Title: Analysis of Hydrologic Properties Data Rev 00

MDL-NBS-HS-000014

10 April 2003 


1. PURPOSE
This Model Report describes the methods used to determine hydrologic properties based on the 
available field data from the unsaturated zone (UZ) at Yucca Mountain, Nevada, and documents 
validation of the active fracture model (AFM). This work was planned in Technical Work 
Plan (TWP) for: Performance Assessment Unsaturated Zone (BSC 2002 [160819], Sections 
1.10.2, 1.10.3, and 1.10.8). Fracture and matrix properties are developed by analyzing available 
survey data from the Exploratory Studies Facility (ESF), Cross Drift for Enhanced 
Characterization of Repository Block (ECRB), and/or boreholes; air injection testing data from 
surface boreholes and from boreholes in the ESF; and data from laboratory testing of core 
samples. The AFM is validated on the basis of experimental observations and theoretical 
developments. This report is a revision of an Analysis Model Report, under the same title, as a 
scientific analysis with Document Identifier number ANL-NBS-HS-000002 (BSC 2001 
[159725]) that did not document activities to validate the AFM. 
The principal purpose of this work is to provide representative uncalibrated estimates of fracture 
and matrix properties for use in the model report Calibrated Properties Model (BSC 2003 
[160240]). The present work also provides fracture geometry properties for generating dual-
permeability grids as documented in the Scientific Analysis Report, Development of Numerical 
Grids for UZ Flow and Transport Modeling (BSC 2003 [160109]). The resulting calibrated 
property sets and numerical grids from these reports will be used in the Unsaturated Zone Flow 
and Transport Process Model (UZ Model), and Total System Performance Assessment (TSPA) 
models. 
The fracture and matrix properties developed in this Model Report include: 
 Fracture properties (frequency, permeability, van Genuchten a and m parameters, 
aperture, porosity, and interface area) for each UZ Model layer 
 Matrix properties (porosity, permeability, and van Genuchten aand m parameters) for 
each UZ Model layer 
 Thermal properties (grain density, wet and dry thermal conductivity, and grain specific 
heat) for each UZ Model layer 
 Fault properties for each major hydrogeologic unit. 
These properties incorporate the available measurement data, as applicable, to estimate fracture 
and matrix properties. Field data from liquid release testing in the ESF and other relevant data 
are also used to validate these properties and provide bounds on property values. 
Another objective of this report is to document activities to validate the AFM based on 
experimental observations and theoretical developments. The AFM is a conceptual model that 
describes the fracture-matrix interaction in the UZ of Yucca Mountain. It is intended to be used 
to represent the hydrologic characteristics of rock fractures for the UZ Flow Model, 
UZ Radionuclide Transport Model under ambient conditions, Mountain-Scale and Drift-Scale 
Thermal-Hydrological-Chemical Models, and Multiscale Thermohydrology Model. These 
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Analysis of Hydrologic Properties Data U0090 
validation activities are documented in Section 7 of this report regarding use of independent lines 
of evidence to provide additional confidence in the use of the AFM in the UZ models. 
This model report was planned in the TWP (BSC 2002 [160819], Section 1.10) and there are 
some deviations from the TWP in this report. Data from the large-infiltration-plot test at Alcove 
8 / Niche 3 and data from the large-block test have not been used for model validation because 
they were not available in time for this report. An interpretation of the AFM based on fractal 
flow patterns is discussed in Section 6.7, in addition to the alternative model (film-flow model in 
Section 6.8) that was planned in the TWP (BSC 2002 [160819], Section 2.7). Software infil2grid 
V1.7 (LBNL 2002 [154793]) was used, not V1.6 (LBNL 1999 [134754]) as planned in the TWP 
(BSC 2002 [160819], Table II-2). This is because infil2grid V1.7 (LBNL 2002 [154793]) can 
handle eight-character grid element names, while infil2grid V1.6 (LBNL 1999 [134754]) cannot. 
Uncertainties associated with calibration measurement and test equipment (BSC 2002 [160819], 
Table 2-5) are not discussed in this report because they are already reflected in the measurements 
and expected to be insignificant compared with uncertainties associated with data limitation and 
spatial variability. 
A list of relevant features, events, and processes (FEPs) is presented in Section 6. Constraints 
and limitations are as follows: the fracture permeability, van Genuchten fracture aand m, matrix 
permeability, and van Genuchten matrix aand m reported here are uncalibrated and serve only 
as initial estimates in the Calibrated Properties Model. The calibrated properties, as well as the 
other properties, are intended for use in the mountain-scale and drift-scale UZ Models. Data 
availability is associated with parameter uncertainty that is represented in the uncertainty 
estimates given in this report. The use of the AFM is limited to conditions under which the 
continuum approach and the relevant hypotheses of the AFM are valid (CRWMS M&O 2000 
[141187], Section 6.4; Section 6.6 of this report). 
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2. QUALITY ASSURANCE
Development of this model report and the supporting modeling activities have been determined 
to be subject to the Yucca Mountain Projects quality assurance (QA) program (BSC 2002 
[160819], Section 8.2, Work Package (WP) AUZM06). Approved QA procedures identified in 
the TWP (BSC 2002 [160819], Section 4) 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 2002 [160819], Section 8.4, WP AUZM06) during the 
modeling and documentation activities. 
This model report provides uncalibrated values for hydrologic properties of natural barriers 
identified in AP-2.22Q, Classification Criteria and Maintenance of the Monitored Geologic 
Repository Q-List as Quality Level  1 items important to waste isolation. The report 
contributes to the analyses and modeling data used to support performance assessment (PA). 
The conclusions of this model report do not affect the proposed repository design or permanent 
items as discussed in AP-2.22Q. 
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3. USE OF SOFTWARE 
The software programs used in this study are listed in Table 1. These are appropriate for the 
intended application and were used only within the range of validation. They were obtained from 
Software Configuration Management, and qualified under AP-SI.1Q, Software Management. 
Table 1. Qualified Software Used in this Report 
Software Name Version Software Tracking Number (STN) DIRS Reference 
Number 
TOUGH2 1.4 10007-1.4-01 146496 
T2R3D 1.4 10006-1.4-00 146654 
infil2grid 1.7 10077-1.7-00 154793 
Standard Excel spreadsheets and visual display graphics programs (Excel 97 SR-1 and Tecplot 
V7.0) were also used but are not subject to software quality assurance requirements. All 
information needed to reproduce the work using these standard software programs is included in 
this report, with references specified (See Attachment III). Names of files based on these 
programs are given in Sections 6 and 7. (Excel files involving computations are specified in 
Sections 6.1 and 6.2.) A detailed description of these files are presented in Attachment III. 
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4. INPUTS
Fracture properties are developed based on available fracture survey data from the ESF, ECRB 
Cross Drift, and boreholes and air-injection testing data from vertical boreholes and ESF alcoves. 
Matrix properties are determined by combining core and small-scale matrix property data. 
Properties are determined by computing means, standard deviations, and standard errors for each 
UZ Model layer (BSC 2003 [160109]) for each property. Fracture porosities are determined 
based on the analyses of gas tracer data from the ESF. When no data for a specific layer are 
available, analogs are identified and used to assign properties. Field-testing data from the ESF 
are used to confirm the magnitude of parameters by establishing appropriate bounds for values. 
Fracture mineral coating data collected from ESF and carbon-14 data from vertical boreholes are 
used for validating the AFM. The data used in this report are appropriate for this study because 
they represent fracture and matrix properties available from the UZ at Yucca Mountain. The 
appropriateness of the data is also discussed in Sections 6 and 7 when they are used for 
developing UZ properties or validating the AFM. 
4.1 DATA AND PARAMETERS 
4.1.1 Data 
The input data used in property-set development include the following: 
 Fracture properties developed from detailed line survey (DLS) fracture data (collected 
from the ESF North and South Ramps, Main Drift, and ECRB Cross Drift and which 
provide spatially varying frequency, length, and fracture dips and strikes) and fracture 
frequency data from boreholes (DTN: LB990501233129.001 [106787]). 
 Air-injection testing data (from vertical boreholes) that are used for fracture permeability 
estimates (DTNs: GS960908312232.013 [105574] through LB980120123142.005 
[114134] in Table 2). 
 Air-injection and/or gas tracer data from the Upper Tiva Canyon, Bow Ridge fault, and 
Upper Paintbrush Contact Alcoves, the Single Heater Test (SHT) area, and the Drift 
Scale Test (DST) area that provide fracture permeability and porosity estimates (DTNs: 
LB990901233124.004 [123273], LB980912332245.002 [105593] and 
GS990883122410.002 [135230]). 
 Data from liquid release test in the ESF niches that provide fracture van Genuchten a 
and porosity estimates (DTNs: LB980901233124.003 [105592] and 
LB0110LIQR0015.001 [156907]). 
 Measured properties from core samples (including effective porosity, bulk density, 
porosity, particle density, volumetric water content, saturation, water potential, hydraulic 
conductivity, matrix van Genuchten a and m values, and residual saturation) and 
stratigraphic descriptions for samples from boreholes that are used for developing matrix 
properties for UZ model layers (DTNs: MO0109HYMXPROP.001 [155989] through 
GS940208314211.008 [145581] in Table 2). 
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 Thermal properties by lithostratigraphic unit, developed from small-scale measurements. 
Development of these properties is documented in a separate model report describing 
thermal conductivity of the potential repository horizon (BSC 2002 [160319]) (DTNs: 
SN0206T0503102.005 [160258] and SN0208T0503102.007 [160257]). 
 Numerical grids and calibrated properties that are used for validating the AFM 
(Table 2). Development of these data are documented in a separate scientific analysis 
report (BSC 2003 [160109]) and a model report describing the Calibrated Properties 
Model, respectively (BSC 2003 [160240]). 
Specific input data sets and the associated Data Tracking Numbers (DTNs) are provided in 
Table 2. Geology information from ESF (CRWMS M&O 1998 [102679], Table 2) is also used 
for estimating matrix properties (Attachment III). This report does not discuss Principal Factors 
as defined in Attachment I of AP-3.15Q, Managing Technical Product Inputs. 
Table 2. Data Tracking Numbers for Input Data Used 
Data Description DTN Data Use* 
Matrix saturation, water potential, and 
hydrologic property data MO0109HYMXPROP.001 [155989] 6.1.3.3 
Att. III 
Physical properties and water potential for 
borehole samples from USW WT-24 GS980708312242.010 [106752] Att. III 
Physical properties and water potential for 
borehole samples from USW SD-6 GS980808312242.014 [106748] Att. III 
Physical properties and hydraulic-conductivity 
measurements from USW WT-24 GS980708312242.011 [107150]** Att. III 
Physical properties and saturated-hydraulic-
conductivity measurements from USW SD-6 GS980908312242.038 [107154]** Att. III 
Physical properties and saturated-hydraulic-
conductivity measurements from boreholes 
USW SD-7, USW SD-9, USW SD-12, USW UZ14 
and UE-25 UZ#16 
GS980908312242.041 [107158]** Att. III 
Measured physical and hydraulic properties of 
core samples from Busted Butte boreholes 
GS990308312242.007 [107185] 
GS990708312242.008 [109822] Att. III 
Physical properties and saturated hydraulic 
conductivity of cores from surface samples from GS971008312231.006 [107184]** Att. III 
the ESF main drift 29+00m to 57+00m 
Water-retention data of borehole samples and 
surface samples from ESF north ramp GS980908312242.037 [107180]** Att. III 
NOTE: * Sections where the data used are described in detail. 
** These DTNs are not used for calculating Principal Factors. 
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Table 2. Data Tracking Numbers for Input Data Used (Continued) 
Data Description DTN Data Use* 
Unsaturated hydraulic properties of borehole 
samples from the PTn exposure in the ESF 
north ramp 
GS980408312242.008 [107161]** Att. III 
Physical properties and saturated hydraulic 
conductivity measurements of core samples 
from boreholes in the ESF north ramp 
GS980908312242.040 [107169]** Att. III 
Physical properties of borehole samples from 
the PTn exposure in the ESF north ramp 
GS980308312242.005 [107165]** Att. III 
Unsaturated water-retention data for samples 
from USW SD-6 
GS980908312242.039 [145272] Att. III 
Moisture-retention data for samples from 
boreholes USW SD-7, USW SD-9, USW SD-12 
and UE-25 UZ#16 
GS960808312231.003 [147590] Att. III 
Unsaturated hydraulic properties from USW 
WT-24 
GS980808312242.012 [149375]** Att. III 
Moisture-retention data from boreholes USW 
UZ-N27 and UE-25 UZ#16. 
GS950608312231.008 [144662] Att. III 
Unsaturated hydraulic conductivity and water 
potential in Busted Butte volcanic tuff cores 
GS010608312242.001 [160822] Att. III 
Lithostratigraphic classification of core samples 
for the Busted Butte Phase 2 test block 
LA0207SL831372.001 [160824]** Att. III 
Lithostratigraphic information and chemical 
analyses from drill cores collected in ESF 
LAJF831222AQ98.014 [160825]** Att. III 
Stratigraphic description and data for the Yucca 
Mountain tuff in boreholes NRG#2B, NRG-7/7A, 
SD-9, UZ-14, UZ#16, UZ-N11, UZ-N33, UZN34, 
UZ-N53, UZ-N54, UZ-N55 
GS950108314211.009 [152556] Att. III 
USW UZ-7a shift drilling summaries, lithologic 
logs, structural logs, weight logs, and composite 
borehole log from 0.0 to 770.0 
TM000000UZ7ARS.001 [160826]** Att. III 
Table of contacts in borehole USW UZ-N35 GS940208314211.007 [155533] Att. III 
Table of contacts for the Tiva Canyon tuff in 
borehole USW UZ-N36 
GS940308314211.018 [145589] Att. III 
Lithostratigraphic data for Paintbrush Group 
bedded tuff units in boreholes USW UZ-N11, 
USW UZ-14, USW NRG-7/7A, USW SD-9, 
USW UZ-N37, USW NRG-6, UE-25 NRG#2B, 
USW UZ-N31, USW UZ-N32, USW SD-12, UE25 
UZ#16, USW UZ-N54, USW UZ-N53 
GS950108314211.008 [152558] Att. III 
Stratigraphic descriptions of the Pah Canyon 
tuff in boreholes UE-25 NRG#2B, UE-25 
NRG#4, USW NRG-6, USW NRG-7/7A, USW 
SD-9, USW SD-12, USW UZ-14, USW UZ-N31, 
USW UZ-N32, and USW UZ-N37 
GS950708314211.028 [160827] Att. III 
Tables of contacts in boreholes USW UZ-N57, 
UZ-N58, UZ-N59, and UZ-N61 
GS940208314211.008 [145581] Att. III 
NOTE: * Sections where the data used are described in detail. 
** These DTNs are not used for calculating Principal Factors. 
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Table 2. Data Tracking Numbers for Input Data Used (Continued) 
Data Description DTN Data Use* 
Developed fracture hydrologic properties for UZ LB990501233129.001 [106787] 6.1.2 
model layers (FY99) 6.1.3.1 
6.1.3.3 
Air permeability data from vertical boreholes GS960908312232.013 [105574]** Section 6.1.1 
Figure 2 
Table 6 
Att. III 
Air permeability data from Alcoves 1,2,3 GS970183122410.001 [105580]** Table 6 
Att. III 
Air-injection and permeability data-SHT area LB960500834244.001 [105587]** Figure 2 
Table 6 
Att. III 
Air-injection and permeability data-DST area LB970600123142.001 [105589]** Figure 2 
LB980120123142.004 [105590]** Table 6 
LB980120123142.005 [114134]** Att. III 
Pre-excavation air-permeability data from LB990901233124.004 [123273] Figure 2 
Niches 3566, 3650 3107, and 4788 
Air-injection, tracer test, and fracture porosity LB980912332245.002 [105593] 6.1.3.1 
data 6.1.3.2 
6.1.3.3 
Ghost Dance fault permeability GS990883122410.002 [135230]** 6.4 
ESF seepage test data LB980901233124.003 [105592]** 6.5.1, 6.5.2 
6.5.3 
6.1.3.4 
ESF seepage test data LB0110LIQR0015.001 [156907] 6.5.3 
6.1.3.4 
Geologic Framework Model (GFM2000) MO0012MWDGFM02.002 [153777] 6.4 
Att. III 
1-D site-scale calibrated properties: 
supporting files 
LB02091DSSCP3I.001 [161292] 7.2.1 
Calibrated hydrologic properties for UZ model LB02091DSSCP3I.002 [161433] 7.2.1 
Supporting files of calibrated hydrologic LB0208UZDSCPMI.001 [161285] 7.2.2 
properties for UZ model 
Yucca Mountain Project borehole locations MO9906GPS98410.000 [109059] 7.2.1 
Diffusion of sorbing and non-sorbing LA000000000034.002 [148603] 7.2.1 
radionuclides 
NOTE: * Sections where the data used are described in detail. 
** These DTNs are not used for calculating Principal Factors. 
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Table 2. Data Tracking Numbers for Input Data Used (Continued) 
Data Name DTN Data Use* 
Infiltration maps GS000308311221.005 [147613] 7.2.2 
3D UZ numerical grid LB03023DKMGRID.001 [162354] 7.2.2 
Thermal properties of nonrepository geologic 
layers SN0206T0503102.005 [160258] 6.3 
Thermal conductivity of the potential repository 
horizons SN0208T0503102.007 [160257] 6.3 
NOTE: * Sections where the data used are described in detail. 
** These DTNs are not used for calculating Principal Factors. 
4.1.2 Parameters and Parameter Uncertainties 
This model report describes the development of hydrologic properties based on the available 
field data from the UZ at Yucca Mountain, Nevada, and documents activities to validate the 
AFM. The input parameters for validating the AFM include the calibrated hydrologic property 
set (porosity, permeability, and van Genuchten parameters) for the UZ model listed in Table 2 
(DTN: LB02091DSSCP3I.002 [161433]). The development of the calibrated property sets is 
documented in the model report Calibrated Properties Model (BSC 2003 [160240]). (Note that 
uncalibrated properties (including DTN: LB990501233129.001 [106787]) are not used for the 
model validation activities described in Section 7 of this report.) Uncertainties of these calibrated 
properties are approximated, based on property measurements at small scales, and result from the 
development of hydrologic properties for UZ Model layers to be discussed in this report. (The 
determination of these uncertainties is presented in Calibrated Properties Model (BSC 2003 
[160240]).) A key parameter in the AFM is the active fracture parameter . (Liu et al. 1998 
[105729]). Evaluation of its uncertainty, by comparing simulation results corresponding to 
different . values with field observations, is part of the model validation activity to be discussed 
in Section 7 of this report. 
4.2 CRITERIA 
The general requirements to be satisfied by TSPA are stated in 10 CFR 63.114 [156605] 
(Requirements for performance assessment). Technical requirements to be satisfied by TSPA are 
identified in the Yucca Mountain Project Requirements Document (Curry and Loros 2002 
[157916]). 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 Yucca 
Mountain Review Plan, Information Only (YMRP; NRC 2003 [162418]). The pertinent 
requirements and criteria for this model report are summarized in Table 3. 
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Table 3. Project Requirements and YMRP Acceptance Criteria Applicable to this Model Report 
Requirement 
Numbera 
Title 10 CFR 63 Link YMRP Acceptance 
Criteria 
PRD-002/T-015 Requirements for Performance 10 CFR 63.114 (ac, eg) Criteria 1 to 3 for Flow 
Assessment Paths in the Unsaturated 
Zoneb 
PRD-002/T-016 Requirements for Multiple Barriers 10 CFR 63.115 (ac) Criteria 1 to 3 for 
10 CFR 63.113 (a) Demonstration of Multiple 
Barriersc 
a
NOTE: 
b from NRC (2003 [162418], Section 2.2.1.3.6.3)
from Curry and Loros (2002 [157916]) 
c 
from NRC (2003 [162418], Section 2.2.1.1.3) 
The criteria identified in Section 2.2.1.1.3 Acceptance Criteria, (for Section 2.2.1.1 System 
Description and Demonstration of Multiple Barriers), which are based on meeting the 
requirements at 10 CFR 63.113(a) and 63.115(a)(c) are: 
 Acceptance Criterion 1, Identification of Barriers is Adequate: 
The hydrogeologic units above and below the proposed repository are natural barriers 
important to waste isolation. Their capability is determined by their hydrologic 
properties. 
 Acceptance Criterion 2, Description of Barrier Capability to Isolate Waste is Acceptable: 
The capability of the identified barriers to prevent or substantially delay the movement 
of water or radioactive materials is adequately identified and described. The uncertainty 
associated with barrier capabilities is adequately described. 
 Acceptance Criterion 3, Technical Basis for Barrier Capability is Adequately Presented: 
The technical bases are consistent with the technical basis for the performance 
assessment. The technical basis for assertions of barrier capability is commensurate 
with the importance of each barriers capability and the associated uncertainties. 
The criteria identified in Section 2.2.1.3.6.3 Acceptance Criteria (for 2.2.1.3.6 Flow Paths in the 
Unsaturated Zone), which are based on meeting the requirements of 10 CFR 63.114(a)(c) and 
(e)(g), relating to flow paths in the saturated zone model abstraction, are: 
 Acceptance Criterion 1, System Description and Model Integration are Adequate: 
The process-level model flow for paths in the unsaturated zone adequately incorporates 
important physical phenomena, and uses consistent and appropriate assumptions 
throughout. 
The aspects of geology, hydrology, and physical phenomena that may affect flow paths 
in the unsaturated zone are adequately considered. Conditions and assumptions in the 
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process-level model of flow paths in the unsaturated zone are readily identified and 
consistent with the body of data presented in the description. 
The process-level model of flow paths in the unsaturated zone uses assumptions, 
technical bases, and data that are appropriate and consistent with other process-level 
models. The descriptions and technical bases are transparent and traceable to site and 
design data. 
Adequate spatial variability of model parameters and boundary conditions are 
employed in process-level models to estimate flow paths in the unsaturated zone. 
Average parameter estimates used in the process-level model of flow paths in the 
unsaturated zone are representative of the temporal and spatial discretizations 
considered in the model. 
 Acceptance Criterion 2, Data Are Sufficient for Model Justification: 
Hydrological values used in the safety case are adequately justified. Adequate 
descriptions of how data were used, interpreted, and appropriately synthesized into the 
parameters are provided. 
Sensitivity or uncertainty analyses are performed to assess data sufficiency, and 
determine the possible need for additional data. 
Accepted and well-documented procedures are used to construct numerical models. 
Reasonably complete process-level conceptual and mathematical models are used in the 
analyses. In particular: (i) mathematical models are provided that are consistent with 
conceptual models and site characteristics; and (ii) the robustness is demonstrated by 
comparison of results from different mathematical models. 
 Acceptance Criterion 3, Data Uncertainty Is Characterized and Propagated Through the 
Model Abstraction: 
Models use parameter values, assumed ranges, probability distributions, and/or 
bounding assumptions that are technically defensible, and reasonably account for 
uncertainties and variabilities. 
The technical bases for the parameter values used are provided. 
The initial conditions, boundary conditions, and computational domain used in 
sensitivity analyses and/or similar analyses are consistent with available data. 
Parameter values are consistent with the initial and boundary conditions and the 
assumptions of the conceptual models for the Yucca Mountain site. 
Uncertainties in the characteristics of the natural system are considered. 
4.3 CODES AND STANDARDS 
No specific formally established standards have been identified as applying to this analysis and 
modeling activity. 
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5. ASSUMPTIONS
This section documents the only assumption made to determine hydrologic properties and 
validate the AFM, based on data available from the UZ of Yucca Mountain. This assumption is 
as follows: 
1. Since a systematic approach for upscaling properties directly from small-scale 
measurements is still lacking for unsaturated fractured rock, simple averaging schemes 
are assumed to be appropriate in most cases for the upscaling purpose in the analyses 
to be reported in this study. The relation of Paleologos et al. (1996 [105736], p. 1336), 
originally developed for porous media, is assumed to be appropriate for upscaling 
matrix permeability when an upper limit of 1.5 orders of magnitude is used for the 
amount of upscaling. Hydrologic property data have been determined on scales that 
are generally much smaller than the scales characterizing the subsurface heterogeneity 
(e.g., characteristic sizes for model layers). While considerable progress has been 
made in developing upscaling schemes for porous media, the scale-dependent behavior 
of a hydrologic property for fractured rocks can be very different from that for porous 
media. For example, the existence of fractures in a fractured rock, which may act as a 
capillary barrier, can increase tortuosity of liquid water in the matrix, and therefore 
reduce large-scale matrix permeability compared with the case without fractures. It is 
necessary to make this assumption to determine rock properties using small-scale 
measurements. Note that the rock properties developed herein are mainly used as 
initial estimates for use in the inversion process documented in a separate Model 
Report describing the Calibrated Properties Model (BSC 2003 [160240]). The 
upscaling issue is further considered in the inversion process, which provides large-
scale properties by matching the large-scale simulation results with grid block-scale 
observations averaged from small-scale data. Based on the above reasoning, no 
confirmation is needed for this assumption. This assumption is used in Sections 6.1 
and 6.2. 
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6. MODEL DISCUSSION
In this section, the methodologies and data used to determine representative estimates of the 
fracture and matrix properties for the UZ Model layers are discussed. The concept and 
mathmatical formulations of the AFM are also presented. Table 4 shows the relationships 
between the lithostratigraphy of the Geologic Framework Model (GFM2000) (BSC 2002 
[159124]) and the UZ Model layers, as documented in a Scientific Analysis Report describing 
development of numerical grids for UZ flow and transport modeling (BSC 2003 [160109], Table 
11). (Note that the relationship between major units and hydrogeologic units in Table 4 is 
slightly different from Table 1 of Flint (1998 [100033]). Most of these estimates of fracture and 
matrix properties are used as inputs in the inversion modeling studies documented in the Model 
Report Calibrated Properties Model (BSC 2003 [160240]). The key scientific notebooks (with 
relevant page numbers) used for this study are listed in Table 5. One assumption is used in this 
section, as presented in Section 5. The intended use of the output data, developed using 
approaches in this section, is given in Section 1. 
In this report, the subsurface heterogeneity of the Yucca Mountain UZ is represented by a 
number of model layers, each of which is considered to have uniform hydrologic properties. This 
is based on the following considerations: First, the overall behavior of flow and transport 
processes in the Yucca Mountain UZ is mainly determined by relatively large-scale 
heterogeneities introduced by stratification of the tuffs. Second, the complexity of models for 
heterogeneity needs to be consistent with data availability. Third, this layered approach is 
supported by field observations, such as matrix water saturation distributions. For a given 
geologic unit, measured matrix saturation distributions can be very similar in different boreholes 
(Flint 1998 [100033], pp. 2430, Figures 59), indicating that matrix flow behavior and effective 
hydraulic properties should be similar within the unit. Further discussion of this issue is 
provided in a separate Model Report describing conceptual and numerical models for UZ flow 
and transport (CRWMS M&O 2000 [141187], Section 6.4.3). 
It should be noted that van Genuchten (1980 [100610]) relationships, originally developed for 
porous media, are used as constitutive relations for the active fracture continuum defined in the 
AFM (Section 6.6 of this report). Not all connected fractures are active in conducting liquid 
water in the UZ of Yucca Mountain (Liu et al. 1998 [105729], pp. 26382641). The active 
fracture continuum consists of fractures that actively conduct liquid water. The use of van 
Genuchten relationships is based on a conceptual model that describes flow in fractures using 
porous medium equivalence. A further discussion of this conceptual model is provided in a 
separate Model Report, Conceptual and Numerical Models for UZ Flow and Transport 
(CRWMS M&O 2000 [141187], Section 6.4.4). Recently, Liu and Bodvarsson (2001 [160110]) 
developed a new constitutive-relationship model for unsaturated flow in fracture networks, based 
mainly on numerical experiments. They found that the van Genuchten model is consistent with 
the new model for low fracture saturations corresponding to the ambient conditions. 
MDL-NBS-HS-000014 REV00 27 April 2003 

