of the mean value and standard to three rather than 
four significant figures after the decimal point has no impact on the product output or this 
comparison of the two versions of the code. 

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