Analysis of Hydrologic Properties Data U0090 
Table 4. GFM2000 Lithostratigraphy, UZ Model Layer, and Hydrogeologic Unit Correlation 
Major Unit(Modified from Montazer and 
Wilson 1984 [100161]) 
GFM2000 
LithostratigraphicNomenclature 
UZ Model Layer 
(BSC 2003 [160109], Table 11) 
Hydrogeologic 
Unit (Flint 1998 
[100033], Table 1) 
Tiva Canyon welded 
(TCw) 
Tpcr tcw11 CCR, CUC 
Tpcp tcw12 CUL, CW 
TpcLD 
Tpcpv3 tcw13 CMW 
Tpcpv2 
Paintbrush nonwelded Tpcpv1 ptn21 CNW 
(PTn) Tpbt4 ptn22 BT4 
Tpy (Yucca) 
ptn23 TPY 
ptn24 BT3 
Tpbt3 
Tpp (Pah) ptn25 TPP 
Tpbt2 ptn26 BT2 
Tptrv3 
Tptrv2 
Topopah Spring welded Tptrv1 tsw31 TC 
(TSw) Tptrn 
tsw32 TR 
Tptrl, Tptf tsw33 TUL 
Tptpul, RHHtop 
Tptpmn tsw34 TMN 
Tptpll tsw35 TLL 
Tptpln tsw36 TM2 (upper 2/3 
of Tptpln) 
tsw37 TM1 (lower 1/3 
of Tptpln) 
Tptpv3 tsw38 PV3 
Tptpv2 tsw39 (vit, zeo) PV2 
Source: BSC (2003 [160109], Table 11) 
MDL-NBS-HS-000014 REV00 28 April 2003 

Analysis of Hydrologic Properties Data U0090 
Table 4. GFM2000 Lithostratigraphy, UZ Model Layer, and Hydrogeologic Unit Correlation (continued) 
Major Unit(Modified from Montazer 
and Wilson 1984 [100161]) 
GFM2000 
LithostratigraphicNomenclature 
UZ Model Layer 
(BSC 2003 [160109], 
Table 11) 
Hydrogeologic 
Unit (Flint 1998 
[100033], Table 1) 
Calico Hills nonwelded Tptpv1 ch1 (vit, zeo) BT1 or 
BT1a (altered)(CHn) Tpbt1 
Tac (Calico) ch2 (vit, zeo) CHV (vitric) 
or 
CHZ (zeolitic) 
ch3 (vit, zeo) 
ch4 (vit, zeo) 
ch5 (vit, zeo) 
Tacbt (Calicobt) ch6 (vit, zeo) BT 
Tcpuv (Prowuv) pp4 PP4 (zeolitic) 
Tcpuc (Prowuc) pp3 PP3 (devitrified) 
Tcpmd (Prowmd) pp2 PP2 (devitrified) 
Tcplc (Prowlc) 
Tcplv (Prowlv) pp1 PP1 (zeolitic) 
Tcpbt (Prowbt) 
Tcbuv (Bullfroguv) 
Crater Flat undifferentiated Tcbuc (Bullfroguc) bf3 BF3 (welded) 
(CFu) Tcbmd (Bullfrogmd) 
Tcblc (Bullfroglc) 
Tcblv (Bullfroglv) bf2 BF2 (nonwelded) 
Tcbbt (Bullfrogbt) 
Tctuv (Tramuv) 
Tctuc (Tramuc) tr3 Not Available 
Tctmd (Trammd) 
Tctlc (Tramlc) 
Tctlv (Tramlv) tr2 Not Available 
Tctbt (Trambt) and 
below 
Source: BSC (2003 [160109], Table 11) 
Table 5. Scientific Notebooks 
LBNL Scientific Notebook ID M&O Scientific Notebook ID Relevant Pages Citation 
YMP-LBNL-GSB-1.1.2 SN-LBNL-SCI-003-V1 9394, 117127, 
145-146 
Ahlers 2000 [155853] 
YMP-LBNL-YWT-SM-3 SN-LBNL-SCI-100-V3 6979 Wang 2003 [161654] 
YMP-LBNL-GSB-LHH-3 SN-LBNL-SCI-215-V1 3553, 101 Wang 2003 [161654] 
YMP-LBNL-UZ-CFA-1 SN-LBNL-SCI-003-V2 5783 Wang 2003 [161654] 
YMP-LBNL-HHL-GZ-1 SN-LBNL-SCI-227-V1 819, 2154, 70 
71, 73-84, 87 
Wang 2003 [161654] 
YMP-LBNL-YSW-3 SN-LBNL-SCI-199-V1 92-99 Wang 2003 [161654] 
YMP-LBNL-GSB-LHH-2 SN-LBNL-SCI-098-V1 6468 Liu 2001 [155675] 
MDL-NBS-HS-000014 REV00 29 April 2003 

Analysis of Hydrologic Properties Data U0090 
This report also addresses the following FEPs that were taken from the License Application (LA) 
FEP List (DTN: MO0301SEPFEPS1.000 [161496]). The LA FEP List is a revision to the 
previous project FEP list (Freeze et al. 2001 [154365]), which was used to develop the list of 
included FEPs in the TWP (BSC 2002 [160819], Table 2-6). The selected FEPs are those taken 
from the LA FEP List that are associated with the subject matter of this report, regardless of the 
anticipated status for exclusion or inclusion in TSPA-LA as represented in BSC (2002 [160819]). 
The results of this model are part of the basis for the treatment of FEPs as discussed in the Total 
System Performance Assessment-License Application Methods and Approach (BSC 2002 
[160146], Section 3.2.2). The cross-reference for each FEP to the relevant sections of this report 
is also given below. 
 YMP FEP Database Number 2.2.07.08.0A: Fracture Flow in the UZ. (This FEP is 
addressed in Sections 6.6-6.8 of this report.) 
 YMP FEP Database Number 1.2.02.01.0A: Fractures. (This FEP is addressed in 
Sections 6.1 and 6.6 of this report.) 
 YMP FEP Database Number 2.2.03.01.0A: Stratigraphy. (This FEP is addressed in 
Sections 6.1- 6.4 of this report.) 
 YMP FEP Database Number 2.2.07.02.0A: Unsaturated Groundwater Flow in 
Geosphere. (This FEP is addressed in Section 6.6 of this report.) 
 YMP FEP Database Number 2.2.07.04.0A: Focusing of unsaturated flow (fingers, 
weeps). (This FEP is addressed in Sections 6.6 and 7 of this report.) 
 YMP FEP Database Number 2.2.03.02.0A: Rock Properties of Host Rock and Other 
Units. (This FEP is addressed in Sections 6.1-6.5 of this report.) 
The following subsections present the methods used to determine fracture properties, matrix 
properties, thermal properties, and fault properties, followed by an analysis confirming specific 
fracture properties utilizing field data. A description of the AFM is also presented in Section 6.6. 
The validation of the AFM is documented in Section 7. 
6.1 FRACTURE PROPERTIES 
Fracture properties determined in this report include fracture frequency, fracture aperture, 
fracture porosity, fracture interface area, uncalibrated van Genuchten fracture a and m, and 
uncalibrated fracture permeability. The development of fracture properties is documented in a 
Scientific Notebook (Wang 2003 [161654], SN-LBNL-SCI-215-V1, pp. 3553). Excel files used 
for calculating fracture properties are listed and described in Attachment III. Excel files 
lecan97.xls, UTCA_BRFA.xls, drift.xls, and airk.xls are used for calculating fracture 
permeability values, and Fpor.xls is used for determining fracture porosity values. 
6.1.1 Fracture Permeability 
The fracture permeabilities calculated here for the UZ Model layers are based on air 
permeabilities inferred from air-injection tests performed in vertical boreholes and in ESF 
MDL-NBS-HS-000014 REV00 30 April 2003 

Analysis of Hydrologic Properties Data U0090 
alcoves. Permeabilities inferred from air-injection tests in boreholes are representative of fracture 
absolute permeabilities. These permeabilities were determined based on pneumatic-pressure 
data and are calculated using a modified version of Hvorslevs (1951 [101868], p. 30, Case 8) 
solution for steady-state elliptic flow (LeCain 1995 [101700], p. 10). The determined 
permeability values are combined here to determine effective fracture permeabilities for the UZ 
Model layers. Geometric means of these fracture permeabilities are considered to reflect 
upscaling of these permeabilities for use as single values representative for each model layer. 
Note that permeability is an intrinsic property for a test medium and theoretically independent of 
test fluids, as long as the test medium can be viewed as a continuum. Thus, fracture 
permeabilities derived from air injection tests are considered to be applicable for describing 
liquid water flow in fractures. 
MDL-NBS-HS-000014 REV00 31 April 2003 

Analysis of Hydrologic Properties Data U0090 
900 
WT-7 
N62 
N59/61* 
N57/58* 
H-3 
N64 
UZ-6 
SD-7 
WT-2 UZ-7a 
H-4SD-6 
SD-12 
H-6 
N11 
N17 
N15/16* 
G2 
WT-24 
N36 
N27 
N33 
G-1 
H-1 
WT#6 
H-5 
SD-9 
WT-1 
N31/32* 
G-4 b#1 
NRG-6 
NRG#5 
N#63 
UZ#4/5* 
WT#18 
WT#4 
N38 
N37 
NRG-7a 
NRG#4 
N53/54* 
N55 
UZ#16 
UZ1/14* 
NSP Northing (m) 
NSP Easting (m) 
LEGEND 
 UZ2002 Model Boundary 
 2002 Repository Boundary 
 ESF and ECRB 
UZ#16 
Note: * Denotes single location used for 
 adjacent boreholes. 
Source: BSC (2003 [160240], Figure 1).
Figure 1. Schematic Showing Locations of Selected Boreholes.
MDL-NBS-HS-000014 REV00 32 April 2003 

Analysis of Hydrologic Properties Data U0090 
For the Tiva Canyon welded hydrogeologic unit (TCw), fracture permeabilities were based on 
air-injection tests performed in boreholes NRG-7a, NRG-6, SD-12, and UZ#16 and in the Upper 
Tiva Canyon, Bow Ridge fault, and Upper Paintbrush Contact Alcoves (Alcoves 1, 2 and 3, 
respectively). For the Paintbrush nonwelded hydrogeologic unit (PTn), the permeability data are 
from borehole NRG-7a and in the Upper Paintbrush Contact Alcove (Alcove 3). For the 
Topopah Spring welded hydrogeologic unit (TSw), the permeability data are from boreholes 
NRG-7a, NRG-6, SD-12 and UZ#16 and from the Single Heater and Drift Scale Test areas in 
Alcove 5. For the Calico Hills nonwelded hydrogeologic unit (CHn), permeability data are 
available only from a single sampled interval in the borehole UZ#16. The locations of the 
boreholes are given in Figure 1. No air-injection data are available for the Prow Pass (pp), 
Bullfrog (bf), and Tram (tr) units. For model layers where no data are available, analogs to other 
units are used based on those designated for matrix properties (Flint 1998 [100033], p. 46), the 
degree of zeolitic alteration, and degree of welding. These fracture permeabilities are used as 
prior information and initial estimates for a separate model report describing the calibrated 
properties model. 
Table 6 lists the geometric means of the fracture permeabilities for the UZ Model layers. The 
lithostratigraphic units were assigned to the UZ Model layers as listed in Table 4. The fracture 
permeabilities were treated as isotropic, and the data from vertical boreholes and from the 
horizontal and inclined boreholes in the ESF alcoves were combined. The scales of these 
measurements are similar, as discussed in Section 6.1.1.1. 
Table 6. Uncalibrated Fracture Permeabilities for the UZ Model Layers 
UZ Model 
Layer 
Fracture 
Permeability (m2 ) 
Basisa kG 
b log(kG ) slog (kG) 
c Nd 
tcw11 BRFA 3.0E-11 -10.52 -2 
tcw12 
UTCA 
UPCA 
NRG-6 
NRG-7a 
SD-12 
UZ#16 
5.3E-12 -11.28 0.78 80 
tcw13 UPCA 
NRG-7a 4.5E-12 -11.35 1.15 3 
ptn21 UPCA 
NRG-7a 3.2E-12 -11.49 0.88 12 
ptn22 NRG-7a 3.0E-13 -12.52 0.20 4 
ptn23 NRG-7a 3.0E-13 -12.52 0.20 4 
ptn24 NRG-7a 3.0E-12 -11.52 -1 
ptn25 NRG-7a 1.7E-13 -12.78 0.10 7 
ptn26 NRG-7a 2.2E-13 -12.66 -1 
tsw31 Average TSW 8.1E-13 -12.09 --
tsw32 
NRG-6 
NRG-7a 
SD-12 
UZ#16 
7.1E-13 -12.15 0.66 31 
MDL-NBS-HS-000014 REV00 33 April 2003 

Analysis of Hydrologic Properties Data U0090 
Table 6. Uncalibrated Fracture Permeabilities for the UZ Model Layers (continued) 
UZ Model 
Layer 
Fracture 
Permeability (m2 ) 
Basisa kG 
b log(kG ) slog (kG) 
c Nd 
tsw33 
NRG-6 
NRG-7a 
SD-12 
UZ#16 
7.8E-13 -12.11 0.61 27 
tsw34 
SHT 
DST 
NRG-6 
NRG-7a 
SD-12 
UZ#16 
3.3E-13 -12.48 0.47 180 
alternate tsw34 
SHT 
DST 
NRG-6 
NRG-7a 
SD-12 
UZ#16 
1.5E-13 -12.81 0.75 180 
tsw35 NRG-7a 
UZ#16 9.1E-13 -12.04 0.54 31 
tsw3[67] SD-12 
UZ#16 1.3E-12 -11.87 0.28 19 
tsw38 Average 
TSw 8.1E-13 -12.09 --
tsw39 Average 
TSw 8.1E-13 -12.09 --
ch1Ze ch2Ze 2.5E-14 -13.60 --
ch1VI ptn26 2.2E-13 -12.66 --
ch[23456]VI ptn26 2.2E-13 -12.66 --
ch[2345]Ze UZ#16 2.5E-14 -13.60 -1 
ch6 ch2Ze 2.5E-14 -13.60 --
pp4 ch2Ze 2.5E-14 -13.60 --
pp3 ptn26 2.2E-13 -12.66 --
pp2 ptn26 2.2E-13 -12.66 --
pp1 ch2Ze 2.5E-14 -13.60 --
bf3 ptn26 2.2E-13 -12.66 --
bf2 ch2Ze 2.5E-14 -13.60 --
tr3 ptn26 2.2E-13 -12.66 --
tr2 ch2Ze 2.5E-14 -13.60 --
Output-DTN: LB0205REVUZPRP.001 [159525] 
DTNs: GS960908312232.013 [105574]; GS970183122410.001 [105580]; 
LB970600123142.001 [105589]; LB980120123142.004 [105590]; 
LB980120123142.005 [114134]; LB960500834244.001 [105587] 
a
NOTE: Identifies the corresponding air-injection borehole(s) and/or alcove(s) 
or analog to another model layer(s). UTCA-Upper Tiva Canyon Alcove, 
BRFA-Bow Ridge fault Alcove, UPCA-Upper Paintbrush Contact Alcove, 
SHT- Single Heater Test Area, DST-Drift Scale Test Area, and NRG-6, 
NRG-7a, SD-12, and UZ#16 are vertical boreholes. (This column is 
presented for information only and is not from the cited DTN.) 
b Geometric mean
c Standard deviation
d Number of sampled intervals
- means that no data are available
MDL-NBS-HS-000014 REV00 34 April 2003 

Analysis of Hydrologic Properties Data U0090 
2
The mean fracture permeabilities range from 2.5 x 1014 m2 to 3.0 x 1011 m. The Tiva Canyon 
welded unit (TCw) has the highest fracture permeabilities. Topopah Spring welded unit (TSw) 
fracture permeabilities are, in general, higher than those for the nonwelded Paintbrush (PTn) and 
Calico Hills (CHn) units. Two fracture permeabilities are shown for the Topopah Spring middle 
nonlithophysal unit (tsw34). These represent two different approaches for weighting the 
available air-injection data. For tsw34, there were 143 sampled intervals in the Alcove 5 heater 
test areas compared to 37 sampled intervals in the four vertical borehole injection tests. For the 
first case, the data from Alcove 5 were weighted with those from the vertical borehole tests (k = 
0.8 kG,vertical boreholes + 0.2 kG, Alcove 5). In the alternate tsw34 case, each sampled interval is 
weighted equally. 
The uncertainty and variability of fracture permeabilities for the UZ Model layers are reflected 
by the standard deviations reported in Table 6. These standard deviations would result in 95% 
confidence intervals covering three orders of magnitude, even for the units that had a large 
number of sampled intervals. The data indicate that fracture permeabilities are highly variable. 
6.1.1.1 Scaling Issues 
As noted previously, the permeabilities measured in the vertical boreholes and alcoves were 
combined to determine the fracture permeability for each UZ Model layer. The packer lengths 
were approximately 4 m for vertical boreholes, 1 to 3 m for Alcoves 1, 2, and 3, and 5 to 12 m in 
the SHT and DST areas (Alcove 5). These data were all considered to be on the same relative 
scale and representative of the fracture permeability on the scale of the UZ Model after 
upscaling, using geometric means. Additional air-permeability data on a scale of one-foot 
intervals are also available from air-injection testing in niches in the ESF in the Topopah Spring 
middle nonlithophysal unit (tsw34). The air-injection data from the niche studies are not used 
here for determining mean fracture permeabilities for the model layers, since these data are on a 
smaller scale and may not be representative of larger-scale effective permeability. Figure 2 
compares the geometric means and range of data (mean  standard deviation) for the model layer 
tsw34 corresponding to Tptpmn (Table 4) (Wang 2003 [161654], SN-LBNL-SCI-215-V1, 
p. 101). The data shown for the niche studies are inferred from pre-excavation air-injection 
testing. The ranges of the data overlap, but the geometric means for the measurements from the 
niche studies are generally lower than the other values. This would be expected because the 
mean permeability decreases as the scale of the measurement decreases (Neuman 1994 
[105731]). 
MDL-NBS-HS-000014 REV00 35 April 2003 

Analysis of Hydrologic Properties Data U0090 
-11.00 
log(K) 
-11.50
-12.00
-12.50
-13.00
-13.50
-14.00
-14.50 
NRG 6 NRG 7a SD-12 UZ#16 SHT & DST Niche 3566 Niche 3650 Niche 3107 Niche 4788 
DTNs: GS960908312232.013 [105574], LB960500834244.001 [105587], LB970600123142.001 [105589], 
LB980120123142.004 [105590], LB980120123142.005 [114134], LB990901233124.004 [123273] 
NOTE: Permeabilities were inferred from air-injection data. X indicates geometric mean, and range is  one 
standard deviation. Details of calculations are shown in Wang (2003 [161654], SN-LBNL-SCI-215-V1, p. 
101) and in spreadsheet drift.xls, in Attachment III, p. 19. 
Figure 2. Fracture Permeabilities for Topopah Spring Middle Nonlithophysal Unit. 
MDL-NBS-HS-000014 REV00 36 April 2003 

Analysis of Hydrologic Properties Data U0090 
6.1.2 Fracture Frequency, Intensity, Fracture Interface Area, Aperture, and van 
Genuchten Parameters 
Fracture frequency, interface area, and the van Genuchten m are determined from qualified 
fracture property data (DTN: LB990501233129.001 [106787]) developed from field data. These 
include Detailed Line Survey (DLS) fracture data (collected from the ESF North and South 
Ramps, Main Drift, and ECRB Cross Drift, providing spatially varying frequency, length, and 
fracture dips and strikes) and fracture frequency data from boreholes. For completeness, 
mathematical equations used for developing these properties in DTN: LB990501233129.001 
[106787] are also described here. 
For calculating fracture frequencies using the DLS in the ESF and ECRB Cross Drift, the mean 
fracture frequency is given by the inverse of the mean spacing. The mean spacing is calculated 
by: 
nf 
s = 1 .(Di - Di-1 ) (Eq. 1)
nf - 12 
where Di is the distance or station along the ESF where fracture i intersects the DLS and nf is the 
number of fractures. This is the apparent spacing. It is not the normal distance between the 
fracture planes and is therefore a rough estimate of the true spacing. These values were not 
corrected for any possible bias in orientation in the DLS. The mean fracture frequency is given 
by the inverse of the mean apparent spacing: 
1
f = (Eq. 2)
s 
For calculating fracture frequency from borehole data, the data are processed to normalize for 
core recovery, corrected for bias in orientation, and scaled to represent larger length fracture. To 
correct for orientation bias for data from vertical boreholes, dip distributions are used as follows 
(modified from Lin et al. 1993 [116797], p. 24 [Eq. 3-1]): 
. fi19 0 , dip . fi39 20 , dip . fi59 40 , dip . fi90 60 , dip
---- 
f
cb = i + i + i + i (Eq. 3)
10cos( ) 30 cos( ) 50cos( ) 75cos( ) 
where fcb is the fracture frequency corrected for orientation bias and fi is the fracture frequency 
corresponding to the range of dip distribution. Finally, these values are corrected to represent 
larger length fractures on the scale of those characterized in the ESF. A simple correction ratio is 
used based on comparisons of ESF data with corresponding vertical boreholes for that model 
layer: 
ff =
corrected = fcbR 
. (Eq. 4)
R = ..
. fESF .. 
. fborehole .average 
MDL-NBS-HS-000014 REV00 37 April 2003 

Analysis of Hydrologic Properties Data U0090 
Two correction factors R were calculated, one for welded units using data for the Topopah 
Springs middle nonlithophysal hydrogeologic unit (tsw34) and one for nonwelded units using 
data for the Pah Canyon Tuff in the Paintbrush hydrogeologic unit (ptn25). These units were 
selected because both ESF and borehole data are available; these were considered to be 
representative of the other units. 
The fracture intensity is calculated by dividing the trace length of the fracture by the area 
surveyed. The area surveyed was 6 m (3 m above and below the traceline) times the length 
along the tunnel considered for that interval. The average fracture intensity I (m/m2) is given by: 
nf nf 
.ti .ti 
i=1 i=1
I == 
area ( m6 )( metersinlengthinterval ) (Eq. 5) 
where ti is trace length in meters for fracture i. 
The fracture interface area is calculated by dividing the fracture area by the volume of the 
interval surveyed. The volume for the interval is estimated by multiplying the interval length 
surveyed by the square of the geometric mean of surveyed fracture trace length. The average 
fracture interface area per volume Afm (m2/m3) is given by: 
nf nf
22 
=
)(
.pri .prii=1 = i=1 
2
A
fm volume ( lengthstraceofmeangeometriclengthinterval )(Eq. 6) 
where r is the radius of fracture i, or one-half the trace length of fracture i. 
Fracture apertures are calculated by the cubic law with the fractures fully connected. The 
fracture aperture b is then given by (Bear et al. 1993 [116773], p. 15): 
1
3
. 12k .
b =
. f .
.(Eq. 7)
.
where k is the fracture permeability. The fracture aperture determined in this way is an effective 
hydraulic aperture, not a physical aperture. Note that the above equation is modified from 
Equation 1.2.28 of Bear et al. (1993 [116773], p. 15). The k here refers to bulk fracture 
permeability rather than permeability in a fracture as defined by Bear et al. (1993 [116773], p. 
15). 
Fitted parameters are required to utilize the van Genuchten equation relating the effective 
saturation Se and capillary pressure Pc (derived from Equations 2, 22, and 24 of van Genuchten 
1980 [100610], pp. 892895): 
1
1 
-1 m -1) n
P =(S 
(Eq. 8a)
ce
a 
MDL-NBS-HS-000014 REV00 38 April 2003 

Analysis of Hydrologic Properties Data U0090 
where a, m, and n = 1 are the van Genuchten parameters. The effective saturation is defined 
1 - m 
by 
S - S
r
S = (Eq. 8b)
e 
S - S
s r 
where S is total water saturation, Ss is satiated saturation, and Sr is residual saturation. 
A simplified form of the Young-Laplace equation is assumed to directly calculate the van 
Genuchten fracture a(af) from b. Note that the subscript f refers to fractures. The resulting 
relationship is: 
b
af = (Eq. 9)
2ts cos. 
where t
s is the surface tension of pure water at 20C (0.072 N/m) and . is the contact angle. 
Essentially, Equation 9 states that van Genuchten a can be estimated as the inverse of the air-
entry value, which is often used in the soil science literature (Wang and Narasimhan 1993 
[106793], p. 374). The contact angle . is set to be zero (Wang and Narasimhan 1993 [106793], 
p. 329), since the rock is expected to be water wetting, and no other specific data are available. 
Fracture aperture and fracture van Genuchten alpha (af) are calculated from fracture frequency 
and fracture permeability estimates using Equations 7 and 9. The estimated mean apertures are 
approximately 100 to 400 m except for model layer tcw11, which had a relatively high fracture 
permeability, resulting in a higher estimated fracture aperture. The fracture van Genuchten alpha 
parameters (af) are on the order of 10-3 Pa-1. There are large uncertainties in these values for the 
Calico Hills formation and lower units, because little or no fracture permeability and fracture 
frequency data are available. 
The van Genuchten fracture m parameter (mf) is determined by fitting Equation 8a to the 
aperture-size distribution calculated from Equation 7. A mf value of 0.633, determined based on 
the above method, is given in DTN: LB990501233129.001 [106787]. This value is used in this 
study. Note that an alternative method to estimate mf is not available in the literature. 
The developed fracture properties are given in Table 7. 
MDL-NBS-HS-000014 REV00 39 April 2003 

Analysis of Hydrologic Properties Data U0090 
3 60 
12 76 
4 --
4 -63 
-1 18 
7 0.6 72 
-1 114 
--140 
31 842 
27 
tcw13 4.5E-12 -11.35 1.15 2.79 1.43 1.9E-3 -2.73 0.633 1.3E-2 
ptn21 3.2E-12 -11.49 0.88 0.67 0.92 2.7E-3 -2.57 0.633 9.2E-3 
ptn22 3.0E-13 -12.52 0.20 0.46 1.4E-3 -2.86 0.633 1.0E-2 
ptn23 3.0E-13 -12.52 0.20 0.57 1.2E-3 -2.91 0.633 2.1E-3 
ptn24 3.0E-12 -11.52 0.46 0.45 3.0E-3 -2.53 0.633 1.0E-2 
ptn25 1.7E-13 -12.78 0.10 0.52 1.1E-3 -2.96 0.633 5.5E-3 
ptn26 2.2E-13 -12.66 0.97 0.84 9.6E-4 -3.02 0.633 3.1E-3 
tsw31 8.1E-13 -12.09 2.17 2.37 1.1E-3 -2.96 0.633 5.0E-3 
tsw32 7.1E-13 -12.15 0.66 1.12 1.09 1.4E-3 -2.86 0.633 8.3E-3 
tsw33 7.8E-13 -12.11 0.61 0.81 1.03 1329 1.6E-3 -2.80 0.633 5.8E-3 
Table 7. Fracture Properties for UZ Model Layers 
UZ Permeability (m2) Frequency (m-1) Van Genuchten 
parameter 
Model Layer kG log(kG) slog(kG) N f sfN a (Pa-1) log(a) m (-) porosity (-) Std (-) Afm 
tcw11 
3.0E-11 -10.52 -2 
0.92 0.94 76 
5.0E-3 -2.30 0.633 
2.4E-2 -1.56 
tcw12 5.3E-12 -11.28 0.78 80 1.91 2.09 1241 2.2E-3 -2.66 0.633 1.7E-2 -13.39 
-3.77 
-1 
-1.41 
-1.75 
-0.34 
-1.09 
-3.56 
-3.86 
-3.21 
-4.44 
tsw34 3.3E-13 -12.48 0.47 180 4.32 3.42 10646 6.7E-4 -3.18 0.633 8.5E-3 2.50E-03 13.54 
alternate 1.5E-13 -12.81 0.75 180 
tsw34 
-9.68 31 -595 
---37 
---3 
---25 
ch6 ----
pp3 ----
pp1 ----
bf2 ----
tr2 ----
tsw35 9.1E-13 -12.04 0.54 3.16 1.0E-3 -2.99 0.633 9.6E-3tsw38 8.1E-13 -12.09 4.36 8.9E-4 -3.05 0.633 1.1E-2ch1Ze 2.5E-14 -13.60 0.04 1.4E-3 -2.86 0.633 1.6E-42.2E-13 -12.66 0.14 1.9E-3 -2.73 0.633 7.7E-42.5E-14 -13.60 0.04 1.4E-3 -2.86 0.633 1.6E-42.2E-13 -12.66 0.20 1.6E-3 -2.78 0.633 9.7E-42.5E-14 -13.60 0.14 8.9E-4 -3.05 0.633 3.7E-42.5E-14 -13.60 0.14 8.9E-4 -3.05 0.633 3.7E-42.5E-14 -13.60 0.14 8.9E-4 -3.05 0.633 3.7E-4tsw3[67] 1.3E-12 -11.87 0.28 19 4.02 -526 1.1E-3 -2.96 0.633 1.3E-2 -12.31 
-13.34 
tsw39 8.1E-13 -12.09 --0.96 -46 1.5E-3 -2.82 0.633 4.3E-3 -2.95 
-0.11 
ch1VI 2.2E-13 -12.66 --0.10 -11 2.1E-3 -2.69 0.633 6.1E-4 -0.3 
ch[23456]VI 
-0.43 
ch[2345]Ze 2.5E-14 -13.60 -1 0.14 -25 8.9E-4 -3.05 0.633 3.7E-4 -0.43 
-0.11 
pp4 2.5E-14 -13.60 --0.14 --8.9E-4 -3.05 0.633 3.7E-4 -0.43 
-0.61 
pp2 2.2E-13 -12.66 --0.20 --1.6E-3 -2.78 0.633 9.7E-4 -0.61 
-0.43 
bf3 2.2E-13 -12.66 --0.20 --1.6E-3 -2.78 0.633 9.7E-4 -0.61 
-0.43 
tr3 2.2E-13 -12.66 --0.20 --1.6E-3 -2.78 0.633 9.7E-4 -0.61 
-0.43 
Output  DTN: LB0205REVUZPRP.001 
NOTE: k is permeability (geometric mean). 
s is standard deviation. 
N is number of samples. 
f is fracture frequency. 
a and m are fitting parameters for the van Genuchten water potential relationship. 
Std refers to standard deviation for fracture porosity. 
Afm refers to fracture matrix interface area (m2/m3). 
- means that no data are available 
6.1.3 Fracture Porosity 
6.1.3.1 General Strategy 
Fracture porosity is herein defined as the effective porosity of fractures in which fluid flow and 
solute transport take place. In this study, a combination of porosity data derived from gas tracer 
tests in the ESF, and porosity estimates, based on the geometry of fracture networks, are used to 
develop representative fracture porosities for the UZ Model layers. The calculation of the 
MDL-NBS-HS-000014 REV00 40 April 2003 

Analysis of Hydrologic Properties Data U0090 
fracture porosity is documented in a Scientific Notebook (Wang 2003 [161654], SN-LBNL-SCI-
215-V1, pp. 4353). 
Gas tracer tests were performed in the ESF to obtain estimates of the effective fracture porosity 
for the Topopah Spring middle nonlithophysal welded tuff, corresponding to the tsw34 model 
layer (DTN: LB980912332245.002 [105593]). Since gas tracer travel times through the fractured 
rocks are directly related to the storage of the corresponding fracture networks, analyses of tracer 
breakthrough data can provide reliable estimates of fracture porosity for the model layer tsw34. 
Gas tracer test data are available only for model layer tsw34. Alternative approaches are 
available to estimate fracture porosity based on the geometry of fractures observed in the ESF. 
These geometric representations of porosity are used to apply the tsw34 value to the other units. 
The 1-D and 2-D porosities to be discussed below refer to porosities determined from 1-D 
borehole and 2-D mapping data, respectively. A so-called 2-D porosity for a model layer can be 
estimated using the aperture and the total fracture length per unit area (fracture intensity). The 
fracture intensity is based on tracer lengths given by the DLS in the ESF and the area enclosing 
the traces (see Equation 5). The equation used to calculate the 2-D porosity is: 
f
2-D = bI (Eq. 10) 
where I is the fracture intensity (m/m2) (DTN: LB990501233129.001 [106787]). When no 
intensity data are available (in cases where the unit does not intersect any portion of the ESF or 
ECRB Cross Drift) (BSC 2001 [159725], Section 6.1), the so-called 1-D porosity can be 
estimated by treating all fractures as continuous. The 1-D porosity is then calculated by: 
f
1-D = fb (Eq. 11) 
Note that a large degree of uncertainty exists in the estimates based on Equations 10 and 11 for 
the following reasons. First, the estimated apertures are hydraulic apertures and may be very 
different from the average geometric apertures, since they are estimated based on air-
permeability data. Second, Equations 10 and 11 only consider 2-D or 1-D geometric features, 
while actual fracture networks are three-dimensional. Therefore, direct estimates from these 
equations may not be reliable. However, it is reasonable to consider that these estimates would 
provide more reliable relative ratios of the fracture porosity for different stratigraphic units. 
Based on these considerations, a fracture porosity is determined by using the corresponding 
estimate from these equations to determine a ratio of fracture porosity between units. Because 
the porosity, based on analyses of the gas tracer tests, is available for the tsw34, this value was 
used with these ratios to estimate fracture porosity for the other units:
f
f
f
x layer model D, -2 (Eq. 12)x layer model =ftsw34 f tsw34 D, -2 
or fx layer model=ftsw34 
fx layer model D, -1 
tsw34 D, -1 
where ftsw34 is fracture porosity for tsw34, estimated from the gas tracer data, and f2-D and f1-D 
refer to values calculated using Equations 10 and 11, respectively. The developed fracture 
porosity values for the UZ Model layers are given in Table 7. All of these values are on the order 
of 1%. An alternative approach would have been to use 1% for all units. Use of this scaling 
MDL-NBS-HS-000014 REV00 41 April 2003 

Analysis of Hydrologic Properties Data U0090 
scheme for estimating fracture porosities is an approximation for determining the spatial 
variability of porosity among the model layers. 
Note that the overall strategy is essentially a combination of the two general approaches 
available for estimating fracture porosities in the literature. The first approach is based on field 
tracer transport data. Researchers outside the Yucca Mountain Project have also used similar 
approaches. For example, inverse modeling was used to analyze a radially convergent flow tracer 
test in a fractured chalk formation, resulting in a calibrated fracture porosity of 0.3% (National 
Research Council 1996 [139151], pp. 292293). The second general approach is based on the 
geometry of a fracture network. This approach considers all the fractures under consideration as 
connected and requires that fracture apertures can be exactly determined. Although a large 
degree of uncertainty exists in fracture porosity values estimated from this approach (for several 
reasons), this approach has often been used when field tracer test data are not available. For 
example, in their review of numerical approaches for modeling multiphase flow in fractured 
petroleum reservoirs, Kazemi and Gilman (1993 [147209], pp. 270271, 312313) discuss the 
determination of fracture porosity, based on fracture geometry data. Considering that gas tracer 
test data are only available for one model layer (tsw34) and a large degree of uncertainty exists 
when the second approach is used, use of both approaches provides significantly better estimates 
for fracture porosity in units through the UZ. A combination of the above two approaches makes 
the best use of the relevant data. 
6.1.3.2 Fracture Porosity from Gas Tracer Testing Data 
The estimated fracture porosities (DTN: LB980912332245.002 [105593]) were developed based 
on several simplifications (Figure 3): Flow and transport are two-dimensional; dispersion, gas 
compressibility and matrix diffusion are ignored; and the testing medium is homogeneous. The 
estimations were made using: 
ff * = Qt5.0 (Eq. 13)
prL 
2 L 
where ff* is the estimated fracture porosity, Q is the volumetric withdrawal rate (Qwithdrawal in 
Figure 3), t0.5 is the mean travel time of tracer, rL is the distance between the tracer injection and 
withdrawal zones, and L is the length of injection zone. 
MDL-NBS-HS-000014 REV00 42 April 2003 

Analysis of Hydrologic Properties Data U0090 

. D 
= . M A 
. 


Figure 3. A Conceptual Model for Estimating Fracture Porosity Using Gas Tracer Testing Data 
The average fracture porosity, estimated from Equation 13 using gas tracer data collected from 
the DST block and Niche 3107, is 1.02E-2 (DTN: LB980912332245.002 [105593]). 
6.1.3.3 Effects of Several Factors on Fracture-Porosity Estimation Based on Gas Tracer 
Testing Data 
The estimation of fracture porosity based on Equation 13 does not consider the effects of several 
factors: gas compressibility, heterogeneity, anisotropy, cavities, dispersion and matrix diffusion. 
Potential effects of these factors on estimating fracture porosity are discussed below. 
Gas compressibility does not have a significant effect on the porosity estimation, because gas-
pressure disturbances introduced by the gas tracer tests are small (relative pressure changes are 
on the order of several percent) as inferred from Appendix A of Freifeld (2001 [161806]). 
Heterogeneity was ignored in estimating fracture porosity, based on Equation 13. It is reasonable 
to estimate an effective porosity by considering homogeneity within the measurement scale (on 
the order of several meters) considering that the scale is much smaller than a UZ-model-layer 
scale. Heterogeneity above the measurement scale is captured by standard deviation of the 
estimates. It is physically unjustified to estimate porosity variation below measurement (support) 
scales. (At a scale much smaller than the measurement scale, fracture porosity cannot be 
defined.) However, it should be noted that the heterogeneity below the measurement scale may 
have considerable effects on the fracture porosity estimates. If the tracer is injected into a fast 
path, the resulting analysis underestimates the fracture porosity. Otherwise, the analysis may 
overestimate the porosity. Nevertheless, those effects are reflected in the variability of the 
estimated fracture porosities. 
MDL-NBS-HS-000014 REV00 43 April 2003 

Analysis of Hydrologic Properties Data U0090 
Effects of anisotropy are essentially captured in porosity estimation because different 
configurations (orientations) were used in gas tracer tests performed in the Drift Scale Test block 
and Niche 3107. 
Not enough data exist to evaluate effects of cavities on porosity estimation in detail. However, 
appropriateness of the estimates can be partially demonstrated by comparing them with those 
from the other sites without cavities, which will be discussed later on. A discussion of cavity 
porosities and their estimates for geological units corresponding to the proposed repository 
horizon is given in BSC (2002 [160319], p. 86, Table 7-10). 
While the dispersion process is not expected to significantly affect the average trace travel time 
used to calculate fracture porosity (Equation 13), Moench (1989 [101146], Figure 2) implies that 
considering dispersion may result in larger porosity estimates than those determined from 
Equation 13 (Wang 2003 [161654], SN-LBNL-SCI-215-V1, p. 53). Therefore, ignoring 
dispersion may partially compensate for the effects of cavities. Also, note that dispersion is a 
result of spatial variability of permeability within testing rocks. 
The determination of fracture porosity depends on the tracer travel times. Diffusion of trace into 
the matrix delays the breakthrough and causes overestimation of fracture porosity. Effects of 
matrix diffusion on the fracture porosity estimation can be quantified by an analytical solution. 
Based on mass balance, radial tracer transport in a system (with matrix diffusion) like that shown 
in Figure 3 can be described by the following differential equation (Wang 2003 [161654], SN-
LBNL-SCI-215-V1, pp. 4445): 
.c + 
q fw .c AfmfmSmgDm .cm
= 
(Eq. 14)
.t ff .rs ff .x 
x =0 
with 
qQ (Eq. 15)
fw = 
2prwL 
= 1 rL 
2 - r 2 
(Eq. 16)
rs 2 rw 
and 
Dm = D0t(Eq. 17) 
where c is the tracer concentration in fractures at a location with a distance r from the withdrawal 
borehole, and at time t, rw is the radius of withdrawal borehole, ff 
is fracture porosity 
(considering matrix diffusion), Afm is the fracture-matrix interface area per unit volume of bulk 
rock, fm is the matrix porosity, Smg is the gas saturation in the matrix, D0 is the molecular-
MDL-NBS-HS-000014 REV00 44 April 2003 

Analysis of Hydrologic Properties Data U0090 
diffusion coefficient for gas in air, t is the tortuosity factor, cm is the tracer concentration in the 
matrix, and x is the distance from the fracture-matrix interface. 
Equation 14 is similar enough in form to Equation 4 of Starr et al. (1985 [101479]) that the 
solution to Equation 14 can be obtained from it. Under conditions of continuous injection with 
concentration c0, Starr et al. (1985 [101479]) derived the solution to their Equation 4 as follows: 
c
= 0 T0 <0 (Eq. 18)
c0 
0
c. A . 
= erfc.. 0 .. T0 > 0 (Eq. 19)
c0 . T . 
where A0 and T0 are functions of the relevant transport parameters (Starr et al. 1985 [101479], 
p. 1044). The corresponding relations between transport parameters here and those of Starr et al. 
(1985 [101479]) can be easily obtained by comparing Equation 14 with their Equation 4. Based 
2
on these relations and using rsw= (rL -rw 
2)/rw . 1/2rL 
2/rw for rw << rL, expressions for A0 and T0 
at the withdrawal borehole (r = rw) can be obtained from the corresponding expressions of Starr 
et al. (1985 [101479], p. 1044) (Wang 2003 [161654], SN-LBNL-SCI-215-V1, pp. 4647): 
1/ 2(pLrL 
2)
A0 = 
Afmf mSmgDm (Eq. 20)
2Q 
f
T 0 = (t -
f rL 
2pL 
)1/ 2 (Eq. 21)
Q 
Note that under continuous tracer injection conditions, average tracer travel time t0.5 corresponds 
to 50% of the relative tracer concentration: 
A0
( ..
t c 5.0 ) = erfc.
. T 0(t ) .
. 
= 5.0 (Eq. 22)
c0 
5.0 
Combining Equations 13 with 20-22 yields 
f f = 1 - t5.0 Dm .
. AfmfmSmg . 2 
(Eq. 23)
f f * f f * . 2 ..
where  is a constant (0.48) defined by erfc() = 0.5 and determined from Domenico and 
Schwartz (1990 [100569], p. 637). The ff 
and ff* are identical if the matrix diffusion does not 
exist (Dm = 0), as shown in Equation 23. This equation can be used to correct the porosity 
MDL-NBS-HS-000014 REV00 45 April 2003 

Analysis of Hydrologic Properties Data U0090 
estimates from Equation 13 to consider the effects of matrix diffusion. Values for Afm and f m (for 
tsw34) are available from DTN: LB990501233129.001 [106787]. The travel time t0.5 is 
calculated using Equation 13 from DTN: LB980912332245.002 [105593]. The average t0.5 is 
210 min and average f f* is 1.02E-2 (Wang 2003 [161654], SN-LBNL-SCI-215-V1, pp. 4849). 
Smg (0.1) is calculated from the average matrix water saturation within tsw34 (DTN: 
MO0109HYMXPROP.001 [155989]). The value for Dm is calculated from Equation 17. D0 
(2.65E-5 m2/s) is determined from Pruess (1987 [100684], pp. 56): 
D 0 
. T + 15.273 .. 
va
D0 =. . (Eq. 24)
P . 15.273 . 
 
with Dva0 = 2.3E-5 m2/s and . =1.80 for a temperature of 20 C and an air pressure of 1 bar. The 
tortuosity for the gas tracer is estimated from the well-known relation of Millington and Quirk 
(1961 [139143]): 
.
7/3 
g
= t 
f 2 (Eq. 25) 
m 
where . g = f mSmg is the volumetric gas content in the matrix. Substituting the determined 
parameter values (including the average t0.5 and f f* values) into the right hand of Equation 23 
yields: 
f f 
= 83.0 (Eq. 26)
*
f f 
This factor is used to consider the effects of matrix diffusion (on average) by multiplying the 
porosity estimates in DTN: LB980912332245.002 [105593] by the factor value of 0.83. The 
resultant average fracture porosity for tsw34 is 0.0085, and the corresponding standard deviation 
is 2.5E-3. This porosity value is used in Equation 12 for determining fracture porosities in other 
UZ Model layers. The final fracture-porosity estimates are given in Table 7. Note that 
uncertainty exists in the estimated porosity value (Equation 26). However, it is difficult to 
quantify this uncertainty for the given data because site-specific parameter values calculating this 
ratio are not available for a tracer test. Nevertheless, as shown in Section 6.1.3.4, the fracture 
porosity values estimated using Equation 26 are reasonable compared with values determined 
from other sources in the UZ and from other sites. 
6.1.3.4 Comparisons with Fracture Porosities Estimated from Other Sources 
To demonstrate the reasonableness of the fracture porosity estimates (given in Table 7), these 
estimates were compared with those determined from other sources in the UZ and from other 
sites. 
 Fracture porosities were also estimated from water content data calculated from water 
travel times observed from water release tests in the Tptpmn (tsw34) of the UZ (DTNs: 
LB980901233124.003 [105592]; LB0110LIQR0015.001 [156907]) (See Section 6.5 of 
MDL-NBS-HS-000014 REV00 46 April 2003 

Analysis of Hydrologic Properties Data U0090 
this report). The mean value of the estimates is ff = 0.018 and the standard deviation is 
0.014. 
 CRWMS M&O (2001 [153045], Table 9) reports fracture porosity estimates obtained by 
inversion of the seepage data collected in the Tptpmn unit. The mean and standard 
deviation are 0.011 and 0.008, respectively. 
 Fracture porosity was estimated from gas pressure data collected from Apache Leap 
Research Site (Neuman et al. 2001 [160849], p. 320). The estimated mean and standard 
deviation are 0.014 and 0.0017, respectively. 
 Fracture porosity for a fractured chalk formation, estimated using inversion modeling of 
a tracer test, was 0.003 (National Research Council 1996 [139151], p. 293). 
 Fracture porosities estimated from gas tracer tests in the northern Ghost Dance Fault in 
the UZ range were from 0.001 to 0.07 (LeCain et al. 2000 [144612], Table 18). 
 Preliminary estimates based on water travel times observed from water release tests in 
Tptpll (tsw35) were 0.013 and 0.067. The small porosity value is believed to mainly 
result from the fracture network rather than cavities (BSC 2001 [158463], Section 
6.11.3.2, p. 246). This value is close to the current estimate for Tptpll (0.0096). 
 The calibrated fracture porosity, based on the Alcove 1 infiltration test data, was about 
0.028 (BSC 2001 [158726], Section 6.8.1.2, Table 6-33). The test site is located in the 
upper portion of the tcw unit. The calibrated value was close to the current estimate for 
tcw11 (0.024) (Table 7). 
These fracture porosity values (obtained using different methodologies, based on different types 
of data and/or from different sites) are consistent with the current estimates (given in Table 7) 
that are on the order of 1%, indicating the reasonableness of these estimates. 
6.2 MATRIX PROPERTIES 
Matrix properties include matrix permeability and van Genuchten (1980 [100610]) parameters 
used to describe water retention and relative permeability relations. They were determined from 
laboratory measurements made on core samples from the UZ. Some boreholes from which core 
samples came are shallow, variously penetrating the TCw, PTn, and top portions of the TSw. 
There are some deep boreholes from which core samples have been collected and analyzed for 
the entire depth: NRG-6, NRG-7a, SD-7, SD-9, SD-12, UZ-7a, UZ-14, and UZ#16. Six of these 
penetrate into the Calico Hills Formation, five penetrate into the Prow Pass Tuff, and one, SD-7, 
penetrates the Bullfrog and Tram Tuffs. Core samples have also been collected from portions of 
two other deep boreholes: SD-6 and WT#24. All the associated DTNs and their use can be found 
in the description of the relevant Excel files in Attachment III of this report. 
Sample collection and laboratory measurement methodologies, as well as estimates of core 
uncertainty, are described by Flint (1998 [100033], pp. 1119) and Rousseau et al. (1999 
[102097], pp. 125153). Core samples are grouped and analyzed according to the hydrogeologic 
units characterized by Flint (1998 [100033], pp. 1946) and detailed in a Scientific Notebook 
MDL-NBS-HS-000014 REV00 47 April 2003 

Analysis of Hydrologic Properties Data U0090 
(Wang 2003 [161654], SN-LBNL-SCI-003-V2, pp. 5783). Table 4 shows these hydrogeologic 
units in relation to the lithostratigraphy of GFM2000 and the UZ Model layers. 
The calculation of matrix properties is described in a Scientific Notebook (Wang 2003 [161654], 
SN-LBNL-SCI-003-V2, pp. 5783). Calculated matrix properties are given in Table 8. The 
Excel files used to perform these calculations are listed and described in Attachment III. The 
matrix porosity and permeability values are calculated with hydroprops_fin.xls (Attachment III). 
The unsaturated hydraulic properties are calculated with MRC_Q_TCw_fin.xls, 
MRC_Q_PTn_fin.xls, MRC_Q_TSw_fin.xls, MRC_Q_CHCF_fin.xls, vG_Summary_fin.xls, 
and PV2 deep borehole data.xls (Attachment III). 
MDL-NBS-HS-000014 REV00 48 April 2003 

Analysis of Hydrologic Properties Data U0090 
Table 8. Matrix Properties Developed from Core Data 
upscaled upscaled 
f s n SE k log(k) slog(k) nnd SElog(k) 1/a log(1/a) SElog(1/a) m SE Sr . SE 
HGU [m2] [log(m2)] [Pa] [log(Pa)] 
CCR & CUC 0.241 0.073 124 0.007 4.7E-15 -14.33 0.47 3 0 0.27 8.27E+4 4.918 0.279 0.388 0.085 0.02 3.47 17.88 
CUL & CW 0.088 0.032 694 0.001 6.4E-20 -19.20 2.74 15 25 0.43 5.46E+5 5.737 0.178 0.280 0.045 0.20 12.29 19.35 
CMW 0.200 0.055 96 0.006 1.8E-16 -15.74 2.38 5 1 0.97 2.50E+5 5.398 0.188 0.259 0.042 0.31 6.08 0.00 
CNW 0.387 0.069 104 0.007 4.0E-14 -13.40 2.05 10 0 0.65 2.03E+4 4.308 0.199 0.245 0.032 0.24 -2.58 0.33 
BT4 0.428 0.100 58 0.013 4.1E-13 -12.39 1.41 11 0 0.43 4.55E+3 3.658 0.174 0.219 0.019 0.13 -0.26 1.17 
TPY 0.233 0.057 39 0.009 1.3E-15 -14.90 0.64 2 0 0.46 7.63E+4 4.883 0.379 0.247 0.064 0.07 3.46 16.73 
BT3 0.413 0.082 73 0.010 1.3E-13 -12.87 1.09 11 1 0.31 8.90E+3 3.950 0.088 0.182 0.008 0.14 -0.56 0.49 
TPP 0.498 0.041 159 0.003 1.1E-13 -12.96 0.39 11 0 0.12 2.12E+4 4.325 0.104 0.300 0.023 0.06 0.26 0.42 
BT2 0.490 0.095 176 0.007 6.7E-13 -12.17 1.12 21 0 0.24 1.74E+4 4.239 0.170 0.126 0.013 0.05 -2.64 0.67 
TC 0.054 0.036 75 0.004 4.4E-17 -16.36 3.02 6 5 0.91 2.71E+5 5.432 0.310 0.218 0.054 0.21 6.14 17.21 
TR 0.157 0.030 449 0.001 3.2E-16 -15.50 0.94 46 1 0.14 9.43E+4 4.974 0.116 0.290 0.025 0.07 5.00 17.49 
TUL 0.155 0.030 438 0.001 2.8E-17 -16.56 1.61 37 12 0.23 1.75E+5 5.244 0.111 0.283 0.024 0.12 7.06 17.98 
TMN 0.111 0.020 277 0.001 4.5E-19 -18.34 0.97 74 35 0.09 1.40E+6 6.147 0.108 0.317 0.042 0.19 10.90 19.28 
TLL 0.131 0.031 502 0.001 3.7E-17 -16.44 1.65 51 24 0.19 6.01E+4 4.779 0.521 0.216 0.061 0.12 6.27 17.23 
TM2 & TM1 0.103 0.025 298 0.001 2.3E-20 -19.63 3.67 21 42 0.46 3.40E+6 6.532 0.097 0.442 0.073 0.20 14.48 21.25 
PV3 0.043 0.040 125 0.004 2.9E-18 -17.54 1.57 16 2 0.37 1.00E+6 6.000 0.278 0.286 0.065 0.42 9.04 18.53 
PV2a 0.275 0.096 13 0.027 a a a a a a 2.17E+5 5.336 0.156 0.059 0.007 0.36 5.03 15.63 
PV2v 0.229 0.132 40 0.021 4.3E-13 -12.37 1.38 16 0 0.34 1.94E+4 4.287 0.042 0.293 0.011 0.13 -0.19 0.23 
BT1a 0.285 0.051 46 0.008 3.5E-17 -16.45 2.74 9 1 0.87 4.72E+6 6.674 0.183 0.349 0.073 0.38 7.39 18.61 
BT1v 0.331 0.091 76 0.010 2.1E-13 -12.67 1.11 35 0 0.19 1.35E+4 4.131 0.049 0.240 0.008 0.06 -2.07 0.23 
CHV 0.346 0.049 130 0.004 1.6E-12 -11.81 1.62 46 0 0.24 3.39E+3 3.530 0.094 0.158 0.008 0.06 -3.80 0.23 
CHZ 0.322 0.048 520 0.002 5.2E-18 -17.28 0.91 99 17 0.08 4.45E+5 5.649 0.094 0.257 0.022 0.26 8.30 18.10 
BTa 0.271 0.046 73 0.005 8.2E-19 -18.08 2.05 9 8 0.50 6.42E+6 6.808 0.043 0.499 0.036 0.36 11.87 21.01 
BTv b b b b b b b b b b 5.04E+4 4.703 0.207 0.147 0.020 b -0.87 14.77 
PP4 0.321 0.047 52 0.006 1.5E-16 -15.81 2.74 6 2 0.97 5.00E+5 5.699 0.401 0.474 0.224 0.29 7.13 19.55 
PP3 0.318 0.032 168 0.002 6.4E-15 -14.20 0.75 51 0 0.11 1.32E+5 5.120 0.084 0.407 0.031 0.08 3.37 18.01 
PP2 0.221 0.058 127 0.005 5.4E-17 -16.27 1.18 34 3 0.19 6.22E+5 5.794 0.147 0.309 0.041 0.10 6.69 18.09 
PP1 0.297 0.043 280 0.003 8.1E-17 -16.09 1.52 27 1 0.29 1.13E+5 5.052 0.234 0.272 0.036 0.30 6.05 17.63 
BF3/TR3 0.175 0.104 126 0.009 1.1E-15 -14.95 1.64 7 1 0.58 8.94E+4 4.951 0.931 0.193 0.117 0.11 3.11 16.20 
BF2 0.234 0.049 40 0.008 c c c c c c 8.46E+6 6.927 0.032 0.617 0.070 0.21 8.86 21.17 
Output  DTN: LB0207REVUZPRP.002 
NOTE: (a) BT1a was used as an analog for permeability because only one permeability data point is available for 
PV2a. 
(b) BT1v was used as an analog for porosity, residual saturation, and permeability because only one 
sample is available for BTv. 
(c) PP1 was used as an analog for permeability because only one measurable permeability data point is 
available for BF2. 
k is permeability.
s is standard deviation.
n is number of samples.
f is porosity.
nd is number of samples with non-detected permeability measurements.
a and m are fitting parameters for the van Genuchten water potential relationship.
SE is standard error.
Sr is residual liquid saturation.
. is defined in Equation 34.
HGU refers to hydrogeologic units. Relation between HGU and UZ model layers is given in Table 4.
E-15 is the same as 10-15.
6.2.1 Matrix Permeability 
Matrix permeability was measured on core samples from several boreholes (including SD-6 and 
WT#24) at Yucca Mountain. Measurements are available for layers from the CUC down to the 
BF2 (Table 8). Two different permeameters were used to measure permeability, with the 
detection limit of the first higher than the second. Most of the samples were tested using the first 
MDL-NBS-HS-000014 REV00 49 April 2003 

Analysis of Hydrologic Properties Data U0090 
permeameter; the second was used to test some new samples and retest some old samples tested 
using the first permeameter, including some with permeabilities too low to measure (nondetect 
results). When the same sample was tested on both permeameters, the permeability measured on 
the one with the lower detection limit was used. This was because the permeameter with the 
lower detection limit was expected to result in a more reliable measurement. 
The measured data are presented in terms of saturated hydraulic conductivity (m/s), K, which is 
converted to permeability (m2), k, by the following relationship 
w
k = K 
(Eq. 27)
g.
w 
where w is the viscosity of water (0.001 N s/m2), g is the acceleration of gravity (9.81 m/s2), and 
.w is the density of water (998 kg/m3). These parameter values correspond to a temperature of 
20C (a typical room temperature at which the hydraulic conductivities were measured) 
(Lide 2002 [160832], p. 6-3). 
Permeability is considered to be a log-normally distributed quantity (Gelhar 1993 [101388], p. 
2). Therefore, the geometric mean was used to represent the average permeability of each model 
layer. The standard deviation of the log-transformed permeabilities, log(k), is used as the basis 
for uncertainty, which is detailed below. Where there are no nondetect measurements in the data 
set for a layer, the calculation of the average and standard deviation of the data is simple. When 
there are nondetect measurements present, they must be taken into account because they may 
represent important information about the extent of the lognormal distribution below the 
detection limit. They are taken into account as follows: 
 All data points, including nondetects, are ranked and assigned a percentile. 
 The data points are fit to a lognormal distribution, based on their percentile ranking. The 
fitting parameters are kg, the geometric mean of the permeability data, and slog(k), the 
standard deviation of the log transformed permeability data. 
The geometric mean permeabilities calculated above represent the average behavior of the core-
scale samples. For a given model layer, this averaged permeability can be very different from the 
effective matrix permeability used to represent large-scale water flow and solute transport due to 
the scale effects (e.g., Paleologos et al. 1996 [105736], Figure 4, p. 1337). While many upscaling 
methods are available in the literature, a method for highly heterogeneous porous media is 
described by the following expression (Paleologos et al. 1996 [105736], p. 1336) 
2
k =k exp[s 
(21 
-D)] (Eq. 28)
k
eg ln()
where ke is the effective permeability, kg is the geometric mean of small (core) scale 
permeability, sln(k)
2 is the variance of the natural log-transformed permeability, and D is a 
function of spatial dimensions (e.g. 2-D and 3-D) and the correlation scale of ln(k). Note that the 
geometric mean permeability is not the same as the effective permeability in a general case. For 
a 3-D isotropic problem, D = 1/3 when the characteristic size of a flow domain under 
MDL-NBS-HS-000014 REV00 50 April 2003 

Analysis of Hydrologic Properties Data U0090 
consideration (say, a model layer) is much larger than the correlation length (Paleologos et al. 
1996 [105736], p. 1336). For a site-scale model layer, these conditions are approximately 
satisfied. In this case, Equation 28 can be rewritten as 
2
log(k )=log(k )+38.0 slog() (Eq. 29)
eg k 
where slog(k)
2 is the variance of the log transformed permeability. 
In these layers, the amount of upscaling predicted by Equation 28 is as large as five orders of 
magnitude. An upper limit of 1.5 orders-of-magnitude upscaling is imposed on layers CUL and 
CW, CMW, CNW, TC, TM2 and TM1, and BT1a and PP4 (Assumption 1). For all other layers, 
the amount of upscaling predicted by Equation 28 is less than 1.5 orders of magnitude. Use of 
this limiting scheme is based mainly on the following consideration. Equation 28 was developed 
for a porous medium (single continuum), and can only be considered as an approximation for a 
dual-continuum system. For example, the existence of fractures, which may act as a capillary 
barrier, can increase tortuosity of liquid water flow in the matrix and therefore reduce the 
effective permeability compared to the case without fractures. This situation is not considered in 
Equation 28. 
6.2.2 Porosity 
Matrix porosity was also measured on core samples from the UZ. Porosity was determined after 
drying samples in a 105C oven for at least 48 hours to obtain a standard dry weight (Flint 1998 
[100033], p. 17). Porosity is considered to be a normally distributed quantity, so the arithmetic 
mean of core measurements and standard deviation were used to characterize the porosity for a 
model layer. 
6.2.3 Matrix van Genuchten Parameters 
The relationships described by van Genuchten (1980 [100610], pp. 892893) were used to 
characterize unsaturated flow in the matrix of Yucca Mountain. Use of the water potential versus 
saturation relationship allows the prediction of the relative permeability relationship. The 
predicted relative permeability is compared with permeability data where available. 
The van Genuchten parameters are Ss (satiated saturation), Sr (residual saturation), a, and m. 
Satiated saturation is defined to be 1.0, i.e., residual gas saturation is neglected. Residual 
saturation is calculated based on two porosity measurements as described below. With satiated 
and residual saturation fixed, aand m are adjusted to fit water potential and saturation data. 
6.2.3.1 Residual Saturation 
Residual saturation was determined from relative humidity (RH) porosity and total porosity. RH 
porosity was measured after drying a sample for 48 hours in a 60C and 65% relative humidity 
oven. This process is designed to remove water from the pores that contributes to flow, leaving 
only bound water and water in the smallest pores (Flint 1998 [100033], pp. 1718). Layer 
average values for RH porosity are calculated in the same manner as total porosity (see Section 
6.2.2). The layer average values of RH porosity are subtracted from the layer average values of 
total porosity to provide an estimate of residual water content (i.e., the amount of water left in the 
MDL-NBS-HS-000014 REV00 51 April 2003 

Analysis of Hydrologic Properties Data U0090 
pores and bound to the minerals after relative permeability (or hydraulic conductivity) has been 
reduced to zero). Residual saturation was calculated by dividing the residual water content by 
total porosity. 
6.2.3.2 Matrix a and m 
Desaturation data (water potential and saturation) from a number of samples (at least one for 
each layer) were measured, while a core sample was drying. DTNs associated with these data 
and their uses are given in descriptions of Excel files MRC_Q_TCw_fin.xls, 
MRC_Q_PTn_fin.xls, MRC_Q_Tsw_fin.xls, and MRC_Q_CHCF_fin.xls (Attachment III of this 
report). These data were used to calculate the a and m parameters for each layer by fitting to 
Equation 8. The best-fit parameters were obtained by minimizing the sum of the squared 
saturation residuals, 
nn 
2
. r =.( S - S()) (Eq. 30)
. i 
2 
ii 
i= 1 i= 1 
where ri is a saturation residual, n is the number of saturation and water potential data pairs for a 
layer, Si is a saturation data point, and S(. i) is the saturation predicted by the van Genuchten 
relationship for water potential, . i. 
The uncertainty or standard error of a and m is given by the diagonal terms of the covariance 
matrix, 
2 T - 1
C = s0 ( J J ) (Eq. 31) 
where Cis the covariance matrix, s02 is the error variance, and J is the Jacobian matrix. It should 
be noted that standard error, SE, can be related to the standard deviation, s , which is given for 
other properties, by 
SE =s (Eq. 32)
n 
6.2.4 Matrix Relative Permeability 
DTNs associated with data (used for calculating relative permeability) and their uses are given in 
descriptions of Excel files MRC_Q_TCw_fin.xls, MRC_Q_PTn_fin.xls, MRC_Q_Tsw_fin.xls, 
MRC_Q_CHCF_fin.xls, and VG_Summary_fin.xls (Attachment III of this report). According to 
van Genuchten (1980 [100610], p. 893), relative permeability (kr) can be related to effective 
water saturation (Se) as 
m 2
1/ m )] }
k = S 1/ 2 { [1- (1- S (Eq. 33)
re e 
However, recent studies indicate that a more general expression for relative permeability is 
(Schaap and Leij 2000 [160841], pp. 843844): 
MDL-NBS-HS-000014 REV00 52 April 2003 

Analysis of Hydrologic Properties Data U0090 
m 2
1/ m )] }
k = S .{ [1- (1- S (Eq. 34)
re e 
where . is an empirical constant. Many studies show that . is not 0.5 as assumed by van 
Genuchten (1980 [100610]), but varies over a very large range (Schaap and Leij 2000 [160841], 
pp. 843844). This is consistent with the matrix relative permeability data collected from the UZ. 
To determine an . estimate for a UZ model layer, the following equation was used to fit the 
unsaturated conductivity (K) data collected within the model layer: 
m 2
(
S K ) S .{[1- (1- S 1/ m )] } (Eq. 35)
ee e
= 
1/ m ]m }2
(
S K 0) 
S0 
.{[1- (1- S )
0 
K(S0) is the conductivity at a saturation S0, which is selected to be close to one. Equation 35 is 
derived by writing Equation 34 for a general value of Se and for Se=S0. The fitted .values are 
reported in Table 8 for different model layers (Wang 2003 [161654], SN-LBNL-SCI-003-V2, 
pp. 6383). They range from 2.58 to 14.48, which are consistent with cited by Schaap and Leij 
(2000 [160841], p. 844). Note that directly fitted .values are available only for hydrogeologic 
units CMW, CNW, BT4, BT3, TPP, BT2, PV2v, BT1v and CHV where unsaturated hydraulic 
conductivity data are collected (Table 8). For other units, the following empirical relation is used 
to estimate the .values: 
.= Am - B log(k ) + C (Eq. 36) 
and the corresponding standard errors are estimated by 
SE.= SEAm - SEB log(k ) + SEC (Eq. 37) 
where k is absolute permeability and A (8.14), B (1.99), and C (-28.24) are empirical parameters 
determined by fitting Equation 36 to . values for hydrogeologic units CMW, CNW, BT4, BT3, 
TPP, BT2, PV2v, BT1v and CHV where unsaturated hydraulic conductivity data are collected 
(Wang 2003 [161654], SN-LBNL-SCI-003-V2, pp. 6383). SEA, SEB, and SEC are the standard 
errors for A, B, and C, respectively, and determined from the curve fitting (Wang 2003 [161654], 
SN-LBNL-SCI-003-V2, p. 82). Values calculated from Equations 36 and 37 are reported in 
Table 8. 
A comparison between the currently obtained results based on Equation 34 and the van 
Genuchten relative permeability-saturation relation, Equation 33 can be easily made with 
kr (Se ) = Se 
.-5.0 
(Eq. 38)
kVG r (Se )
, 
where subscript VG refers to relative permeability obtained from the van Genuchten relation, 
Equation 33. Equation 38 is derived from Equations 33 and 34. Since estimated .values were 
very different from 0.5 for many model layers (Table 8), relative permeabilities predicted with 
van Genuchten relation, Equation 33, have considerable errors, especially for low saturations. 
MDL-NBS-HS-000014 REV00 53 April 2003 

Analysis of Hydrologic Properties Data U0090 
The errors become insignificant for saturations close to one, which is the case for welded units 
under the ambient conditions. Also note that a large degree of uncertainty in estimated .values 
exists because of data limitations. Therefore, the van Genuchten relation is still used in modeling 
studies in this report. 
6.3 THERMAL PROPERTIES 
Thermal properties include rock grain density, dry and wet rock thermal conductivities, rock 
grain specific heat capacity, matrix porosity, lithophysae porosity, and fracture porosity 
(Table 7). These properties are basic inputs into model studies involving heat flow. 
Thermal properties for the UZ Model layers were developed from the thermal-property data for 
the various lithostratigraphic layers (DTNs: SN0206T0503102.005 [160258] and 
SN0208T0503102.007 [160257]). The first of these two DTNs supplies thermal properties for 
most of the lithostratigraphic layers except the geological layers at the proposed repository 
horizon. The second DTN deals with thermal properties of the geological layers in the repository 
horizon, namely the upper lithophysal, the middle nonlithophysal, the lower lithophysal, and the 
lower nonlithophysal stratigraphic units of Topopah Spring welded tuff. The development of 
data in DTN: SN0208T0503102.007 [160257] is discussed in a Model Report entitled Thermal 
Conductivity of the Potential Repository Horizon Model Report (BSC 2002 [160319]). 
The matrix porosities in DTN: SN0206T0503102.005 [160258] and DTN: 
SN0208T0503102.007 [160257] are based on petrophysical measurements. Borehole 
petrophysical measurements of bulk density and neutron porosity are used to make quantitative 
estimates of matrix porosity (BSC 2002 [160319], Section 4). These data provide substantial 
information regarding the spatial heterogeneity of porosity across the entire site. To arrive at the 
thermal conductivities of the geological layers in the model reports mentioned above, those 
matrix porosity data (as well as the thermal conductivity of the solid minerals, and the geometry 
and connectivity of the solid) were treated as spatial random functions (BSC 2002 [160319]). A 
geostatistical method, sequential Gaussian simulation, was used to develop 50 independent, 
equally likely realizations of these uncertain properties. The measured porosity data (and other 
data) were used to condition the geostatistical simulations. These conditioned property sets then 
served as inputs to the matrix thermal conductivity model, yielding geostatistically based 
realizations of the matrix thermal conductivity. Since the developed matrix thermal-conductivity 
data were dependent on the input matrix porosity data, the porosity data in the above two DTNs 
are adopted as appropriate matrix porosities in preference to those given in Table 8 for the 
thermal property set. 
The correlation of the UZ Model layers with the geological units is shown in Table 4. In most 
cases, a UZ Model layer directly corresponds to a unique lithostratigraphic unit. In such 
instances, the thermal properties are adopted directly from their corresponding stratigraphic unit 
without alteration. On the other hand, when a UZ Model layer is composed of two or more 
adjacent lithostratigraphic units, the averaging technique of Francis (1997 [127326], pp. 57) is 
used for estimating the properties while assuming an equal thickness for all the relevant units. 
The conceptual model underlying this technique is that heat flow is one-dimensional and in a 
direction normal to interfaces between the units under consideration. This is appropriate 
considering that heat flow in the ambient system and in the disturbed system (during repository 
MDL-NBS-HS-000014 REV00 54 April 2003 

Analysis of Hydrologic Properties Data U0090 
heating) at Yucca Mountain is predominantly vertical. (This is because the horizontal dimensions 
of the repository horizon are much larger than the vertical dimension.) The corresponding 
equivalent thermal conductivity (.wet or dry, eq), grain density (.g,eq), and heat capacity (Cp,eq) are 
calculated using the following equations which were derived from those of Francis (1997 
[127326], pp. 57) assigning a uniform thickness for different geologic units within each model 
layer containing more than one geologic units (Table 4): 
n 
n..i k 
.
, 
eq k 
i=1
= (k = wet or dry) (Eq. 39)
, 
nn 
. ( ..)
i 
j=1 i=,1 i. j 
n 
.. ,
i g 
.= i=1 (Eq. 40)
eq g 
, 
n 
n 
,, 
C =
.C i p .i g 
i=1 (Eq. 41)
eq p 
, n.
eq g 
, 
where n is the total number of the involved lithostratigraphic units, and .g,i, .g,i and Cp,i are heat 
conductivity, grain density, and heat capacity, respectively, for a lithostratigraphic unit i. Note 
that the use of an equal thickness for all the relevant units within a model layer is adequate here 
because differences between thermal properties for these units (within a model layer) are not 
significant. Additionally, resultant matrix porosities are the simple arithmetic mean of the 
porosities for the constituent stratigraphic units. The determined thermal properties for the UZ 
Model layers are given in Table 9. The determination of the properties is described in a Scientific 
Notebook (Wang 2003 [161654], SN-LBNL-SCI-100-V3, pp. 6979). 
The thermal conductivities listed in Table 9 are matrix thermal conductivities. For lithophysal 
stratigraphic units and corresponding UZ Model layers, it is often necessary to use the bulk 
thermal conductivities instead of the matrix thermal conductivities. For stratigraphic units 
Tptpul, Tptpmn, Tptpll and Tptpln (or UZ Model layers tsw33, tsw34, tsw35 and tsw36), 
lithophysal porosities are listed in Table 9 (from DTN: SN0208T0503102.007 [160257]). For 
these stratigraphic units, the bulk thermal conductivities are also listed in DTN: 
SN0208T0503102.007 [160257]. The bulk thermal conductivities of the corresponding UZ 
Model layers are listed in Table 10. For further details, refer to the Scientific Notebook (Wang 
2003 [161654], SN-LBNL-SCI-100-V3, pp. 6979). 
The thermal properties of the faults are developed using the averaging techniques discussed 
above (Francis 1997 [127326]). The UZ Model represents faults as having four layers that are 
defined by the major hydrogeologic units (HGU), TCw, PTn, TSw, and CHn/Cfu. For each of 
these units, averages are taken across all the stratigraphic subunits. For example, to obtain the 
matrix thermal properties of tcwf, averages were taken over Tpcr, Tpcp, Tpcpv3, and Tpcpv2. 
The details of the calculations can again be found in a Scientific Notebook (Wang 2003 
MDL-NBS-HS-000014 REV00 55 April 2003 

Analysis of Hydrologic Properties Data U0090 
[161654], SN-LBNL-SCI-100-V3, pp. 7679). The calculated fault thermal properties are listed 
in Table 11. 
The data reported in Tables 9, 10, and 11 have been compiled and submitted to the TDMS under 
Output - DTN: LB0210THRMLPRP.001. 
Table 9. Matrix Thermal Properties for the UZ Model Layers 
Model Layer Grain Density 
(kg/m3) 
Grain Specific 
Heat Capacity 
(J/kg-K) 
Dry Thermal 
Conductivity 
(W/m-K) 
Wet Thermal 
Conductivity 
(W/m-K) 
Matrix 
Porosity(-) 
Lithophysae 
Porosity(-) 
tcw11 2514 985 1.3023 1.8039 0.1183 N/A 
tcw12 2514 985 1.3023 1.8039 0.1183 N/A 
tcw13 2274 1040 0.6698 0.7944 0.0457 N/A 
ptn21 2288 1040 0.4890 1.0660 0.3541 N/A 
ptn22 2288 1040 0.4890 1.0660 0.3541 N/A 
ptn23 2288 1040 0.4890 1.0660 0.3541 N/A 
ptn24 2288 1040 0.4890 1.0660 0.3541 N/A 
ptn25 2288 1040 0.4890 1.0660 0.3541 N/A 
ptn26 2283 1040 0.5374 0.9569 0.2513 N/A 
tsw31 2274 1040 0.6698 0.7944 0.0457 N/A 
tsw32 2514 985 1.3023 1.8039 0.1183 N/A 
tsw33 2358 985 1.3234 1.9059 0.1425 0.123 
tsw34 2466 985 1.4553 2.1276 0.1287 0.025 
tsw35 2325 985 1.3998 2.0701 0.1486 0.088 
tsw36 2473 985 1.5356 2.1958 0.1058 0.03 
tsw37 2473 985 1.5356 2.1958 0.1058 0.03 
tsw38 2274 1040 0.6698 0.7944 0.0457 N/A 
tsw39 2274 1040 0.6698 0.7944 0.0457 N/A 
ch1Ze 2288 1040 0.4890 1.0660 0.3541 N/A 
ch1VI 2288 1040 0.4890 1.0660 0.3541 N/A 
ch[2-5]V 2256 1038 0.5996 1.2708 0.3282 N/A 
ch[2-5]Z 2256 1038 0.5996 1.2708 0.3282 N/A 
ch6 2256 1038 0.5996 1.2708 0.3282 N/A 
pp4 2103 1040 0.5375 1.1095 0.2974 N/A 
pp3 2103 1040 0.5375 1.1095 0.2974 N/A 
pp2 2385 1009 0.7326 1.3421 0.2331 N/A 
pp1 2038 1040 0.5641 1.1302 0.2731 N/A 
bf3 2106 1018 0.7570 1.3292 0.1883 N/A 
bf2 2012 1040 0.5765 1.1400 0.2615 N/A 
tr3 2371 1019 0.6137 1.2272 0.2799 N/A 
tr2 2224 1040 0.4977 1.0778 0.3354 N/A 
Output - DTN: LB0210THRMLPRP.001 
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Analysis of Hydrologic Properties Data U0090 
Table 10. Bulk Thermal Conductivities of Repository Model Layers 
Model Layer Dry Thermal Conductivity (W/m-K) Wet Thermal Conductivity (W/m-K) 
tsw33 1.164 1.675 
tsw34 1.419 2.074 
tsw35 1.278 1.889 
tsw36 1.49 2.13 
Output - DTN: LB0210THRMLPRP.001 
Table 11. Fault Thermal Properties 
Major Unit Fault 
Layer 
Grain 
Density 
(kg/m3) 
Grain Specific 
Heat Capacity 
(J/kg-K) 
Dry Thermal 
Conductivity 
(W/m-K) 
Wet Thermal 
Conductivity 
(W/m-K) 
Matrix 
Porosity(-) 
TCw tcwf 2394 1011 0.8846 1.1030 0.082 
PTn ptnf 2286 1040 0.5086 1.0164 0.31 
TSw tswf 2368 1003 0.9980 1.2988 0.1026 
CHn/CFu chnf 2198 1034 0.5731 1.1706 0.295 
NOTE: CFu is assigned the same property value as CHn. 
Output - DTN: LB0210THRMLPRP.001 
6.4 FAULT PROPERTIES 
The UZ Model represents faults as having four layers defined by the major HGUs: TCw, PTn, 
TSw, and CHn/CFu. The constituent sublayers of these HGUs are shown in Table 4. Fault, 
fracture, and thermal properties are calculated for these four layers. Each HGU has been 
approximately assigned the uniform properties within faults. The reason for this consolidation is 
that data to characterize faults are very limited. Matrix hydraulic properties in faults, however, 
are assigned the same as the corresponding nonfault UZ properties. 
Direct measurements of fault-specific properties were limited to air-injection tests performed in 
Alcoves 2, 6, and 7, which are also called the Bow Ridge fault alcove, the North Ghost Dance 
fault access drift, and the South Ghost Dance fault access drift, respectively. Analysis of 
crosshole tests run in the Bow Ridge fault alcove (LeCain 1998 [100052], p. 21) and the North 
Ghost Dance fault access drift (DTN: GS990883122410.002 [135230]; LeCain et al. 2000 
[144612]) gave the best estimates of fracture permeability in the TCw and TSw fault layers, 
respectively. 
All other fault properties were calculated as averages of nonfault layer. Some layers are much 
thicker than others, and thus the properties of those layers should be weighted more heavily 
when calculating the fault properties. Properties were weighted by their respective average layer 
thickness. Porosity was arithmetically averaged because its differences between model layers 
within each HGU are not significant, 
MDL-NBS-HS-000014 REV00 57 April 2003 

Analysis of Hydrologic Properties Data U0090 
n 
.
L p 
ii 
=p 
i=1 
(Eq. 42)
an 
. Li 
i=1 
where pa is the weighted arithmetic average property (porosity), n is the number of layers being 
averaged, pi is the property for layer i, and Li is the thickness of layer i. The fracture-matrix 
interface areas are also calculated using Equation 42. Permeability is more appropriately 
harmonically averaged because it is generally considered to be log-normally distributed (Gelhar 
1993 [101388], p. 2), 
n 
. Li 
=p 
i=1 
(Eq. 43)
hn 
. Li 
i=1 pi 
where ph is the weighted harmonic average property (permeability). Layer thickness is estimated 
as the average (arithmetic) layer thickness over the GFM2000 model (DTN: 
MO0012MWDGFM02.002 [153777]) area. Another consideration for using Equation 43 is that 
it is exact when th flow direction is perpendicular to interfaces between model layers. This is 
approximately the case for the UZ because the flow direction is mainly vertical. 
In principle, a more rigorous way to estimate the fault (fracture) properties is to correlate them 
with geologic information specific to each fault being modeled and to individual locations within 
each fault, such as amount of fault offset, width of the disturbed zone, and presence of contacts 
with significant property changes. This alternative approach, however, requires the development 
of relationships between hydraulic properties and geologic information that can not be reliably 
estimated with available data regarding fault properties. 
Fracture permeability for the TCw and TSw fault layers was given by the crosshole air-injection 
tests described above. Permeability for the PTn and CHn/CFu fault layers was calculated by 
scaling the weighted average bulk-rock fracture permeability. As with the matrix permeability, 
equivalent fracture permeability was calculated for all four fault layers using the weighted 
harmonic mean of permeabilities for the corresponding nonfault model layers. The average 
(geometric mean) ratio of the measured permeability to the calculated equivalent permeability 
for layers TCw and TSw was calculated (Ahlers 2000 [155853], pp. 124125). This factor 
multiplies the calculated equivalent permeability of the PTn and CHn/CFu layers to scale them 
upward. This process is equivalent to the process used to scale bulk-rock matrix a, which is 
explained in Section 6.2. 
Equivalent fracture spacing, equal to the inverse of fracture frequency, can be calculated using 
the weighted arithmetic mean. Again, it can be shown that the weighted arithmetic mean of 1/pi 
is equal to the harmonic mean of pi, so the weighted harmonic mean of frequency is used to 
calculate the equivalent frequency for the faults. 
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Analysis of Hydrologic Properties Data U0090 
Fracture aperture was calculated as in Section 6.1 (using Equation 7), based on the cubic law and 
the fault permeabilities and frequencies. 
Fault fracture porosity was determined by scaling the weighted arithmetic mean of bulk-rock 
fracture porosity. The scaling factor is the ratio of fault fracture aperture to mean bulk-rock 
fracture aperture. The mean bulk-rock fracture aperture was calculated as the weighted 
arithmetic average of fracture aperture. 
The fracture van Genuchten m (mf) is taken as 0.633 as for all other fractures (see Section 6.1). 
The fracture van Genuchten a(af) is calculated based on the fracture aperture, using Equation 9 
as documented in Section 6.1. 
The fracture-to-matrix connection area for the faults was approximated as the weighted 
arithmetic mean of bulk rock fracture-to-matrix connection area. The rationale for the 
development of fault properties is documented in a Scientific Notebook (Ahlers 2000 [155853], 
pp. 117127, 145146). Table 12 presents the calculated fault fracture properties. 
Table 12. Calculated Fault Fracture Properties 
Major Unit Fault Layer Permeability 
(m2) 
Porosity 
(-) 
Frequency 
(m-1) 
af (Pa-1) mf (-) Interface 
area 
(m2/m3) 
TCW tcwf 2.7E-11 2.9E-2 1.9 3.8E-3 0.633 12.9 
PTn ptnf 3.1E-12 1.1E-2 0.54 2.8E-3 0.633 1.3 
TSw tswf 1.5E-11 2.5E-2 1.7 3.2E-3 0.633 8.7 
CHn/CFu chnf 3.7E-13 1.0E-3 0.13 2.3E-3 0.633 0.46 
Output-DTN: LB0207REVUZPRP.001 
6.5 CONFIRMATION OF FRACTURE PROPERTIES 
Uncertainties generally exist in the estimated rock properties. This is particularly true for the 
fracture properties, such as fracture van Genuchten parameters and porosity, because they are not 
directly measured, but indirectly estimated from other property measurements. In addition to 
model calibration, it is useful to confirm the estimated properties by independent methods and 
relevant data. In this subsection, the ESF seepage test results are used to independently determine 
fracture van Genuchten a and porosity for confirmation purposes. The determination procedures 
are very different from those used in Section 6.1 of this report. This confirmation activity is also 
documented in a Scientific Notebook by Liu (2001 [155675], pp. 6468). 
6.5.1 ESF Seepage Tests 
After Niche 3650 (Niche 2) in the ESF was excavated, a series of seepage tests were performed 
by pumping water into boreholes labeled UL, UM, and UR, located above the niche 
(BSC 2001 [158463], Section 6.2). Water was released into several packed-off intervals in these 
boreholes. Tracers were also introduced during the seepage tests. Water entering the niche was 
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Analysis of Hydrologic Properties Data U0090 
captured, and water arrival times were recorded during the tests. Based on water seepage rates 
and the corresponding water release fluxes for a given test interval, a seepage threshold flux was 
determined (DTN: LB980901233124.003 [105592]). The threshold flux is defined as the 
water-release flux within a test interval at which seepage into the niche no longer occurred. The 
threshold water flux inferred from observations and water-arrival-time data are the primary data 
used for determining van Genuchten fracture a (a f) and porosity. The test sites were located at 
the fractured middle nonlithophysal zone of the Topopah Spring welded unit, corresponding to 
the UZ Model layer tsw34. A detailed discussion of the tests can be found in BSC (2001 
[158463], Section 6.2). 
6.5.2 Approach 
The approach for determining van Genuchten fracture a from the seepage test results is based on 
the theory of Philip et al. (1989 [105743]). They developed analytical solutions for water 
exclusion from, or entry into, cavities from downward seepage through unsaturated porous 
media. The considerations they used to derive their solutions are as follows (Philip et al. 1989 
[105743], pp. 1623): 
First, liquid water flow is downward and steady, and the concerned porous medium is isotropic 
and homogeneous. To be consistent with this consideration, our analyses used the results for the 
seepage tests associated with fracture networks. This was because a fracture network may be 
conceptualized as a continuum such that the solutions developed for porous media can be 
approximately applied. Data in DTN: LB980901233124.003 [105592] indicated that the tests 
were associated with either connected fracture networks or individual vertical fractures (or small 
groups of vertical fractures). A study by Finsterle (2000 [151875]) indicated that the continuum 
approach could be used for dealing with seepage (at the same scale) in practice. However, a 
theoretical validation of the continuum approach for the given scale is not available and further 
investigation may still be needed to resolve this issue. 
Second, Philip et al. (1989 [105743], pp. 1618) considered that the flow domain is infinite in 
extent and flow velocity in the upstream is spatially uniform. This is because liquid water flow in 
a fracture continuum is largely dominated by gravity and capillary-dispersion effects are weak. 
Therefore, it is not unreasonable to use the theory of Philip et al. (1989 [105743]) for analyzing 
the localized tests for fracture networks. 
Third, Philip et al. (1989. [105743], p. 18, Eq. 12) considered that there exists a functional 
relation between unsaturated hydraulic conductivity, K(. ) (m/s), and moisture potential, . (m), 
that is exponential in nature. 
' (. - . 0 ) (Eq. 44)
K (. ) = e K a
0 
where K0 (m/s) is the hydraulic conductivity at a referential water potential . 0 (m), and a' (m-1) 
is the sorptive number (corresponding to a in Philip et al. 1989 [105743]). In this study, we treat 
K0 as the saturated hydraulic conductivity, and therefore . 0 = 0 by definition. The sorptive 
number is a constant for a homogeneous porous medium, and usefully characterizes the capillary 
properties of the medium in unsaturated flow (Philip et al. 1989 [105743], p. 18). This number 
can be used to determine van Genuchten a , which will be discussed below. 
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According to Philip et al. (1989 [105743], pp. 19, 23), the threshold water flux, K0* (m/s), can be 
related to the saturated hydraulic conductivity, K0 as 
= K0[. (s)]-1 (Eq. 45)
max
K0* 
' 
with s = (1/2)al, where l (m) is the radius for a circular cylindrical cavity. For the ESF seepage 
tests, it is the radius of the niche. Under the condition that s is large (or capillary effects are 
weak), .max can be expressed as (Philip et al. 1989 [105743], p. 23, Eq. 83) 
11
. (s) = 2s + 2 -+ - ... (Eq. 46)
max 2
ss 
Based on Equations 45 and 46 a sorptive number, a', can be estimated from known saturated 
conductivity and threshold flux values for a given seepage test. The estimation of sorptive 
number values (a') are discussed in BSC (2001 [158463], Section 6.2) and given in the data set 
(DTN: LB980901233124.003 [105592]). Note that in Section 6.2 of BSC (2001 [158463]), the 
so-called capillary strength is the same as (a')-1 here. 
The sorptive number can be related to van Genuchten fracture a(af) by the following curve-
fitting procedure. Based on the definition of relative permeability, Equation 44 leads to a relative 
permeability (kr) relation (Liu 2001 [155675], pp. 6468) 
c
ln(k ) =
a'p (Eq. 47)
r 
.g 
where pc (Pa) is the capillary pressure, g (m/s2) is the gravitational acceleration, and .(kg/m3) is 
the liquid-water density. The van Genuchten (1980 [100610], Equations 3 and 8) relationships 
result in 
m 1
ln(k ) - = 
2 
*) ln[( n + 1]+ 1ln[ 2 - (1- )m ] (Eq. 48)
rp 
1 + ( p*)n 
where n and m=1-1/n are van Genuchten parameters, and p* is a dimensionless capillary pressure 
defined by 
p* =af 
p 
(Eq. 49)
c 
In terms of p*, Equation 47 can be rewritten as 
a' 
ln(k ) - = p * (Eq. 50)
r 
g
a . f 
For a given fracture m (mf) value and a range of p* values, a'/(.gaf) can be estimated by fitting 
Equation 50 through a number of data points calculated from Equation 48. Since a'is known, the 
corresponding af 
(Pa-1) can be easily estimated. 
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In addition to determining the van Genuchten fracture a, the seepage test results can be used to 
estimate the volumetric water content of the fracture continuum. The estimated saturated water 
contents provide useful information for confirming fracture properties estimated in Section 6.1 of 
this report. 
Under the conditions that water flow is one-dimensional and the wetting front has a constant 
velocity, the depth of the wetting front can be determined as 
t q 
z = s (Eq. 51)p (. 
.)
-
av r 
where zp (m) is the depth from the water supply source to the wetting front, t (s) is the arrival 
time of the front at depth zp, qs is the constant flux of water supplied at the source, .r 
is the 
residual water content, and .av is the average volumetric water content between the source and 
the wetting front. Water content is defined as volume of liquid water divided by the total rock 
volume. For the seepage tests, zp and t can be considered as the distance between the source and 
the ceiling of the niche, and the wetting front arrival time at the ceiling, respectively. A detailed 
discussion of the procedures to determine water content values was given in BSC (2001 
[158463], Section 6.2). Letting .r 
= 0, .av can be estimated for each seepage test. The estimated 
(.av -.r) values are reported in DTN: LB980901233124.003 [105592]. Note that the conditions 
for Equation 51 to hold are approximately satisfied when the capillary effects are weak, which is 
the case for water flow in fractures. 
Finally, it should be indicated that in the above discussion, the matrix imbibition was ignored, 
because for the given temporal and spatial scales of the seepage tests, the matrix imbibition is 
expected to be insignificant compared to the amount of water flowing through fractures. 
6.5.3 Results and Discussion 
The van Genuchten fracture a values estimated from the seepage test results are given in Table 
13. To estimate these values, modelers applied the curve fitting procedure for p* =5, based on 
the following considerations. First, van Genuchten fracture a is closely related to the air-entry 
pressure, which is mainly characterized by capillarity and relative permeability data at large 
saturations (or small capillary pressures). Therefore, it is appropriate to perform the curve fitting 
for a range of p*, corresponding to relatively small capillary pressure values, in order to estimate 
the van Genuchten fracture a. Second, considering a unit hydraulic head gradient condition, the 
water release flux at the source for a given seepage test can be considered as the unsaturated 
hydraulic conductivity. In this case, the ratio of the water release flux to the saturated hydraulic 
conductivity is an approximation of the relative permeability. The natural logs of the calculated 
ratio for the relevant seepage tests based on the data in DTN: LB980901233124.003 [105592] 
are larger than 5. In this case, p* = 5 results in relative permeability values that are adequate to 
cover this range (Figure 4). We also used mf = 0.633 for the curve fitting. The determination of 
this m value was given in Section 6.1 of this report. Curve fitting results are shown in Figure 4. 
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Table 13. Log Values of van Genuchten Fracture a Estimated from the ESF Seepage Tests (Niche 3650) 
Borehole (Depth(m)) Log(a)a 
Upper Left (7.01-7.32) -3.31 
Upper Middle (4.27-4.57) -2.73 
Upper Middle (5.49-5.79) -2.90 
Upper Right (4.27-4.57) -3.10 
Upper Right (4.88-5.18) -2.69 
Upper Right (5.49-5.79) -4.23 
average -3.16 
NOTE: a Log(af) is calculated using a' data (DTN: LB980901233124.003 [105592]). 
Table 13 shows that the estimated fracture a value varies for different test locations owing to 
heterogeneities. However, the average log(af) value is 3.16, which is very close to the log(af) 
value of 3.18, determined from air-permeability data for UZ Model layer tsw34 (Table 7). As 
indicated before, the test sites are located in zones represented by the model layer tsw34 
(Table 7). It is encouraging that independent approaches used to estimate the fracture a, based on 
different data sets, lead to similar fracture a values. This convergence indicates that the approach 
used to estimate fracture a based on air-permeability data and the resultant fracture avalues, 
reported in Section 6.1 of this report, are reasonable. 
Eq. [48] 
Eq. [50] 
Source: Liu (2001 [155675], pp. 6468)
Figure 4. Graph of Fitting of Equation 48 to Equation 50 using a Number of Data Points for p* < 5
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Analysis of Hydrologic Properties Data U0090 
The data from DTNs: LB980901233124.003 [105592] and DTN: LB0110LIQR0015.001 
[156907] include values for the fracture water-content change in the seepage tests. These data 
were collected from Niches 3650 and 4788, which are located in the same geological unit. As 
discussed before, this change is equal to the average volumetric water content when assuming 
zero residual water content. Herein, we are interested in the water content values under the 
saturated condition or when the liquid-water release flux at the source is close to the saturated 
hydraulic conductivity (or water flux is the largest among those used in the tests for a given 
testing interval). In these cases, the fracture networks are highly saturated, and therefore, the 
water-content values are good approximations of the corresponding fracture porosities. Several 
water-content values of this kind were given in DTNs: LB980901233124.003 [105592] and 
LB0110LIQR0015.001 [156907]. They are 0.0101, 0.0242, 0.0150, 0.0124 0.0024, 0.0200, 
0.0489, and 0.0092, respectively, and the average value is 0.018. The fracture porosity for the 
model layer tsw34, determined in Section 6.1, is about 0.0085, in the same order of magnitude 
with this average value. 
6.6 ACTIVE FRACTURE MODEL (AFM) 
In a dual-continuum approach, the treatment of fracture-matrix interaction is important for 
accurate modeling flow and transport. This subsection discusses the AFM used in the UZ Flow 
and Transport Model. The validation of this conceptual model is given in Section 7. 
6.6.1 Active Fracture Concept 
Although a number of mechanisms exist, fingering flow at a fracture-network scale is considered 
to be a key mechanism for limiting fracture-matrix interaction, more important than that at a 
single-fracture scale. We expect that for unsaturated fractured rocks, the water flow pattern 
should be characterized by significant preferential (fingering) flow at a fracture-network scale, 
on account of the large nonlinearity involved in an unsaturated system and heterogeneities of 
fracture structure at different scales. The AFM concept is based on the reasoning that because of 
fingering flow, only a portion of fractures in a connected, unsaturated fracture network 
contribute to liquid water flow, while others are simply bypassed. The portions of the connected 
fractures that actively conduct water are called active fractures. We hypothesize that the number 
of active fractures in the Yucca Mountain UZ is small compared with the total number of 
connected fractures. Hence, active fractures, rather than total connected fractures, should be used 
in numerical models. We further hypothesize that the number of active fractures within a 
gridblock is large, such that a continuum approach is still valid for describing fracture flow. 
These hypotheses are consistent with the consideration that fractures permitting flow in the UZ 
are many and highly dispersed. 
To use the AFM concept to model flow and transport in fractures, we treat active fractures as 
part of a homogeneous fracture continuum for a given gridblock. Note that differences exist 
between the AFM and the conventional, capillary-equilibrium-based, fracture water distribution 
model. The latter assumes that liquid water first occupies fractures with small apertures, and then 
occupies fractures with relatively large apertures as water potential (or water saturation) 
increases. In contrast, the AFM presumes gravity-dominated, nonequilibrium, preferential liquid 
water flow in fractures, which is expected to be similar to fingering flow in unsaturated porous 
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Analysis of Hydrologic Properties Data U0090 
media. A liquid finger can bypass a large portion of a porous medium, which does not 
necessarily correspond to large pores. 
Flow and transport conditions and fractured rock properties should determine the fraction of 
active fractures in a connected fracture network, fa. An expression for fa must satisfy the 
following conditions: all connected fractures are active (fa = 1) if the system is fully liquid 
saturated; all fractures are inactive (fa = 0) if the system is at residual saturation; and fa should be 
related to water flux in fractures. It is generally believed that more fractures are conducive to a 
larger water flux. The water flux in fractures is considered to be mainly dependent on fracture 
saturation, because fracture water flow is gravity-dominated. A simple expression for fa (-), 
which meets these conditions and includes one parameter only, is a power function of effective 
water saturation in connected fractures, Se (-). 
ae
f = S . 
(Eq. 52) 
where . (-) is a positive constant depending on properties of the corresponding fracture network, 
and the effective water saturation in connected fractures is given by 
Sf - S
r
S = 
e 
1 - Sr (Eq. 53) 
where Sf (-) is the water saturation of all connected fractures and Sr is the residual fracture 
saturation. Note that the satiated saturation is set to 1 and (-) means dimensionless here. In 
this study, Equation 52 is used to determine the fraction of active fractures because it is 
physically reasonable and mathematically simple. As discussed below, Equation 52 allows us to 
treat all the ramifications of the active fracture hypothesis (modified fracture capillarity, relative 
permeability, and fracture-matrix interaction reduction) in an integrated manner. The simple 
Equation 52 is considered as a first-order approximation. As will be shown in Section 7, it is 
consistent with a fractal flow pattern in a fracture network. 
6.6.2 Constitutive Relationships 
Note that only the active fracture continuum, a portion of the total fracture continuum, 
contributes to flow and transport in fractures and fracture-matrix interaction. Therefore, fracture 
hydraulic properties should be defined for active fractures. The effective water saturation of 
active fractures, Sae (-), is related to the effective water saturation in connected fractures, Se, by 
e
S = S = S 1-.
ae e
fa (Eq. 54) 
Because Sae = 1, . should be in a range between 0 and 1. The effective water saturation of active 
fractures is related to the actual water saturation in active fractures, Sa, by 
S - S
ar
S = 
ae 
1 - Sr (Eq. 55) 
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If all connected fractures are considered to be active in conducting water, as assumed in previous 
studies, the water capillary pressure for the fracture continuum may be described by the 
well-known van Genuchten relation (Equation 8). 
In AFM, however, the van Genuchten capillary pressure relation is considered to be relevant for 
the active fracture continuum, rather than for the whole fracture continuum. The capillary 
pressure for active fractures is determined by replacing Se in Equation 8 with Sae, 
1
S P ) = [S -1/ m - 1]1/ n = 1[S (.-1)/ m - 1]1/ n (Eq. 56)
(
ceae e
a a 
10-1 
100 
101 
102 
m = 0.5 
0.5 
0.9 
.- Pc x a  
= 0 (V-G relation) 
0.0 0.2 0.4 0.6 0.8 1.0 
S 
e 
Source: Liu et al. 1998 [105729], p. 2636, Figure 2 
Figure 5. Capillary Pressure Curves of Fracture Continuum for . = 0, 0.5, and 0.9 
Equation 56 should be used to simulate water flow in the fracture continuum. Figure 5 shows 
fracture capillary pressure curves for several . values. For a given effective water saturation in 
connected fractures, a larger . value corresponds to a larger effective water saturation in active 
fractures, and therefore to a lower absolute value for capillary pressure. 
The liquid-phase relative permeability for the active fracture continuum, kar (-), is directly 
determined by the effective water saturation of active fractures. However, as only a portion of 
the fractures are active, the relative permeability of the entire fracture continuum, kr, (-) should 
be the relative permeability of active fractures multiplied by fa, or 
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k = k f = S .k (Eq. 57)
r a ar e ar 
where kar can be given by the following van Genuchten permeability relation: 
1/ m }m ]2 (1-.)/ m }m ]2
k = S 1/2[1- 1{ - S = S (1-.)/2[1- 1{ - S (Eq. 58)
arae aee e 
Combining Equations (57) and (58) yields 
(1+.)/2[1- 1{ - S (1-.)/ m }m ]2
k = S (Eq. 59)
re e 
Relative permeability (kr) curves are shown in Figure 6 for several .values. In general, the 
relative permeability (kr) is affected by . in a complicated manner for a given Se. A larger . 
value, resulting in a higher effective water saturation in active fractures (Sae), gives rise to a 
larger value of kar. On the other hand, a larger . value corresponds to a smaller value of fa. 
Because the former effect is dominant, a larger . value gives a larger relative permeability for a 
given effective water saturation of the fracture continuum, as indicated in Figure 6. 
100 
10
-1 
10
-2 
10
-3 
10
-4 
10
-5 
10
-6 
10
-7 
10
-8 
10
-9 
0.5 
0.9 
k r 
.m=0.5 
= 0 (V-G relation) 
0.2 0.4 0.6 0.8 1.0 
S 
e 
Source: Liu et al. 1998 [105729], p. 2637, Figure 3 
Figure 6. Relative Permeability Curves of Fracture Continuum for . = 0, 0.5, and 0.9 
In the AFM, the fracture-matrix interface area reduction factor results from three aspects. First, 
the average interface area between mobile water (saturated liquid water segments) in an active 
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fracture and the surrounding matrix is smaller than the geometric interface area. Second, the 
number of active fractures is smaller than that of connected fractures. Conventionally, all the 
connected fractures are considered to contribute to fracture-matrix interaction. Third, average 
active fracture spacing is much larger than that for connected fractures. Under the quasi-steady-
state condition, flow and transport between fractures and surrounding matrix is inversely 
proportional to the corresponding fracture spacing. Based on these considerations and Equation 
53, Liu et al. (1998 [105729], pp. 26362638) derived an expression for the reduction factor: 
R . S 1+.(Eq. 60)
e 
Note that the AFM uses a combination of the volume-averaged method and a simple filter to deal 
with fracture flow and transport. Inactive fractures are filtered out in modeling fracture-matrix 
interaction, flow, and transport in the fracture continuum. We believe that filtering could add the 
capability to continuum approaches to capture dispersed fingering flow at a subgrid scale. Note 
that the . factor may be interpreted as a measure of the activity of connected fractures. 
Generally speaking, a smaller . value corresponds to a larger number of active fractures in a 
connected fracture network. For example, . = 0 results in fa = 1 in Equation 52, corresponding to 
all connected fractures being active. On the other hand, . = 1 corresponds to zero fracture 
capillary pressure (Equation 56), indicating that all active fractures are saturated. In the latter 
case, the fraction of active fractures is very small for small percolation fluxes, because relatively 
high fracture permeabilities measured at Yucca Mountain allow most of the water to flow 
through only a few fractures. 
6.7 AFM AND A FRACTAL-BASED FLOW MODEL 
6.7.1 Evidence of Fractal Flow- Patterns in Unsaturated or Multiphase Flow Systems 
Fractals have been shown to be a common language for describing many different natural 
phenomena (Mandelbrot 1983 [160848]). A vast literature exists for discussing the validity of the 
fractal concept in a great number of fields. Many studies recently show that complex flow 
patterns in unsaturated or multiphase flow systems can be described by fractals. For example, 
viscous fingering in porous media has been experimentally shown to be fractal (Feder 1988 
[160844], Chapter 4). (The problem of viscous fingering in porous media is of central 
importance in oil recovery.) Flury and Flhler (1995 [160845]) fitted a diffusion-limited-
aggregation (DLA) model for solute transport in one of three field plots under unsaturated 
conditions, and then predicted reasonably well for the other two. A detailed description of DLA 
is given in Flury and Flhler (1995 [160845]). The DLA generates fractal patterns (Feder 1988 
[160844], pp. 5356). Persson et al. (2001 [160840]) further confirms the finding of Flury and 
Flhler (1995 [160845]) by showing that flow patterns resulting from an unsaturated field site 
display a fractal resemblance. Glass (1993 [160751]) demonstrated that unsaturated flow patterns 
in individual fractures can be reasonably modeled by a percolation-based model. Percolation-
based models generate fractal patterns (Feder 1988 [160844], Section 7.8; Stauffer and Aharony 
1991 [160846], Section 6.6). Detailed experimental studies on unsaturated flow patterns in 
natural fracture networks are still lacking in the literature. 
Unsaturated flow patterns in a fracture network are expected to be (at least approximately) fractal 
also. This is supported by fracture coating data from the unsaturated zone of Yucca Mountain. 
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As will be discussed in Section 6.7.2, fracture coating is generally a signature of water flow 
paths. Detailed line survey data for coated fracture is available from DTN: 
GS980308315215.008 [107355]. Since Tptpmn (tsw34) unit has the largest number of survey 
intervals, data from this unit are analyzed using the box counting method that will be discussed 
in Section 7.2.2. The locations of the coated fractures along the survey line form a set of points 
in a one-dimensional space. For a given box size (length of a segment) l, there are 30/l small 
boxes (or segments) for a given survey interval that is 30 m long. N in this subsection denotes 
total numbers of boxes that cover at least one location of the coated fractures (along the survey 
line) for all the survey intervals. The determination of N as a function of l is given in Attachment 
II. Figure 7 shows that the observed N values as a function of l can be fitted by a power function 
with a power of 0.5 that corresponds to a fractal dimension of 0.5 for the set of points. A more 
detailed discussion of fractal dimension will be given in Section 6.7.2. This indicates that coated 
fractures may result from a fractal flow pattern in the corresponding fracture network. Note that 
for a given spatial pattern, fractal dimensions are different for different Euclidean dimensions of 
a space. The curve fitting is performed using Tecplot. 
20 30 40 
25 
50 
75 
100 
125 
150 
N 
10 50 
l(m) 
Figure 7. Relation between N and l (Attachment II). The data points correspond to observed values for 
the Tptpmn unit. 
6.7.2 Fractal Dimension 
A fractal pattern is characterized by the fractal dimension (df) that is generally noninteger and 
less than the corresponding Euclidean dimension of a space, D. There are different kinds of 
definitions for fractal dimension (Feder 1988 [160844], Section 2.3). The most straightforward 
definition is based on box counting. In this case, the fractal dimension is determined from the 
following equation by counting the number (N) of boxes (e.g., square and cubic for two-
dimensional and three-dimensional problems, respectively), needed to cover a spatial pattern, as 
a function of the box size (l) (Feder 1988 [160844], pp. 1415): 
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Analysis of Hydrologic Properties Data U0090 
l N )
(
=
..
L
l 
..
df 
(Eq. 61) 
where L refers to the size of whole spatial domain under consideration. Figure 8 shows a box-
counting procedure for a spatial pattern with df = 1.6 in a two-dimensional domain with size L 
(Yamamoto et al. 1993 [160843], Figure 3). 








Figure 8. Schematic Showing Demonstration of Box Counting Procedure for Several Box Sizes, With 
the Shaded Areas Containing Saturation and Active Flow 
Obviously, if a spatial pattern is uniformly distributed in space, the fractal dimension will be 
identical to the corresponding Euclidean dimension. In this case, the box number, N* , and the 
box size l have the following relation 
D 
N *(l )
=
..
.
L
l 
..
.
(Eq. 62) 
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6.7.3 Consistency of the AFM and a Fractal Flow Pattern 
Consider Figure 8a to be a gridblock containing a fracture network and the corresponding flow 
pattern in the fracture network to be fractal. In this case, only a portion of the medium within a 
gridblock contributes to water flow (Figure 8). This is conceptually consistent with the AFM 
(Liu et al. 1998 [105729]). Note that in Figure 8, a box is shaded if it covers one or more 
fractures (or fracture segments) that conduct water. For simplicity, further consider that fractures 
are randomly distributed in space and thus the dimension for water saturation distribution is the 
corresponding Euclidean dimension when all the connected fractures actively conduct water. 
Combining Equations 61 and 62 gives 
[l N )]1/ df =[N *(l)]1/ D (Eq. 63) 
( 
The average water saturation (S) for the whole gridblock (Figure 8a) is determined as 
V
S = 
lDfN *(l) 
(Eq. 64) 
where V is the total water volume (excluding residual water) in fractures within the gridblock 
(Figure 8a), and f is fracture porosity. Similarly, the average water saturation (Sb) for shaded 
boxes with a size of l is given as 
V
Sb = (Eq. 65)
lDfl N )
(
From Figure 8, it is obvious that there exists a box size l1 < L satisfying: 
V 
l1 
Df= 1 (Eq. 66) 
Based on Equations 6366, the average saturation for shaded boxes with a size of l1, Sb1, can be 
expressed by 
df 
Sb1 = SD (Eq. 67) 
Because a fractal is similar at different scales, the procedure for deriving Equation 67 from a 
grid-block with size L can be applied to shaded boxes with a smaller size l1. In this case, for a 
given box size smaller than l1, the number of shaded boxes will be an averaged number for those 
within the relatively large shaded boxes with a size of l1. Again, one can find a box size l2 < l1 to 
obtain a saturation relation: 
df .
. df .
. 
2 
Sb2 = (Sb1) D = S 
.
. D .
. (Eq. 68) 
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Analysis of Hydrologic Properties Data U0090 
The procedure to obtain Equation 68 can be continued until it reaches an iteration level n* at 
which all the shaded boxes with a size of ln cover active fractures only. The resultant average 
saturation for these shaded boxes is 
. df .n* 
.. 
S 
.
bn = (S )..
D . (Eq. 69) 
By definition of active fractures, Sbn should be equivalent to the effective saturation of active 
fractures. It is remarkable that Equation 69 is similar to Equation 52, obtained from a key 
hypothesis of the AFM that the fraction of active fractures in an unsaturated fracture network is a 
power function of the average effective saturation of the network. Comparing these two 
equations yields: 
.= 1 -
.
.
. dDf 
.
.
.n* 
(Eq. 70) 
Equation 70 provides the first theoretical relation between the parameter . and the fractal 
dimension for a fractal flow system, while .was initially developed as an empirical parameter 
(Liu et al. 1998 [105729]). Therefore, the AFM essentially captures fractional flow behavior at 
the subgridblock scale (df < D), whereas traditional continuum approaches assume a uniform 
flow pattern (or effective-saturation distribution) at that scale (corresponding to df = D or .= 0). 
In other words, the AFM can be used for simulating fractal flow behavior in an unsaturated 
fracture network that cannot be handled by the traditional continuum approach. 
Equation 70 implies that in the fractal flow model, . is not a constant, but a function of 
saturation, because both iteration level n* and df may be dependent on water saturation for a 
given fracture network. However, a constant .is a reasonable treatment at least for a limited 
range of water saturations (or flow conditions), which is the case for the Yucca Mountain UZ 
where fracture saturation is typically less than 10% under ambient conditions. It is not totally 
clear how .depends on the other hydraulic parameters for a large range of water saturations. 
Experimental evidence seems to indicate that .is a weak function of saturation (at least for 
porous media), which will be discussed below. It is obvious from the derivation of Equation 70 
that the fractal flow concept and Equation 70 can be applied to porous media also, as long as 
fingering flow patterns in them are fractals. Therefore, results from porous media can be used to 
conceptually evaluate the relation between . and water saturation for unsaturated fracture 
networks. 
Based on laboratory experimental observations collected by applying water at the top of the 
corresponding porous media, Wang et al. (1998 [155770], pp. 21882189) reported a relation 
between flow conditions and a parameter, F, defined as the ratio of horizontal cross-sectional 
area occupied by fingers to the total cross-sectional area. F corresponds to fa, defined as the 
portion of active fractures in a fracture network (Liu et al. 1998 [105729]). Wang et al. (1998 
[155770], pp. 21882189) related F to the ratio of average water flux through the whole cross-
sectional area, q, to saturated hydraulic conductivity of the porous medium, Ks, by 
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Analysis of Hydrologic Properties Data U0090 
. q . 1/ 2 
F  .. .. (Eq. 71)
. Ks . 
for q/Ks = 0.4  1.0. By definition, the average water flux within fingers (qF) can be related to q 
by 
q
qF = (Eq. 72)
F 
and the average water saturation of fingers, SF, can be related to the average water saturation for 
the whole cross-section area, Se, by 
SF = Se (Eq. 73)
F 
It is expected that flow within a gravitational finger is gravity dominated. In this case: 
qF = kr = SF 
 *(Eq. 74)
Ks 
Equation 74 uses the Brooks-Corey (Brooks and Corey 1964 [156915]) model for describing 
relative permeability (kr)saturation relationship. * is a constant. Combining Equation 71 to 74 
yields 
 * 
F = (Se )1+ * (Eq. 75) 
Comparing the above equation with Equation 51 (Liu et al. 1998 [105729], Equation 1) gives 
.= 
 * (Eq. 76)
1 + * 
Therefore, . is a constant under certain conditions in porous media. Consequently, it is expected 
that . should be a weak function of saturation for unsaturated fracture networks if fingering flow 
patterns in a porous medium are considered to be an analog of flow patterns in the networks. 
Note that Equation 76 cannot be directly used for estimating . values for fracture networks (in 
the AFM) because detailed flow mechanisms are different for unsaturated fractured rock and 
porous media. It also needs to be emphasized that Equation 76 is valid for porous media under a 
condition of q/Ks = 0.4 1.0 (Wang et al. 1998 [155770], pp. 21882189). The relation between . 
and other hydraulic properties has not been established for a fracture network. 
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Analysis of Hydrologic Properties Data U0090 
6.8 COMPARISON WITH A FILMFLOW MODEL 
Film flow on fracture surfaces may be an important mechanism for fast flow in unsaturated 
fractured rocks (Tokunaga and Wan 1997 [139195]), although the importance of the film flow in 
the UZ is still an issue of debate (Pruess 1999 [104250]). As an alternative conceptual model for 
unsaturated flow in fractures, a model assuming pure film flow within unsaturated fractures is 
developed to compare with the AFM. The major objective of this comparison is to demonstrate 
that fast flow behavior caused by film flow is already essentially captured by the current version 
of the AFM. 
6.8.1 Film Flow Model 
Tokunaga et al. (2000 [152914], pp. 17431744) both conceptually and experimentally 
demonstrated similarities between film flow on rough fracture surfaces and unsaturated flow in 
porous media. They also found that measured film thickness-potential relation data could be 
fitted very well by a power function, which may be related to unsaturated flow in porous media 
with fractal pore-size distributions (Tokunaga et al. 2000 [152914], p. 1743). 
Constitutive relationships for porous media with fractal pore-size distributions have been 
extensively studied. The most commonly used constitutive-relationship model for these porous 
media is the Brooks-Corey model, although this model was not initially developed based on 
fractal concepts (Brooks and Corey 1964 [156915]). The Brooks-Corey model (Liu and 
Bodvarsson 2001 [160110]) can be expressed by 
-.
= 
Pc / Pd 
(Eq. 77a)
Se 
2 
=tSe 
(1+ )
kr . (Eq. 77b) 
t= Se 
2 (Eq. 77c) 
where Pc (Pa) is the capillary pressure, Pd (Pa) is the air entry pressure, . (-) is a dimensionless 
index of pore size distribution, kr (-) is the relative permeability, t(-) is the tortuosity factor, and 
Se (-) is the effective saturation. Note that Equation 77a is valid when absolute values of Pc are 
larger that the absolute value of Pd. 
Film flow is closely related to fracture surface roughness, which has also been studied 
extensively (National Research Council 1996 [139151]). It has been found that the surface 
topographies for fractures could be represented in terms of fractal geometry. Although an 
equivalent pore-size distribution for a fracture surface has not been defined, it is conceptually 
reasonable to hypothesize that film flow on a fractal fracture surface is similar to an unsaturated 
water flow process in a porous medium with fractal pore-size distribution. Based on this 
hypothesis, the Brook-Corey model can be adopted to develop a constitutive-relationship model 
for film flow. Considering average film thickness, f (m), and film transmissivity, T (m2/s), to be 
analogues of effective water saturation and hydraulic conductivity (in the model of Brooks and 
Corey 1964 [156915]), respectively, for a porous medium, one can obtain 
MDL-NBS-HS-000014 REV00 74 April 2003 

Analysis of Hydrologic Properties Data U0090 
-. 
f 
Pc
= 
(Eq. 78a)
fref 
Pref 
2
1 
T = .
. f . (. *+ + )
. 
T
ref . fref 
.
.
. (Eq. 78b)
. 
. *
t. f (Eq. 78c) 
where fref ( m) and Tref (m2/s) are average film thickness and film transmissivity, respectively, 
for a reference capillary pressure Pref (Pa), and . * (-) is an empirical constant. Note that upper 
limits for f and T, unlike their counterparts for porous media, cannot be defined for unbounded 
fracture surfaces (Tokunaga et al. 2000 [152914]). Here, . is considered to be a parameter 
characterizing the geometry of the fracture surface. Since film flow is essentially two-
dimensional and pore-scale water flow in a porous medium is essentially three-dimensional, 
different tortuosity factors are expected for the two different cases. 
Experimental observations of Tokunaga and Wan (1997 [139195]) and Tokunaga et al. (2000 
[152914]) are used to verify the proposed constitutive-relationship model for film flow. 
Specifically, we examine whether Equation 78 could match the data. Note that the two data sets 
are measured for very different fracture surfaces, corresponding to a natural fracture surface of 
Bishop Tuff (Tokunaga and Wan 1997 [139195], p. 1298) and an artificially roughened surface 
(Tokunaga et al. 2000 [152914], p. 1740), respectively. Comparisons with these two data sets 
provide a unique opportunity to examine the validity (or robustness) of the proposed model 
under different conditions. 
Tokunaga et al. (2000 [152914], p. 1743, Figure 6) showed that the measured film thickness 
potential relation data could be fitted by a power function corresponding to . = 0.37 in Equation 
78a. The curve fitting is performed using Tecplot for all the relevant figures in this subsection. 
Figure 9 shows a match of Equation 78b with the surface transmissivityfilm thickness relation 
(Attachment I of this report) of Tokunaga et al. (2000 [152914]), which was derived from their 
measurements, indicating that the relation can be represented by a power function with a power 
value of 6.91. Using . = 0.37 and the power value of 6.91, we obtain . * = 0.50 from Equation 
78b. Figure 10 shows matches of Equations 78a and 78b with the data of Tokunaga and Wan 
(1997 [139195]), again indicating that the data are very well represented by power functions. The 
resultant . and . * values are 1.36 and -1, respectively. Different . values are obtained for the 
two data sets, because of the differences between the corresponding fracture surfaces, as 
previously mentioned. Considerably different . * values are also obtained for the two surfaces as 
a potential result of different ranges of average film thickness involved in the two data sets 
(Figures 9 and 10). Further examination of the relation from Tokunaga et al. (2000 [152914]) 
(Figure 9) seems to indicate that the it can be more accurately represented by different power 
functions for different thickness ranges. For example, Figure 11 shows a match of Equation 78b 
to the data of Tokunaga et al. (2000 [152914]) that exclude measurements below the thickness of 
2  m, as compared with the match in Figure 9. The resultant power value is 5.67, corresponding 
to . * = -0.74 (based on Equation 78b and . = 0.37 given by Tokunaga et al. (2000 [152914])). 
MDL-NBS-HS-000014 REV00 75 April 2003 

Analysis of Hydrologic Properties Data U0090 
This is generally consistent with .* = -1 for the data of Tokunaga and Wan (1997 [139195]) 
(Figure 10), considering that their data involve larger values of average film thickness than those 
reported by Tokunaga et al. (2000 [152914]). 
Theoretically, .* should be positive because a tortuosity factor should be smaller than one 
(Brooks and Corey 1964 [156915]). The negative .* value results from the empirical nature of 
Equations 79 and 78. A similar situation was also reported for several unsaturated porous media 
(Schaap and Leij 2000 [160841]). Based on the above analyses, .* = -1 seems to be reasonable 
for film flow with an average film thickness larger than 2 m. In summary, while the proposed 
model may need to be further evaluated using more data sets, the excellent agreement between 
calculation results and experimental observations of Tokunaga and Wan (1997 [139195]) and 
Tokunaga et al. (2000 [152914]) supports the usefulness of the model for describing film flow in 
unsaturated fractures. 
10
-11 
10
-12 
10
-13 
10
-14 
10
-15 
Surface Transmissivity (m2/s) 
1 2 345 
Average Film Thickness (micrometer) 
NOTE: Squares correspond to the surface transmissivity-film thickness relation (Attachment I) obtained from 
Figure 9 of Tokunaga et al. (2000 [152914]) 
Figure 9. Match of Equation 78b to Surface Transmissivity-Film Thickness Measurements 
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Analysis of Hydrologic Properties Data U0090 
(a) 
100 101 102 103 
100 
101 
102 
()Average Film Thickness micrometer
-Capillary Potential (Pa) 
(b) 
/s)
10
-8 
(m2
Surface Transm
10
-10 
10
-11 
100 
101 
102 
103 
-Capillary Potential (Pa) 
NOTE: Squares correspond to the measurements (Attachment I). (a) potential measurements given in Figure 7 of 
Tokunaga and Wan (1997 [139195]); (b) potential measurements given in Figure 6 of Tokunaga and Wan 
(1997 [139195]). 
Figure 10. Matches of Equation 78 to (a) Film Thickness and (b) Surface Transmissivity-Film Thickness 
MDL-NBS-HS-000014 REV00 77 April 2003 
issivity 
10
-9 

Analysis of Hydrologic Properties Data U0090 
10
-12 
10
-13 
10
-14 
10
-15 
10
-16 
1 2 345 
Average Film Thickness (micrometer) 
NOTE: Squares correspond to the surface transmissivity-film thickness relation (Attachment I) obtained from 
Figure 9 of Tokunaga et al. (2000 [152914]) that exclude points below the thickness of 2  m, as compared 
with the match in Figure 9. 
Figure 11. Match of Equation 78b to Surface Transmissivity-Film Thickness Measurements 
Assuming that film flow only occurs in unsaturated fractures, the fractures would become 
saturated when the average film thickness is equal to half the average aperture. In this case, the 
air entry value Pd for fractures can be used as the reference capillary pressure. As a result, film 
thickness ratio and transmissivity ratio in Equation 78 are equivalent to the effective saturation 
(Se) and relative permeability (kr), respectively, for fractures. Equation 78 with . = -1 is then 
rewritten as 
-.
Pc
= 
(Eq. 79a)
Se Pd 
kr = Se 
2/ . 
(Eq. 79b) 
Note that for the same . value, the ratio of relative permeability from the Brooks-Corey model 
(Equation 77) to that from Equation 79b is Se3, indicating that Equation 79b predicts much higher 
kr value than the Brooks-Corey model at low saturations. That also explains why film flow 
corresponds to fast flow in unsaturated fractures. Equation 79 can be used to describe film flow 
within the context of the continuum approach, if all fractures are assumed to be hydraulically the 
same within a gridblock. Note that effects of variability in fracture aperture are not considered in 
10
-11 
Surface Transmissivity (m2/s) 
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Analysis of Hydrologic Properties Data U0090 
Equation 79. This issue may need further investigation in the future if the film flow is 
determined to be a key flow mechanism. 
6.8.2 Model Comparison 
This section provides a simple comparison between the film flow model (Equation 79) and the 
AFM. In welded units, water mainly percolates through fractures (Liu et al. 1998 [105729], 
Figure 12). For a given infiltration flux, pore velocity within fractures in a welded unit is 
proportional to the relative permeability, divided by the effective saturation, because fracture 
flow is gravity dominant (Liu et al. 1998 [105729]). Therefore, for a given relative permeability, 
it will be determined whether the effective saturation predicted by the film model, using 
parameters derived from observations by Tokunaga and Wan (1997 [139195]) and Tokunaga et 
al. (2000 [152914]), is approximately within the range of saturations calculated by the AFM for a 
range of . values. If it is the case, pore velocity determined from the film-flow model should be 
approximately within the range of pore velocity predicted by the AFM. In other words, the fast 
flow behavior caused by fracture film flow is captured by the AFM. 
10
-6 
10
-7 
10
-8 
10-5 
10-4 
10-3ve permeabilambda=0.37 
lambda=1.36 
gamma = 0 
0.4 
0.8 
1.0 
0.2 0.4 0.6 0.8 1 
Se 
10
100 
-1 
10
-2 
lity
Relati
Source: From experimental results of Tokunaga and Wan (1997 [139195]) and Tokunaga et al. (2000 [152914]). 
Figure 12. Comparison Between Relative Permeability-Saturation Relations Calculated Using the AFM 
with m = 0.633 (Table 7) for Several . Values (Solid Lines) and Relations Calculated from the 
Film-Flow Model with . Values (Dashed Lines) 
Figure 12 shows a comparison between relative permeability-saturation relations calculated with 
the AFM and the film-flow model. For . = 0.37, even the AFM with .= 0 (i.e., all connected 
fractures are active) can predict larger pore velocity at low saturations. For . = 1.36, the relative 
permeability-saturation relation given by the film-flow model is consistent with the AFM with a 
. value between 0.8 and 1.0. Therefore, fast flow predicted by the film-flow model with 
parameters obtained from observations by Tokunaga and Wan (1997 [139195]) and Tokunaga et 
MDL-NBS-HS-000014 REV00 79 April 2003 

Analysis of Hydrologic Properties Data U0090 
al. (2000 [152914]) can be captured by the AFM with different . values. This largely results 
from the capability of the AFM to capture a large range of flow behaviors. 
Although a typical . value for the UZ is not available, the typical value is quite likely already 
bound by the two .values used in Figure 12. This is based on the observation that water flow 
processes in the UZ are bound to those described by the AFM with .values between 0 and 0.8, 
while relative permeability-saturation relations with this range of . are bounded by the two 
relations determined with the two . values (Figure 12). As indicated by Liu et al. (1998 
[105729]), field observations support the concept that not all connected fractures are active in the 
UZ (. >0). On the other hand, Liu et al. (1998 [105729], Figure 11) also showed that pore 
velocity within fractures in welded units is on the order of 1.0E-4 m/s (or 3154 m/yr) for .=0.8. 
No evidence supports such a high pore velocity in the UZ. As a result, if film flow were indeed 
important in the UZ, the corresponding relative permeability-saturation relation would be bound 
by the relations for the two .values shown in Figure 11. Consequently, the above comparison is 
likely valid for the Yucca Mountain UZ. 
Note also that derivation of Equation 79 implies a uniform film flow in all fractures. This is 
supported by the widespread nature of the film flow observed from small-scale laboratory 
experiments (Tokunaga and Wan 1997 [139195]; Tokunaga et al. 2000 [152914]). This uniform 
film-flow generally results in a much larger degree of matrix diffusion (resulting from a larger 
fracture-matrix interfacial area) than with the AFM, and therefore a larger degree of retardation 
for radionuclide transport in the UZ, even when pore velocity is the same for the two models. All 
these considerations support the idea that AFM does not overestimate the performance of the 
UZ, even if film flow were the main mechanism for fracture flow in the UZ. 
6.9 UNCERTAINTY, ALTERNATIVE MODELS (APPROACHES) AND OTHER 
ISSUES 
This report has two major objectives. The first objective is to determine uncalibrated properties 
for UZ model layers (Sections 6.1  6.4). Uncertainties of most of these properties are reported 
using the corresponding standard deviations or standard errors (e.g., Tables 6  8). Alternative 
approaches or alternative values from different sources have been used to verify the estimated 
properties (Sections 6.1.3.4, 6.2.4 and 6.5). As indicated in Section 1, these properties are 
uncalibrated and serve only as initial estimates in the Calibrated Properties Model. The calibrated 
properties are intended for use in the mountain-scale and drift-scale UZ models 
The second objective is to evaluate the AFM presented in Section 6.6. The uncertainties of the 
AFM result from the representation of fracture systems with continua, the validity of the relevant 
hypotheses and accuracy of the estimated AFM parameters. The model validation activities 
(including determination of reasonable range of AFM parameter .) are reported in Section 7. 
Alternative model (film flow model and fractal-based model) and its comparison with the AFM 
are also included in Section 6.8. 
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Analysis of Hydrologic Properties Data U0090 
7. VALIDATION
This section documents activities to validate the AFM and the corresponding model validation 
results. 
7.1 MODEL VALIDATION ACTIVITIES 
The AFM is a modification of the traditional dual-continuum model that specifies a constitutive 
relationship for unsaturated flow in fractures. This modification is motivated by field 
observations showing that under unsaturated conditions, not all fractures in a connected fracture 
network actually carry water flow (Liu et al. 1998 [105729], CRWMS M&O 2000 [141187], 
Section 6.4). The AFM has been reviewed and published by Liu et al. (1998 [105729]), and 
several papers describing simulation results from the UZ Model based on the AFM have been 
accepted for publication by Journal of Contaminant Hydrology, an international journal 
publishing scientific articles pertaining to the contamination of groundwater. Section 6.7 shows 
that the AFM is approximately consistent with a fractal-distribution behavior for liquid water 
(characterized by a fractal dimension) in a fracture network. The fractal flow behavior has been 
often reported in the literature for unsaturated flow and multiphase flow systems. The fractal 
analysis is used to analyze the fracture coating data from UZ, indicating that the UZ may also 
exhibit fractal flow behavior (Section 6.7). The validation of the AFM with field observations 
from the Yucca Mountain UZ, including fracture coating data and carbon-14 data, will be 
documented in this section. The criteria of model validation are given in the TWP (BSC 2002 
[160819], Section I-1-3-1). The criteria for validation using carbon-14 data and fracture coating 
data are (a) that simulated water travel times are sensitive to AFM parameters and within the 
range of measured data for the TSw unit, and (b) that the simulated active portion of the fractures 
is similar to the percentage of fractures with mineral coatings, or (c) that other factors, not 
directly related to the AFM, can explain any significant disparity between simulations and the 
observed results. These criteria allow for validating the AFM by showing that model results 
based on the AFM are consistent with observations when the relevant results are sensitive to the 
AFM parameters. As will be shown in Section 7.2, these criteria ((a) and (b)) are met for the 
suitable AFM parameter values. In numerical simulations to be reported in this section, codes 
TOUGH2 V1.4 (LBNL 2000 [146496]) and T2R3D V1.4 (LBNL 1999 [146654]) are used. This 
is because these codes have been comprehensively tested and widely used for modeling UZ flow 
and transport. These codes also include the AFM features. The upstreaming weighting approach 
is used for modeling flow processes. The reasonableness of the approach was discussed in BSC 
(2003 [160240], Section 6). 
7.2 VALIDATION OF THE ACTIVE FRACTURE MODEL WITH CARBON-14 AND 
MINERAL COATING DATA 
Carbon-14 and fracture coating data are used for validating the AFM. They contain useful 
information regarding water flow and fracture-matrix interaction in the UZ under ambient 
conditions. It is especially of interest to examine if the AFM can represent two different data sets 
for a similar range of the AFM parameter .. This model validation activity is documented in 
Scientific Notebooks (Wang 2003 [161654], SN-LBNL-SCI-227-V1, pp. 819, 2154, 7071, 
7384; SN-LBNL-SCI-199-V1, pp. 9299). 
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Analysis of Hydrologic Properties Data U0090 
7.2.1 Model validation with Carbon-14 Data 
Carbon-14 data were collected from perched water, pore water, and gas samples from the Yucca 
Mountain UZ (BSC 2002 [160247], Section 6.6.4). Pore-water Carbon-14 data from various 
boreholes at Yucca Mountain were affected by contamination from atmospheric 14CO2 during 
drilling, which may result in apparently younger residence times (Yang 2002 [160839], Section 
4.1.2; BSC 2002 [160247], Section 6.6.4.2). Carbon-14 data from gas samples are considered to 
be most representative of in situ conditions (Yang 2002 [160839], Section 4.1.2). Gas samples 
were collected from different kinds of boreholes including open surface-based boreholes and 
instrumented surface boreholes. The data from the latter boreholes (USW SD-12 and USW UZ1) 
are the most reliable indicators of in situ conditions (BSC 2002 [160247], Section 6.6.4.3). 
Carbon-14 residence ages (BSC 2002 [160247], Table 20) calculated using the data from these 
two boreholes are used for validating the AFM. Water travel times from the ground surface to 
the perched water bodies are dominated by PTn where flow occurs mainly in the rock matrix and 
is thus insensitive to the AFM parameters (Wang 2003 [161654], SN-LBNL-SCI-227-V1, pp. 
4950). Therefore, carbon-14 data collected from perched water are not used for validating the 
AFM. 
Gas-phase carbon-14 ages (DTN: GS961108312271.002 [121708] for borehole USW SD-12 and 
MO0012CARB1314.000 [153398] for borehole USW UZ-1) are interpreted to be representative 
of ages of the in situ pore water. The rationales for this interpretation are provided by Yang 
(2002 [160839], Section 4.1.2). This interpretation presumes rapid exchange of gas-phase CO2 
(in hours to days) with dissolved CO2 and (HCO3-) in pore water. Furthermore, the amount of C 
in an aqueous-phase pore water relative to C in the CO2 gas-phase reservoir is a hundred times 
greater. Consequently, the aqueous phase will dominate the gaseous phase when exchange 
occurs, indicating the reasonableness of the interpretation (Yang 2002 [160839], Section 4.1.2). 
One-dimensional numerical models are developed for boreholes USW SD-12 and USW UZ-1. 
Numerical grids for these models are taken from DTN: LB02091DSSCP3I.001 [161292]. The 
measurement elevations are determined from collar elevations of the boreholes (DTN: 
MO9906GPS98410.000 [109059]) and depth information given in the DTNs in the above 
paragraph. The calibrated rock properties for present-day, mean infiltration maps are used except 
. values (DTN: LB02091DSSCP3I.002 [161433]). Value of the AFM parameter .for model 
layers tsw32 to tsw38 is varied for different simulations to check the sensitivity of this parameter 
to simulated water travel times. Top boundary condition corresponds to the present-day 
infiltration rate for flow simulations and a constant tracer concentration for transport simulations. 
Initial conditions for solute transport include zero concentration within the fractured rocks. 
Previous studies indicate that dispersion processes have an insignificant effect on overall solute 
transport behavior in unsaturated fractured rocks (BSC 2001 [158726], Section 6.8.1), and 
therefore they are ignored here. An effective-diffusion-coefficient value of 1.97E-10 m2/s is 
employed in this study and equal to the average value of measured coefficients for tritiated water 
(DTN: LA000000000034.002 [148603]; BSC 2002 [160828], Table 16). TOUGH2 V1.4 (LBNL 
2000 [146496]) and T2R3D V1.4 (LBNL 1999 [146654]) codes (Table 1) are used for 
simulating steady-state water flow and tracer transport processes. (The input and output files 
associated with these software items are described in Scientific Notebook (Wang 2003 [161654], 
SN-LBNL-SCI-227-V1, pp. 819, 2154, 7071, and 7384) and submitted to Technical Data 
Management System (TDMS) under Output DTNs: LB0212C14INFIL.001 and 
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Analysis of Hydrologic Properties Data U0090 
LB0212AFPGAMMA.001.) Simulated water travel times (or ages) for rock matrix are compared 
with carbon-14 ages. A simulated water travel time at a location is determined as the time when 
the matrix concentration reaches 50% of the top-boundary concentration. It represents the 
average travel time for water particles from the ground surface to the location. 
Figure 13 shows simulated water travel times (ages) for different .values of UZ Model layers 
tsw32 to tsw38. The considerable sensitivity of simulated results to . indicates that Carbon-14 
data are useful for validating the AFM and for constraining the . values for the TSw unit. For . 
values ranging from 0.2 to 0.4, simulated results approximately match the observations. A larger 
. value generally corresponds to a larger travel time for the matrix because of a smaller degree of 
matrix diffusion, resulting from a smaller fracture-matrix interfacial area available for mass 
transport between fractures and the matrix. 
Simulated water travel times change sharply at an elevation of about 1,100 m for two boreholes 
(Figure 13). This is because the upper portion of the TSw unit has relatively small fracture 
density values and thus corresponds to a smaller degree of matrix diffusion for a given . value 
(Table 7). For the borehole USW UZ-1, simulated water travel time is generally longer than the 
observation for a given elevation. This may be a result of subsurface heterogeneity, which gives 
larger fracture densities (resulting in a larger degree of matrix diffusion) at the borehole location 
than what are used in the numerical model. Layer-averaged fracture properties are used in the UZ 
Model (Section 5). Also note that unlike the case for USW SD-12, the simulation result for .=0 
provides the best match to the data for USW UZ-1. This may again result from the spatial 
variability. Like many other hydraulic properties (e.g., permeability), . is spatially variable. 
Nevertheless, .=0.20.4 gives the reasonable matches to the data from the two boreholes 
simultaneously. 
In summary, a comparison between simulated water travel times and observed carbon-14 ages 
indicates that the AFM with . values between 0.2 to 0.4 (for UZ model layers tsw32-tsw38) can 
reasonably represent the data. 
MDL-NBS-HS-000014 REV00 83 April 2003 

Analysis of Hydrologic Properties Data U0090 
(a) 
800 
1000 
1200 
1400 
ElUZ-1 
evation(m) 
PTn 
TSw 
CHn 
Measured 
gamma=0 
gamma=0.1 
gamma=0.2 
gamma=0.3 
gamma=0.4 
gamma=0.5 
gamma=0.6 
100 1000 10000 100000 1000000
Age(Yrs)
(b) 
800 El) 
900 
1000 
1100 
1200 
1300 
evation(mSD-12 
TCw 
PTn 
TSw 
CHn 
Measured 
gamma=0 
gamma=0.1 
gamma=0.2 
gamma=0.3 
gamma=0.4 
gamma=0.5 
gamma=0.6 
100 1000 10000 100000 1000000
Age(Yrs)
Output - DTN: LB0212C14INFIL.002 (files: C14_UZ-1_age.doc and C14_SD-12_age.doc) 
Source: Wang (2003 [161654], SN-LBNL-SCI-227-V1, p. 87) 
Figure 13. Comparisons Between Simulated Water Travel Times (Ages) for Rock Matrix at Boreholes 
(a) USW UZ-1 and (b) USW SD-12, as well as the Corresponding Carbon-14 Ages for 
Several .Values 
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Analysis of Hydrologic Properties Data U0090 
7.2.2 Model Validation with Mineral Coating Data 
The process of UZ mineral deposition is initiated during infiltration. Here, meteoric water 
interacts with materials in the soil, after which a portion may then enter the bedrock fracture 
network. Fracture coating is generally a signature of water flow paths. Therefore, the coating 
data are useful for validating the AFM that describes water flow in fractures. 
Fracture coating data were collected in the ESF (DTN: GS980308315215.008 [107355]). 
Observed spatial distribution of fractures with coatings is used to estimate the portion of active 
fractures in the UZ. For a given survey interval along ESF, a frequency of coated fractures can 
be estimated for a geologic unit, based on the total number of coated fractures. The ratio of 
coated-fracture frequency to total fracture frequency (Table 7) will provide an estimate of the 
portion of active fracture for the given geologic unit (Wang 2003 [161654], SN-LBNL-SCI-227-
V1, pp. 7071). The estimated average portion of the active fracture for the TSw is 7.2%. The 
abundance of mineral coating (coating volume divided by total rock volume), divided by the 
corresponding fracture porosity, gives another estimate of the portion of the active fractures in 
the UZ under ambient conditions. Abundance data for all intervals in welded tuffs have an 
arithmetic mean of 0.084% (BSC 2002 [160247], Section 6.10.1.1), while a typical fracture 
porosity is 1% (Table 7). Therefore, an estimate of the average portion of active fractures for 
welded units is %084.0%1 = 8.4%, close to the estimate determined from frequency of coated 
fractures. Note that fracture coatings may not precisely represent active flow paths in the UZ and 
some flow paths may not have coatings (Liu et al. 1998 [105729]). Nevertheless, these values at 
least give a rough estimate of lower limits for the portion of active fractures in the UZ  about 
10%. 
Mineral growth rate data imply that the UZ fracture network has maintained a large degree of 
hydrologic stability over time, and fracture flow paths in the deep unsaturated zone are buffered 
from climate-induced variations in precipitation and infiltration (BSC 2002 [160247], Section 
6.10.3.9). If the AFM actually represents water flow processes in the UZ, modeling results based 
on the AFM should be consistent with this important observation. 
To check the consistency of the AFM against the coating data, a one-dimensional model for 
borehole USW SD-12 is used. The model is the same as that described in Section 7.2.1. USW 
SD-12 is chosen because it is located near the middle of the ESF, where coating data were 
collected. Two infiltration rates, present data mean infiltration rate and glacial maximum 
infiltration rate, are used for simulations. Again, uniform . distributions within model layers 
tsw32 to tsw38 are employed. The latter infiltration rate is about five times as large as the former 
rate and represents the maximum infiltration rate in past climates. 
MDL-NBS-HS-000014 REV00 85 April 2003 

Analysis of Hydrologic Properties Data U0090 
fa (active fracture portion)
1.0E+1 
1.0E+0 
1.0E-1 
1.0E-2 
1.0E-3 
1.0E-4 
Present day mean infiltration rate 
Glacial maximum infiltration rate 
0.00 0.20 0.40 0.60 0.80 1.00 
Gamma 
Output - DTN: LB0212AFPGAMMA.002 (files: gamma_analysis.doc, FA_GA_GU.dat and FA_GA_PM.dat) 
Figure 14. Simulated Average Portion of Active Fracture for the Relevant Model Layers (tsw32 to tsw38) 
as a Function of Infiltration Rate and . 
Figure 14 shows the simulated average portion of active fractures, fa, for the relevant model 
layers (tsw32 to tsw38) as a function of infiltration rate and .. The average portion is calculated 
from Equation 52 using the average effective saturation for model layers tsw32 to tsw38. The 
calculated fa values range about 10% for . values close to 0.4, which are similar to those used for 
matching the carbon-14 data. For the same range of .values, the calculated fa values do not 
change significantly for the two infiltration rates (Figure 14), which is consistent with the 
observation of flow-path stability over time. The present day mean infiltration rate is obtained 
from file GENER_m_rad_200 in DTN: LB0208UZDSCPMI.001 [161285]. The glacial 
maximum infiltration rate is determined using software routine infil2grid V1.7 [154793] based 
on infiltration maps (USGS 2001 [160355]) (DTN: GS000308311221.005 [147613]) and UZ 
numerical grid (DTN: LB03023DKMGRID.001 [162354]) (Wang 2003 [161654], SN-LBNL-
SCI-199-V1, pp. 9299; SN-LBNL-SCI-227-V1, pp. 89). The determined glacial maximum 
infiltration rate is 17.3108 mm/yr (Wang 2003 [161654], SN-LBNL-SCI-227-V1, p. 8). 
In summary, the simulation results based on the AFM are consistent with both carbon-14 data 
and fracture coating data, for a similar range of . values. This result, together with the 
consistency of the AFM with fractal flow patterns (Section 6.7), supports the validity of the 
AFM. No further activities are needed to complete this model validation for its intended use. 
MDL-NBS-HS-000014 REV00 86 April 2003 

Analysis of Hydrologic Properties Data U0090 
8. CONCLUSIONS
Methodologies have been described for providing representative estimates of fracture and matrix 
properties for UZ Model layers, based on the relevant data. The fracture and matrix properties 
developed here were submitted to the TDMS under Output-DTNs: LB0205REVUZPRP.001 and 
LB0207REVUZPRP.002, respectively. Thermal properties developed here were submitted to the 
TDMS under Output-DTN: LB0210THRMLPRP.001. Fault properties developed here were 
submitted to the TDMS under Output-DTN: LB0207REVUZPRP.001. Estimated properties are 
also documented in this report for use as prior information in the inversion processes in a 
separate Model Report documenting the Calibrated Properties Model (BSC 2003 [160240]). The 
resultant fracture geometry properties are important inputs for the development of the UZ Model 
grids. The independent determination of fracture properties, based on ESF seepage test results, 
confirms the appropriateness of the estimated fracture properties and the procedures used for the 
estimation. The summarized simulation results for the AFM validation were also submitted to the 
TDMS under Output-DTNs: LB0212AFPGAMMA.002 and LB0212C14INFIL.002. 
Like many field-scale problems, data availability and limitations in approaches for upscaling 
flow parameters directly from small-scale measurements are major sources of uncertainties in the 
estimated hydraulic properties. It is particularly true for the unsaturated fractured rocks, due to 
the complexity of the flow processes involved. To reduce the uncertainties, model calibrations 
are generally needed. Calibration is discussed in a separate Model Report describing the 
Calibrated Properties Model (BSC 2003 [160240]). Therefore, it should be emphasized that flow 
parameter estimates reported herein are only developed as inputs into model calibrations, and 
should not be directly used for modeling UZ flow and transport processes without careful 
evaluation. The calibrated properties should be used for the relevant modeling studies. 
This report also documents activities to validate the AFM and the corresponding model 
validation results. The AFM is evaluated on the basis of (fractal) flow patterns in unsaturated 
systems, the consistency with different types of field observations, and a comparison with an 
alternative (film-flow) model. The AFM is shown to be consistent with fractal distributions of 
flow patterns that have been often observed from unsaturated or multiphase flow systems 
(porous media). Simulation results based on the AFM, with a similar range of AFM-parameter 
values, match both the carbon-14 ages and fracture coating data collected from the UZ. 
Furthermore, a simple comparison between the AFM and the film-flow model, an alternative 
model, shows that the AFM would capture the fast flow behavior of water film if film flow were 
the major mechanism for fracture flow. All these support the validity of the AFM in describing 
flow and transport processes in the UZ. 
In addition to the Output-DTNs mentioned above, the output DTNs containing support files for 
the AFM validation study (LB0212C14INFIL.001 and LB0212AFPGAMMA.001) were also 
submitted for TDMS. All Output-DTNs from this report are listed in Section 9.4. 
MDL-NBS-HS-000014 REV00 87 April 2003 

Analysis of Hydrologic Properties Data U0090 
INTENTIONALLY LEFT BLANK
MDL-NBS-HS-000014 REV00 88 April 2003 

Analysis of Hydrologic Properties Data U0090 
9. INPUTS AND REFERENCES
The following is a list of the references cited in this document. Column 1 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 [155950]). The purpose of 
these numbers is to assist the reader in locating a specific reference. Within the reference list, 
multiple sources by the same author (e.g., BSC 2002) are sorted alphabetically by title. 
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correspondence from J.S. Wang (BSC) to File, February 28, 2003, with attachments. 
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235461. 
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Analysis of Hydrologic Properties Data U0090 
160839 Yang, I.C. 2002. Percolation Flux and Transport Velocity in the Unsaturated Zone, 
Yucca Mountain, Nevada. Applied Geochemistry, 17, ([6]), 807-817. [New York, 
New York]: Elsevier. TIC: 253605. 
Software Cited 
134754 LBNL (Lawrence Berkeley National Laboratory) 1999. Software Code: infil2grid. 
V1.6. PC with Windows/95 or 98. Sun or DEC Workstation with Unix OS. 10077-
1.6-00. 
146654 LBNL (Lawrence Berkeley National Laboratory) 1999. Software Code: T2R3D. 
V1.4. FORTRAN 77, SUN, DEC / ALPHA. 10006-1.4-00. 
146496 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Code: TOUGH2. 
V1.4. Sun Workstation and DEC/ALPHA. 10007-1.4-01. 
154793 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: infil2grid. 
V1.7. DEC-Alpha, PC. 10077-1.7-00. 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
156605 10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic 
Repository at Yucca Mountain, Nevada. Readily available. 
AP-2.22Q, Rev. 0. Classification Criteria and Maintenance of the Monitored Geologic 
Repository Q-List. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: MOL.20020314.0046. 
AP-3.15Q, Rev. 3, ICN 4. Managing Technical Product Inputs. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20021105.0163. 
AP-SI.1Q, Rev. 4. Software Management. Washington, D.C.: U.S. Department of Energy, 
Office of Civilian Radioactive Waste Management. ACC: MOL.20030113.0149. 
AP-SV.1Q, Rev. 0, ICN 3. Control of the Electronic Management of Information. Washington, 
D.C.: U.S. Department of Energy, Office of Civilian Radioactive Waste 
Management. ACC: MOL.20020917.0133. 
YMP-LBNL-QIP-SV.0 Rev. 2, Mod. 1. Management of YMP-LBNL Electronic Data. Berkeley, 
California: Lawrence Berkeley National Laboratory. ACC: MOL.20020717.0323. 
9.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
147613 GS000308311221.005. Net Infiltration Modeling Results for 3 Climate Scenarios for 
FY99. Submittal date: 03/01/2000. 
160822 GS010608312242.001. Unsaturated Hydraulic Conductivity and Matric Potential in 
Busted Butte Volcanic Tuff Cores. Submittal date: 08/07/2001. 
MDL-NBS-HS-000014 REV00 95 April 2003 

Analysis of Hydrologic Properties Data U0090 
155533 GS940208314211.007. Table of Contacts in Borehole USW UZ-N35. Submittal 
date: 02/10/1994. 
145581 GS940208314211.008. Table of Contacts in Boreholes USW UZ-N57, UZ-N58, UZN59, 
and UZ-N61. Submittal date: 02/10/1994. 
145589 GS940308314211.018. Table of Contacts for the Tiva Canyon Tuff in Borehole 
USW UZ-N36. Submittal date: 03/28/1994. 
152558 GS950108314211.008. Lithostratigraphic Data for Paintbrush Group Bedded Tuff 
Units TPBT3 and TPBT4 in Boreholes USW UZ-N11, USW UZ-14, USW NRG-
7/7A, USW SD-9, USW UZ-N37, USW NRG-6, UE-25 NRG#2B, USW UZ-N31, 
USW UZ-N32, USW SD-12, UE-25 UZ#16, USW UZ-N54, USW UZ-N53. 
Submittal date: 01/20/1995. 
152556 GS950108314211.009. Stratigraphic Descriptions and Data for the Yucca Mountain 
Tuff in Boreholes NRG#2B, NRG-7/7A, SD-9, UZ-14, UZ#16, UZ-N11, UZ-N33, 
UZ-N34, UZ-N53, UZ-N54, UZ-N55. Submittal date: 01/27/1995. 
144662 GS950608312231.008. Moisture Retention Data from Boreholes USW UZ-N27 and 
UE-25 UZ#16. Submittal date: 06/06/1995. 
160827 GS950708314211.028. Stratigraphic Descriptions of the Pah Canyon Tuff in 
Boreholes UE-25 NRG #2B, UE-25 NRG#4, USW NRG-6, USW NRG-7/7A, USW 
SD-9, USW SD-12, USW UZ-14, USW UZ-N31, USW UZ-N32, AND USW UZN37. 
Submittal date: 07/20/1995. 
147590 GS960808312231.003. Moisture Retention Data for Samples from Boreholes USW 
SD-7, USW SD-9, USW SD-12 and UE-25 UZ#16. Submittal date: 08/30/1996. 
105574 GS960908312232.013. Air-Injection Testing in Vertical Boreholes in Welded and 
Non-Welded Tuff, Yucca Mountain, Nevada. Submittal date: 09/26/1996. 
121708 GS961108312271.002. Chemical and Isotopic Composition of Pore Water and Pore 
Gas, 199496, from Boreholes USW UZ-1, USW UZ-14, UE-25 UZ#16, USW NRG6, 
USW NRG-7A, USW SD-7, USW SD-9, ESF-AL#3-RBT#1, and ESF-AL#3-
RBT#4, and ESF Rubble. Submittal date: 12/04/1996. 
105580 GS970183122410.001. Results from Air-Injection and Tracer Testing in the Upper 
Tiva Canyon, Bow Ridge Fault, and Upper Paintbrush Contact Alcoves of the 
Exploratory Studies Facility, August 1994 through July 1996, Yucca Mountain, 
Nevada. Submittal date: 02/03/1997. 
107184 GS971008312231.006. Physical Properties and Saturated Hydraulic Conductivity of 
Cores from Surface Samples from the ESF Main Drift 29+00 M to 57+00 M. 
Submittal date: 10/06/1997. 
MDL-NBS-HS-000014 REV00 96 April 2003 

Analysis of Hydrologic Properties Data U0090 
107165 GS980308312242.005. Physical Properties of Lexan-Sealed Borehole Samples from 
the PTN Exposure in the ESF North Ramp (ESF Station 7+27 M to ESF Station 
10+70 M). Submittal date: 03/11/1998. 
107355 GS980308315215.008. Line Survey Information from the Exploratory Studies 
Facility Obtained to Estimate Secondary Mineral Abundance. Submittal date: 
03/24/1998. 
107161 GS980408312242.008. Unsaturated Hydraulic Properties of Borehole Samples from 
the PTN Exposure in the ESF North Ramp (ESF Station 7+27 M to ESF Station 
10+70 M) Measured Using a Centrifuge. Submittal date: 04/17/1998. 
106752 GS980708312242.010. Physical Properties of Borehole Core Samples, and Water 
Potential Measurements Using the Filter Paper Technique, for Borehole Samples 
from USW WT-24. Submittal date: 07/27/1998. 
107150 GS980708312242.011. Physical Properties and Hydraulic Conductivity 
Measurements of Lexan-Sealed Samples from USW WT-24. Submittal date: 
07/30/1998. 
149375 GS980808312242.012. Unsaturated Hydraulic Properties of Lexan-Sealed Samples 
From USW WT-24, Measured Using a Centrifuge. Submittal date: 08/05/1998. 
106748 GS980808312242.014. Physical Properties of Borehole Core Samples and Water 
Potential Measurements Using the Filter Paper Technique for Borehole Samples from 
USW SD-6. Submittal date: 08/11/1998. 
107180 GS980908312242.037. Water Retention Data of Lexan-Sealed Borehole Samples 
and Surface Samples from ESF North Ramp Moisture Study. Submittal date: 
09/23/1998. 
107154 GS980908312242.038. Physical Properties and Saturated Hydraulic Conductivity 
Measurements of Lexan-Sealed Samples from USW SD-6. Submittal date: 
09/22/1998. 
145272 GS980908312242.039. Unsaturated Water Retention Data for Lexan-Sealed Samples 
from USW SD-6 Measured Using a Centrifuge. Submittal date: 09/22/1998. 
107169 GS980908312242.040. Physical Properties and Saturated Hydraulic Conductivity 
Measurements of Core Plugs from Lexan-Sealed Samples from Boreholes in the ESF 
North Ramp. Submittal date: 09/24/1998. 
107158 GS980908312242.041. Physical Properties and Saturated Hydraulic Conductivity 
Measurements of Core Plugs from Boreholes USW SD-7, USW SD-9, USW SD-12, 
USW UZ-14, and UE-25 UZ#16. Submittal date: 09/24/1998. 
107185 GS990308312242.007. Laboratory and Centrifuge Measurements of Physical and 
Hydraulic Properties of Core Samples from Busted Butte Boreholes UZTT-BB-INJ-1, 
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Analysis of Hydrologic Properties Data U0090 
UZTT-BB-INJ-3, UZTT-BB-INJ-4, UZTT-BB-INJ-6, UZTT-BB-COL-5 and UZTT-
BB-COL-8. Submittal date: 03/22/1999. 
109822 GS990708312242.008. Physical and Hydraulic Properties of Core Samples from 
Busted Butte Boreholes. Submittal date: 07/01/1999. 
135230 GS990883122410.002. Qualified Data in Results from Geothermal Logging, Air 
and Core-Water Chemistry Sampling, Air-Injection Testing and Tracer Testing in the 
Northern Ghost Dance Fault, November, 1996 - August, 1998. Submittal date: 
08/16/1999. 
148603 LA000000000034.002. Diffusion of Sorbing and Non-Sorbing Radionuclides. 
Submittal date: 06/22/1993. 
160824 LA0207SL831372.001. Lithostratigraphic Classification of Hydrologic-Property 
Core-Sampling Depths, Busted Butte Phase 2 Test Block. Submittal date: 
07/16/2002. 
160825 LAJF831222AQ98.014. Chloride, Bromide, and Sulfate Analyses of Salts Leached 
from ESF-NR-Moiststdy Drillcore. Submittal date: 09/09/1998. 
156907 LB0110LIQR0015.001. Developed Data for Liquid Release/Seepage Tests and 
Systematic Testing. Submittal date: 11/12/2001. 
161285 LB0208UZDSCPMI.001. Drift-Scale Calibrated Property Sets: Mean Infiltration 
Supporting Files. Submittal date: 08/27/2002. 
161292 LB02091DSSCP3I.001. 1-D Site Scale Calibrated Properties: Supporting Files. 
Submittal date: 09/18/2002. 
161433 LB02091DSSCP3I.002. 1-D Site Scale Calibrated Properties: Data Summary. 
Submittal date: 09/18/2002. 
162354 LB03023DKMGRID.001. UZ 3-D Site Scale Model Grids. Submittal date: 
02/26/2003. 
105587 LB960500834244.001. Hydrological Characterization of the Single Heater Test Area 
in ESF. Submittal date: 08/23/1996. 
105589 LB970600123142.001. Ambient Characterization of the ESF Drift Scale Test Area 
by Field Air Permeability Measurements. Submittal date: 06/13/1997. 
105590 LB980120123142.004. Air Injections in Boreholes 57 through 61, 74 through 78, 
185 and 186 in the Drift Scale Test Area. Submittal date: 01/20/1998. 
114134 LB980120123142.005. Hydrological Characterization by Air Injections Tests in 
Boreholes in Heated Drift in DST. Submittal date: 01/20/1998. 
MDL-NBS-HS-000014 REV00 98 April 2003 

Analysis of Hydrologic Properties Data U0090 
105592 LB980901233124.003. Liquid Release and Tracer Tests in Niches 3566, 3650, 3107, 
and 4788 in the ESF. Submittal date: 09/14/1998. 
105593 LB980912332245.002. Gas Tracer Data from Niche 3107 of the ESF. Submittal 
date: 09/30/1998. 
106787 LB990501233129.001. Fracture Properties for the UZ Model Grids and Uncalibrated 
Fracture and Matrix Properties for the UZ Model Layers for AMR U0090, Analysis 
of Hydrologic Properties Data. Submittal date: 08/25/1999. 
123273 LB990901233124.004. Air Permeability Cross-Hole Connectivity in Alcove 6, 
Alcove 4, and Niche 4 of the ESF for AMR U0015, In Situ Testing of Field 
Processes. Submittal date: 11/01/1999. 
153398 MO0012CARB1314.000. Water - Carbon 13 and Carbon 14 Abundance. Submittal 
date: 12/01/2000. 
153777 MO0012MWDGFM02.002. Geologic Framework Model (GFM2000). Submittal 
date: 12/18/2000. 
155989 MO0109HYMXPROP.001. Matrix Hydrologic Properties Data. Submittal date: 
09/17/2001. 
161496 MO0301SEPFEPS1.000. LA FEP List. Submittal date: 01/21/2003. URN-1084 
109059 MO9906GPS98410.000. Yucca Mountain Project (YMP) Borehole Locations. 
Submittal date: 06/23/1999. 
160258 SN0206T0503102.005. Thermal Conductivity of the Non-Repository Layers of 
Yucca Mountain. Submittal date: 06/27/02. 
160257 SN0208T0503102.007. Thermal Conductivity of the Potential Repository Horizon 
Rev 3. Submittal date: 08/26/2002. 
160826 TM000000UZ7ARS.001. USW UZ-7A Shift Drilling Summaries, Lithologic Logs, 
Structural Logs, Weight Logs, and Composite Borehole Log from 0.0 to 770.0. 
Submittal date: 09/05/1995. 
9.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
LB0205REVUZPRP.001. Fracture Properties for UZ Model Layers Developed from Field Data. 
Submittal date: 05/14/2002. 
LB0207REVUZPRP.001. Revised UZ Fault Zone Fracture Properties. Submittal date: 
07/03/2002. 
LB0207REVUZPRP.002. Matrix Properties for UZ Model Layers Developed from Field and 
Laboratory Data. Submittal date: 07/15/2002. 
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Analysis of Hydrologic Properties Data U0090 
LB0210THRMLPRP.001. Thermal Properties of UZ Model Layers: Data Summary. Submittal 
date: 10/25/2002. 
LB0212C14INFIL.001. 1-D Simulation and Sensititvity Analyses of Groundwater Age by 
Matching to C14 Age Data: 1. Supporting Files. Submittal date: 12/19/2002. 
LB0212C14INFIL.002. 1-D Simulation and Sensititvity Analyses of Groundwater Age by 
Matching to C14 Age Data: 2. Data Summaries. Submittal date: 12/19/2002. 
LB0212AFPGAMMA.001. Active Fracture Parameter Analysis: 1. Supporting Files. Submittal 
date: 12/23/2002. 
LB0212AFPGAMMA.002. Active Fracture Parameter Analysis: 2. Data Summaries. Submittal 
date: 12/23/2002. 
MDL-NBS-HS-000014 REV00 100 April 2003 

Analysis of Hydrologic Properties Data U0090 
10. ATTACHMENTS
Attachment I  Data Point Values in Figures 10 and 11
Attachment II  Data Point Values in Figure 7
Attachment III  Description of Excel Files Used
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Analysis of Hydrologic Properties Data U0090 
INTENTIONALLY LEFT BLANK
MDL-NBS-HS-000014 REV00 102 April 2003 

Analysis of Hydrologic Properties Data U0090 
ATTACHMENT IDATA POINT VALUES IN FIGURES 10 AND 11. 
Data point values in Figure 9 (estimated from Figure 9 of Tokunaga et al. (2000 [152914])): 
Average film thickness (m) Surface transmissivity (m2/s) 
1.2 2.5E-16 
1.5 1.0E-14 
2.0 1.5E-13 
2.5 7.0E-13 
3.0 2.0E-12 
3.5 7.0E-12 
4.0 1.0E-11 
5.0 2.5E-11 
Data point values in Figure 10 (a) (estimated from Figure 7 of Tokunaga and Wan (1997 [139195])): 
Capillary potential (Pa) Average film thickness (m) 
-310 1.0 
-215 2.0 
-165 3.0 
-118 5.0 
-93 7.0 
-70 8.0 
-45 20.0 
-30 30.0 
-14 70.0 
MDL-NBS-HS-000014 REV00 Attachment I-1 April 2003 

Analysis of Hydrologic Properties Data U0090 
Data point values in Figure 10 (b) (estimated from Figure 6 of Tokunaga and Wan (1997 [139195])): 
Capillary pressure (Pa) Surface transmissivity (m2/s) 
-250 8.0E-11 
-200 1.5E-10 
-150 3.5E-10 
-100 8.0E-10 
-50 3.5E-9 
-12 4.0E-8 
MDL-NBS-HS-000014 REV00 Attachment I-2 April 2003 

Analysis of Hydrologic Properties Data U0090 
ATTACHMENT IIDATA POINT VALUES IN FIGURE 7. 
Coating data from DTN: GS980308315215.008 [107355] are used to determine the data point values. Since Tptpmn 
unit has the largest number of survey intervals, data from this unit are analyzed using the box counting method. 
There are 29 survey intervals (30 m long) for this unit that include coated fractures. The total number of coated 
fractures is 134. The locations of the coated fractures along the survey line form a set of points in a one-dimensional 
space. For a given box size (length of a segment) l, there are 30/l small boxes (or segments) for a given survey 
interval. N denotes total numbers of boxes that cover at least one location of the coated fractures (along the survey 
line) for all the survey intervals. The determined N values as a function of l are given as follows: 
N l (m) 
102 3
91 3.75
83 5
81 6
68 7.5
63 10
50 15
29 30
MDL-NBS-HS-000014 REV00 Attachment II-1 April 2003 

Analysis of Hydrologic Properties Data U0090 
ATTACHMENT III - Description of Excel Files 
This attachment describes Excel files used for developing uncalibrated hydraulic properties. All data mentioned in 
this attachment were downloaded directly from TDMS unless otherwise noted. All Excel files were also submitted 
to TDMS. 
hydroprops_fin.xls (OUTPUT DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
Worksheet borehole data' 
 Import DTN: MO0109HYMXPROP.001 [155989]. 
 Import DTN: GS980708312242.010 [106752] (WT-24 physical properties), and GS980808312242.014 
[106748] (SD-6 physical properties) into same columns used in MO0109HYMXPROP.001 [155989]. 
 Import DTN: GS980708312242.011 [107150] (WT-24 high pressure permeameter conductivity), 
GS980908312242.038 [107154] (SD-6 high pressure permeameter conductivity), and 
GS980908312242.041 [107158] (SD-7, SD-9, SD-12, UZ-14, and UZ#16 high pressure permeameter 
conductivity) into columns AA  AD. 
 Added column D, depth in ft, converted from column F. 
 Added column J, residual water content (RWC), with conditional formatting to highlight RWC = 5%. 
 Calculated saturation, in column N, for WT-24 and SD-6. 
 Added GFM lithostratigraphy from MO0012MWDGFM02.002 [153777] file contacts00md.dat to 
columns S & T. Where contacts00md.dat did not have borehole, note in column U indicates source of 
lithostratigraphy (DTNs also listed in rows 5389-5398: GS950108314211.009 [152556], 
GS940208314211.007 [155533], GS940308314211.018 [145589], GS950108314211.008 [152558], 
GS950708314211.028 [160827], GS940208314211.008 [145581], TM000000UZ7ARS.001 [160826]). 
Hydrostratigraphy (hydrogeologic unit  HGU) is determined in column V (based on rules of Flint (1998 [100033], 
pp. 21-32); groupings indicated below are for UZ Model layers: 
 1 & 2 = CCR & CUC  Tpcrn & Tpcrl, lower contact (l.c.) of CCR where porosity (f) > 9%, l.c. of CUC 
where f < 20%. Units are combined because greater property resolution is not needed at upper margin of 
UZ Model. 
 3 & 4 = CUL & CW  Tpcpul & Tpcpmn & Tpcpll & Tpcpln, l.c. of CUL at lithostratigraphic contact 
(l.c. of Tpcpul of identified in columns P & Q, but contact is unimportant for UZ Model 
hydrostratigraphy), l.c. of CW where f > 15%. Units are combined because greater property resolution is 
not needed at upper margin of UZ Model. 
 5 = CMW  base of Tpcpln & Tpcpv3 & Tpcpv2, l.c. where f > 28% 
MDL-NBS-HS-000014 REV00 Attachment III-1 April 2003 

Analysis of Hydrologic Properties Data U0090 
 6 = CNW  base of Tpcpv2 & Tpcpv1, l.c. at lithostratigraphic contact (l.c. of Tpcpv1) 
 7 = BT4  Tpbt4 & top of Tpy, l.c. where f > 30% or at l.c. of Tpy whichever is stratigraphically higher 
 8 = TPY  moderately welded interior of Tpy, l.c. where f < 30% 
 9 = BT3  base of Tpy & Tpbt3, l.c. at lithostratigraphic contact (l.c. of Tpbt3) (note: if Tpbt4 is not 
present and Tpy f > 30% then all Tpy is included in BT3) 
 10 = TPP = Tpp 
 11 = BT2  Tpbt2 & Tptrv3 & Tptrv2, l.c. at lithostratigraphic contact (l.c. of Tptrv2) 
 12 = TC  Tptrv1 & top of Tptrn, l.c. where f > 9% 
 13 = TR  Tptrn, l.c. at lithostratigraphic contact (l.c. of Tptrn) 
 14 = TUL = Tptrl & Tptpul 
 15 = TMN = Tptpmn 
 16 = TLL = Tptpll 
 17 & 18 = TM2 & TM1 = Tptpln 
 19 = PV3 = Tptpv3 
 20 = PV2a = altered Tptpv2 (altered vitric rocks defined where residual water content = 5%) 
 20.1 = PV2v = vitric Tptpv2 
 21 = BT1a = altered Tptpv1 & Tpbt1 
 21.1 = BT1v = vitric Tptpv1 & Tpbt1 
 22 = CHV = vitric Tac 
 23 = CHZ = altered (zeolitic) Tac 
 24 = BTa = altered Tacbt 
 24.1 = BTv = vitric Tacbt 
 25 = PP4 = Tcpuv 
 26 = PP3 = Tcpuc 
 27 = PP2  Tcprn & Tcplc, l.c. where residual water content = 5% 
 28 = PP1  Tcplv & Tcpbt & Tcbuv, l.c. where residual water content < 5% 
 29 = BF3  Tcbuc & Tcbrn & Tcblc, l.c. at lithostratigraphic contact (l.c. of Tcblc) 
 30 = BF2 = Tcblv & Tcbbt & Tctuv 
 31 = TR3 = Tctuc 
Differences between HGU picks from MO0109HYMXPROP.001 [155989] are indicated by non-zero values and 
highlighting in column W. 
MDL-NBS-HS-000014 REV00 Attachment III-2 April 2003 

Analysis of Hydrologic Properties Data U0090 
Worksheet hydroprops' 
 Worksheet borehole data is copied and renamed hydroprops. 
 Columns D, E, F, H, K, M, O-R, U, and W from borehole data are deleted. 
 Columns G, I, H, K, L, J, and N from hydroprops are copied to columns D to J, respectively. 
 All saturation values greater than one in column J are changed to one. 
 The logarithm of all the hydraulic conductivity measurement are collected in column U. Where two 
hydraulic conductivity measurements were made on the same sample the high pressure permeameter 
measurement from column Q is chosen. 
 Values are copied from column U to column V. 
 Rows 62 to 5387 are sorted by column M (HGU). 
Worksheet 007 Ks 
 Import DTN: GS990308312242.007 [107185] (Busted Butte lab measurements of hydrologic properties). 
 The logarithm of hydraulic conductivity is calculated in column J. 
Worksheet 007 n 
 Import DTN: GS990308312242.007 [107185] (Busted Butte lab measurements of physical properties). 
Worksheet 98.008 Ks 
 Import DTN: GS990708312242.008 [109822] (Busted Butte lab measurements of hydrologic properties). 
 The logarithm of hydraulic conductivity is calculated in column G. 
Worksheet 98.008 n 
 Import DTN: GS990708312242.008 [109822] (Busted Butte lab measurements of physical properties). 
Worksheet 006 Ks 
 Import DTN: GS971008312231.006 [107184] (ESF surface sample lab measurements of saturated 
hydraulic conductivity). 
Worksheet Ksat w ND 
 All columns except F, G, and V from hydroprops are copied to Ksat w ND. 
 All rows from 62 to 5387 without conductivity values are deleted. 
 All rows in HGUs without non-detect conductivity measurements (noted as nf or NF) are deleted. 
 Hydraulic conductivity, SPC #, and sample # are copied from 006 Ks to HGU #15 columns O, Q, and 
R, respectively. 
MDL-NBS-HS-000014 REV00 Attachment III-3 April 2003 

Analysis of Hydrologic Properties Data U0090 
 Within each HGU (or group of HGUs as defined above) rows are sorted by ascending conductivity with 
non-detects first. 
 The conductivity for each remaining HGU is ranked in column T. 
 The rank is converted to a percentile in column U, where percentile equals rank divided by (total number 
of measurements plus one). 
 The NORMSINV function is applied to the percentile in column V. This gives (the value minus the 
expected value) divided by the standard deviation for that percentile in a normal distribution, which is 
analogous to plotting the log conductivity values on probability paper. 
The intercept and slope of the line fitted through the NORMSINV values (x-axis) and the log conductivity values (y-
axis) give the expected value and the standard deviation, respectively, of the log conductivity data and account for 
the unknown values of the non-detect measurements, which are assumed to be less than the lowest conductivity 
measured. 
Worksheet Summary 
Matrix properties for each UZ Model layer are shown. Columns and rows are labeled. 
For relative humidity porosity (f(RH)), porosity (f), saturation (S), bulk density (. bulk), and particle density (. 
particle), the arithmetic mean, standard deviation, number of samples, and standard error are given. Standard error is 
standard deviation divided by the square root of number of samples. Minimum and maximum values of saturation 
are also given. Note: data from only one sample are available for the BTv; these data are not inconsistent with the 
BT1v, so the BT1v will be used as an analog for the BTv. 
Residual saturation (Sr), here equal to porosity minus relative humidity porosity, is given in column O. Note: again 
BT1v should be used as an analog for the BTv. 
For log conductivity, the arithmetic mean, standard deviation, number of samples with a measured conductivity, 
standard error (the standard deviation divided by the square root of the number of samples with a measured 
conductivity), and number of non-detect measurements are given in columns X  AB. Where there are non-detect 
measurements, these values are shown in red italics. Note: again BT1v should be used as an analog for the BTv. 
The mean and standard deviation of the log conductivity data for layers with non-detect measurements (the intercept 
and slope of the data in worksheet Ksat w ND) are shown in columns AD and AE. The standard error is calculated 
in column AF as the standard deviation divided by the square root of the total number of conductivity measurements 
including non-detects. 
Log conductivity is converted to log permeability in column AI, where permeability equals conductivity times water 
viscosity divided by water density and gravity. Permeability is shown in column AH. Upscaled log permeability is 
calculated in column AL, where permeability is upscaled by the factor of 0.38 times the variance. Upscaled 
permeability is shown in column AK. Note: again BT1v should be used as an analog for the BTv; additionally, BT1a 
should be used as an analog for the PV2a, and PP1 should be used as an analog for the BF2. 
MDL-NBS-HS-000014 REV00 Attachment III-4 April 2003 

Analysis of Hydrologic Properties Data U0090 
MRC_Q_TCw_fin.xls (OUTPUT-DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
Worksheet 037 wc 
 Import DTN: GS980908312242.037 [107180] (ESF North Ramp moisture study borehole sample water 
retention water content data). 
Worksheet 037 wp 
 Import DTN: GS980908312242.037 [107180] (ESF North Ramp moisture study borehole sample water 
retention water potential data). 
Worksheet 008 wp & wc(g per g) 
 Import DTN: GS980408312242.008 [107161] (ESF North Ramp moisture study borehole sample water 
retention data). 
Worksheet 040 Ks 
 Import DTN: GS980908312242.040 [107169] (ESF North Ramp moisture study borehole sample 
saturated hydraulic conductivity data). 
Worksheet 040 por + 
 Import DTN: GS980908312242.040 [107169] (ESF North Ramp moisture study borehole sample 
physical properties data). 
Worksheet 005 bd 
 Import DTN: GS980308312242.005 [107165] (ESF North Ramp moisture study borehole sample bulk 
density data). 
Worksheet 005 105n 
 Import DTN: GS980308312242.005 [107165] (ESF North Ramp moisture study borehole sample oven 
dried porosity data). 
Worksheet 005 RHn 
MDL-NBS-HS-000014 REV00 Attachment III-5 April 2003 

Analysis of Hydrologic Properties Data U0090 
 Import DTN: GS980308312242.005 [107165] (ESF North Ramp moisture study borehole sample relative 
humidity oven porosity data). 
Worksheet 008 Kr 
 Import DTN: GS980408312242.008 [107161] (ESF North Ramp moisture study borehole sample relative 
permeability data). 
 Calculate saturation in column H from water content (column G) by assuming that the maximum water 
content for each sample is equal to a saturation of one. 
 Calculate saturation in column I from water content (column G) by assuming that the maximum water 
content for each sample as measured for the water potential data (worksheet 008 wp & wc(g per g) 
column G) is equal to a saturation of one. 
 Calculate saturation in column J from water content (column G) by assuming that a water content equal 
to the porosity (from either worksheet 040 por + column G rows 39 to 49 or worksheet 005 105n 
column B) represents a saturation of one. 
 Calculate relative permeability in column K from conductivity (column B) by assuming that the 
maximum conductivity for each sample is equal to a relative permeability of one. 
 Calculate relative permeability in column L from conductivity (column B) by assuming that the saturated 
conductivity (from worksheet 040 Ks column B) is equal to a relative permeability of one. 
Worksheet 039 wc 
 Import DTN: GS980908312242.039 [145272] (USW SD-6 water retention water content data). 
 Use data from hydroprops.xls worksheet borehole data to make HGU assignments for each sample 
(column G). 
MDL-NBS-HS-000014 REV00 Attachment III-6 April 2003 

Analysis of Hydrologic Properties Data U0090 
Worksheet 039 wp 
 Import DTN: GS980908312242.039 [145272] (USW SD-6 water retention water potential data). 
 Use data from hydroprops.xls worksheet borehole data to make HGU assignments for each sample 
(column G). 
Worksheet in-situ 
 Copy average, minimum, and maximum in-situ saturation from hydroprops.xls worksheet summary to 
columns B, C, and D, respectively for HGUs in column A. 
 Copy average in-situ water potential from DATAfix_satsum.xls (Wang 2003 [161654], SN-LBNL-SCI-
003-V2, p. 65; Ahlers 2000 [155853], pp. 9394) worksheet summary to column E for HGUs in 
column A. Note that the averaged data are used for demonstration and not used for calculations. 
Therefore, they do not have any effect on the determined matrix property. 
 Dummy values for plotting in column F. 
Worksheet CUC 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-CUCQ). 
2. Assignment of samples to HGU is checked against hydroprops.xls worksheet borehole data. 
3. Saturation (column D) is (re)calculated assuming that the highest measured water content for each sample is 
equivalent to full saturation. 
4. Data with water potential values less than ~1.4 bars (note: for plotting reasons water potential is expressed here 
as a positive number rather than the conventional negative number) that were acquired with a chilled-mirror 
psychrometer (data from other than borehole USW SD-6 or moisture study boreholes in the ESF North Ramp) 
are moved to the bottom of columns A  E and are excluded from further use. 
5. Water content is plotted vs. water potential with chilled-mirror psychrometer data shown in open diamond 
symbols. Each sample is shown in a different color. 
6. van Genuchten parameters satiated saturation, residual saturation, alpha, n, and m are labeled and are at the top 
of columns G and H (note: satiated saturation fixed equal to one; residual saturation is fixed to the value 
calculated in hydroprops_fin.xls Summary; m=1-(1/n)). 
7. Saturation, S, is predicted in column G based on the measured water potential and the van Genuchten 
parameters using the following expression, 
-m
n
S = S +(S - S )[1 + (aP )]
rsr c 
where Sr is residual saturation, Ss is satiated saturation, Pc is water potential, and a, n, and m are van Genuchten 
parameters. 
8. The squared error (difference) between the predicted saturation and the measured saturation is calculated in 
column H and summed in cell H29. 
MDL-NBS-HS-000014 REV00 Attachment III-7 April 2003 

Analysis of Hydrologic Properties Data U0090 
9. The solver function of Excel is used to estimate values for aand n by minimizing the sum of the squared error 
(cell H29). Note: all optional solver settings are default; where necessary the following constraints are added: 
a= 0 n = 1 
10. Saturation is calculated in column J based on the van Genuchten water retention function, the estimated 
parameters, and a range of water potential values in column I. The results are plotted as a red line. 
11. The Jacobian matrix is calculated numerically in columns L  M using the perturbed parameter values given in 
cells M26:M28. 
J 
ij 
.zi
= 
.pj 
is an element of the Jacobian matrix where zi is the ith predicted value (of saturation for this problem), and pj is 
the jth parameter. Note because n and m are not independent, Jacobian elements for parameter n are not 
evaluated. 
12. The parameter covariance matrix, evaluated in cells P32:Q33, is 
2 T -1
C = s0 (J J )
pp 
where s02 is the sum of the squared error divided by the degree of freedom (# of data points - # of parameters) 
and J is the Jacobian matrix. 
13. The uncertainty (analogous to the standard error calculated for other hydrologic parameters in hydroprops.xls 
worksheet Summary) of the estimated parameters is calculated in cells P36:P37 as the square root of the 
diagonal elements of Cpp. 
14. The 95% error band (two standard errors) on the fitted van Genuchten curve are calculated in columns R and S 
and plotted as gray lines. 
Worksheet CUL & CW 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-CULQ and Worksheet CWQ). 
1a. Appropriate data from worksheets 039 wc and 039 wp are added to columns B, C, and E. 
2. Same as step 2 above.
2a. Sample N27 61.9r is not within either the CUL or CW and is removed.
3. Same as step 3 above.
4. Same as step 4 above.
4a. Data with water potential values of 0.0 bars that were acquired with a centrifuge (data from borehole USW SD6 
or moisture study boreholes in the ESF North Ramp) are moved to the bottom of columns A  E and are 
excluded from further use. 
5. Same as step 5 above 
5a. Centrifuge data are shown as filled circles. Each centrifuge sample is shown in a different color that is not 
necessarily different from the colors used for the chilled-mirror psychrometer data. 
6. Same as step 6 above. 
7. Same as step 7 above. 
8. Same as step 8 above. 
MDL-NBS-HS-000014 REV00 Attachment III-8 April 2003 

Analysis of Hydrologic Properties Data U0090 
9. Same as step 9 above. 
10. Same as step 10 above. 
11. Same as step 11 above. 
12. Same as step 12 above. 
13. Same as step 13 above. 
14. Same as step 14 above. 
15. Average in-situ water potential and saturation data from worksheet in-situ is plotted with 2 bar error bars. 
Worksheet CMW 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-CMWQ). 
1a. Appropriate data from worksheets 037 wc and 037 wp are identified by the lithostratigraphy and ESF station 
in DTN: LAJF831222AQ98.014 [160825] and ESF station of contacts from Table 2 in CRWMS M&O (1998 
[102679]) and are added to columns B, C, and E. 
1b. Appropriate data from worksheet 008 wp & wc(g per g) are identified as in step 1a above are added to 
columns B, C, and E. 
1c. Appropriate data from worksheets 039 wc and 039 wp are added to columns B, C, and E. 
2. Same as step 2 above. 
3. Same as step 3 above. 
3a. The saturation for the data from 1a is calculated assuming that the highest water content from either worksheet 
008 wp & wc(g per g) or 008 Kr for that sample represents the water content at full saturation. 
4. Same as step 4 above. 
4a. Same as step 4a above. 
5. Same as step 5 above. 
5a. Same as step 5a above. 
6. Same as step 6 above. 
7. Same as step 7 above. 
8. Same as step 8 above. 
9. Same as step 9 above. 
10. Same as step 10 above. 
10a. Plotting the estimated function shows that the shape of the curve does not match well with the data. The 
parameters (a and n) are adjusted by hand to improve the subjective match between the shape of the curve and 
the data. This adjustment results in an increase of 0.22 (9%) in the sum of the squared error. 
11. Same as step 11 above. 
12. Same as step 12 above. 
13. Same as step 13 above. 
14. Same as step 14 above. 
15. Same as step 15 above. 
16. Appropriate unsaturated conductivity data from worksheet 008 Kr are identified as in step 1a above, added to 
columns V, W, and X, and are plotted as circles connected by a dashed line. As in 3a, the saturation is 
MDL-NBS-HS-000014 REV00 Attachment III-9 April 2003 

Analysis of Hydrologic Properties Data U0090 
calculated assuming that the highest water content measured for the sample represents full saturation (column 
W is linked to either column H or I in 008 Kr). 
2
m
S . 
(1
17. Relative permeability is k =()[1 - - S 1 m )]
rwe e 
where . and m are fitting parameters and
(S -S )
r
S = 
e 
(S -S )
sr 
where S is saturation, Sr is residual saturation, and Ss is satiated saturation. Use of a match point, S0, that is near, 
but not at, full saturation is recommended to avoid problems with identification of full saturation and large 
changes in unsaturated conductivity very near saturation due to macropores. Relative permeability can then be 
redefined in terms of the conductivity at the match point: 
m .
K ()=. e ..
.
. (11 m )2 
S .S ..1 - - S
we e
m
. 1 m
(1 
).
. 
Kw (Se0 , ).Se0 , ...
1 - - Se0 , 
. 
where Se,0 is the effective saturation at the match point and Kw(Se,0) is the conductivity at the match point. A 
saturation match point, Se,0, of 0.95 is used for the CMW. The unsaturated conductivity at the match point, 
Kw(Se,0), is calculated in column Z for each sample by linear interpolation between the nearest saturation and log 
conductivity data points. 
18. The redefined relative permeability, left side of last equation in step 17, is calculated in column Y. 
19. The relative permeability predicted from the saturation data (column W), as expressed on the right side of the 
last equation in step 17, is calculated in column AA. Parameters m and Sr are the same as for the water retention 
function. 
20. The squared error (difference) between the predicted relative permeability and the measured relative 
permeability is calculated in column AB and summed in cell AB31. 
21. The solver function of Excel is used to estimate ., cell X29, by minimizing the sum of the squared error (cell 
X23, which is linked to cell AB31). Note: all optional solver settings are default. 
22. The Jacobian matrix is calculated numerically in column AC using the perturbed .value given in cell AC29. 
The variance and standard error of . are calculated in cells AD34 and AE34, respectively. 
23. Relative permeability is calculated in column U for a range of saturation values in column T. The results are 
plotted as an orange line. 
24. The average, minimum, and maximum in-situ saturation are plotted as a gray circle and error bars. 
MDL-NBS-HS-000014 REV00 Attachment III-10 April 2003 

Analysis of Hydrologic Properties Data U0090 
MRC_Q_PTn_fin.xls (OUTPUT-DTN: LB0207REVUZPRP.002) 
THIS FILE WAS USED TO DEVELOP MATRIX PROPERTIES FOR UZ MODEL 
LAYERS. 
Worksheets in-situ, 037 wc, 037 wp, 008 Kr, 040 por +, 005 bd, 005 105n, 005 RHn, 008 wp & wc(g 
per g), 039 wc, 039 wp are prepared as for MRC_Q_TCw_fin.xls with the exception of relative permeability 
calculations documented in steps 16 - 23 below which are carried out in worksheet 008 Kr. 
Worksheets CMW, BT4, TPY, BT3, TPP, and BT2 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-CNWQ, Worksheet-BT4Q, Worksheet-TPYQ, Worksheet-BT3Q, Worksheet-TPPQ, 
Worksheet-BT2Q,). 
1a. Same as step 1a above except that no data are identified for TPY. 
1b. Same as step 1b above except that no data are identified for TPY. 
1c. Same as step 1c above except that no data are identified for TPY. 
2. Same as step 2 above. This results in sample UZ16 171.7r begin reassigned from TPY to BT4 and sample SD9 
74.1 being removed from BT3 (it is already assigned correctly to CNW). 
3. Same as step 3 above. 
3a. Same as step 3a above. 
4. Same as step 4 above. 
4a. Same as step 4a above. 
5. Water content is plotted vs. water potential on worksheets PTn curves and PTn curves (2) for HGU groups 
CNW, BT4, and TPY and BT3, TPP, and BT2, respectively, with chilled-mirror psychrometer data shown in 
open diamond symbols. Each sample is shown in a different color. 
5a. Centrifuge data are shown as filled circles. Each centrifuge sample is shown in a different color that is not 
necessarily different from the colors used for the chilled-mirror psychrometer data. Note: there are no centrifuge 
data for TPY. 
6. Same as step 6 above. 
7. Same as step 7 above. 
8. Same as step 8 above. 
9. Same as step 9 above. 
10. Same as step 10 above. Results are plotted in worksheets PTn curves and PTn curves (2). 
11. Same as step 11 above. 
12. Same as step 12 above. 
13. Same as step 13 above. 
14. Same as step 14 above. Results are plotted in worksheets PTn curves and PTn curves (2). 
15. Same as step 15 above. Results are plotted in worksheets PTn curves and PTn curves (2). 
MDL-NBS-HS-000014 REV00 Attachment III-11 April 2003 

Analysis of Hydrologic Properties Data U0090 
16. Appropriate unsaturated conductivity data in worksheet 008 Kr are identified as in step 1a above and are 
plotted as circles connected by a dashed line in worksheets PTn curves and PTn curves (2). As in 3a, the 
appropriate saturation data are identified and highlighted in either column H or I in 008 Kr by assuming that 
the highest water content measured for the sample represents full saturation. 
17. Same as step 17 above except that the saturation match points are 
CNW: S0 = 0.85, 
BT4: S0 = 0.65, 
BT3: S0 = 0.95, 
TPP: S0 = 0.80, 
BT2: S0 = 0.83, 
and there are no relative permeability data for TPY. 
18. Same as step 18 above except the calculation is carried out in worksheet 008 Kr in column M. 
19. Same as step 19 above except the calculation is carried out in worksheet 008 Kr in column N. 
20. In worksheet 008 Kr, the squared error (difference) between the predicted relative permeability and the 
measured relative permeability is calculated in column X and summed in cells W18:W22 for each of the HGUs 
identified in cells V18:V22. 
21. In worksheet 008 Kr, the solver function of Excel is used to estimate ., cells Z18:Z22, for each HGU 
identified in cells V18:V22 by minimizing the sum of the squared error for the same HGU, cells W18:W22. 
Note: all optional solver settings are default. 
22. In worksheet 008 Kr, the Jacobian matrix is calculated numerically in column Y using the perturbed . value 
given in cells AA18:AA22. The variance and standard error of . are calculated in cells AB18:AB22 and 
AC18:AC22, respectively. 
23. In worksheet 008 Kr, relative permeability is calculated in columns Q:U for a range of saturation values in 
column P. The results are plotted as an orange line in worksheets PTn curves and PTn curves (2). 
24. The average, minimum, and maximum in-situ saturation are plotted as a gray circle and error bars in worksheets 
PTn curves and PTn curves (2). 
MRC_Q_TSw_fin.xls (OUTPUT-DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
Worksheets in-situ, 037 wc, 037 wp, 008 wp & wc(g per g), 039 wc, 039 wp are prepared as for 
MRC_Q_TCw_fin.xls. 
Worksheet 007 S wp 
 Import DTN: GS990308312242.007 [107185] (Busted Butte water retention data). 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column F). 
MDL-NBS-HS-000014 REV00 Attachment III-12 April 2003 

Analysis of Hydrologic Properties Data U0090 
Worksheet 007 Ks 
 Import DTN: GS990308312242.007 [107185] (Busted Butte hydraulic conductivity data). 
Worksheet 007 S K 
 Import DTN: GS990308312242.007 [107185] (Busted Butte relative permeability data). 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column F). 
 In column G, calculate relative permeability from conductivity in column E assuming that the saturated 
conductivity in worksheet 007 Ks column H represents a relative permeability of one. Note: there is no 
saturated conductivity measurement for sample INJ-4-16.8B, so the highest conductivity measured is 
assumed to equal a relative permeability of one, and relative permeability is calculated in column H. 
Worksheet 99.008 S wp 
 Import DTN: GS990708312242.008 [109822] (Busted Butte water retention data). 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column G). 
Worksheet 99.008 Ks 
 Import DTN: GS990708312242.008 [109822] (Busted Butte hydraulic conductivity data). 
Worksheet 99.008 S K 
 Import DTN: GS990708312242.008 [109822] (Busted Butte relative permeability data). 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column G). 
 In column H, calculate relative permeability from conductivity in column E assuming that the saturated 
conductivity in worksheet 007 Ks column H represents a relative permeability of one. 
Worksheet 012 wc 
 Import DTN: GS980808312242.012 [149375] (USW WT-24 water retention water content data). 
 Use data from hydroprops.xls worksheet borehole data to make HGU assignments for each sample 
(column G). 
MDL-NBS-HS-000014 REV00 Attachment III-13 April 2003 

Analysis of Hydrologic Properties Data U0090 
Worksheet 012 wp 
 Import DTN: GS980808312242.012 [149375] (USW WT-24 water retention water potential data). 
 Use data from hydroprops.xls worksheet borehole data to make HGU assignments for each sample 
(column G). 
Worksheet 95.008 wc 
 Import DTN: GS950608312231.008 [144662] (USW UZ-N27 and UE-25 UZ#16 water retention water 
content data). Note: for the most part, these data are already included under DTN: 
MO0109HYMXPROP.001 [155989], however, there are some additional data under this DTN. 
Worksheet 95.008 wp 
 Import DTN: GS950608312231.008 [144662] (USW UZ-N27 and UE-25 UZ#16 water retention water 
potential data). Note: for the most part, these data are already included under DTN: 
MO0109HYMXPROP.001 [155989], however, there are some additional data under this DTN. 
Worksheets TC, TR, TUL, TMN, TLL, TM2 & TM1, PV3, PV2a, and PV2v 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-TCQ, Worksheet-TRQ, Worksheet-TULQ, Worksheet-TMNQ, Worksheet-TLLQ, 
Worksheet-TM2Q, Worksheet-TM1Q, Worksheet-PV3Q, Worksheet-PV2Q). Note: Data from WorksheetTM2Q 
and Worksheet-TM1Q are combined in worksheet TM2 & TM1. 
1a. Appropriate data from worksheets 037 wc and 037 wp are identified by the lithostratigraphy and ESF station 
in DTN: LAJF831222AQ98.014 [160825] and ESF station of contacts from Table 2 in CRWMS M&O (1998 
[102679]) and are added to columns B, C, and E of worksheets TC and TR. 
1b. Appropriate data from worksheet 008 wp & wc(g per g) are identified as in step 1a above are added to 
columns B, C, and E of worksheet TC. 
1c. Appropriate data from worksheets 039 wc and 039 wp are added to columns B, C, and E of worksheets 
TC, TR, TM2 & TM1, and PV3. 
1d. Appropriate data from worksheets 007 S wp and 99.008 S wp are added to columns B, D, and E of 
worksheet PV2v. 
1e. Appropriate data from worksheets 012 wc and 012 wp are added to columns B, C, and E of worksheet 
PV2a. 
1f. Appropriate data from worksheets 95.008 wc and 95.008 wp are added to columns B, C, and E of 
worksheets TLL and PV3. 
2. Same as step 2 above.
2a. Sample SD9-1440.5r is not within the PV2 and is removed.
3. Same as step 3 above except for worksheet PV2v where data are given in terms of saturation rather than water 
content. 
MDL-NBS-HS-000014 REV00 Attachment III-14 April 2003 

Analysis of Hydrologic Properties Data U0090 
4. Same as step 4 above. 
4a. Same as step 4a above. 
5. Same as step 5 above. 
5a. Same as step 5a above. 
6. Same as step 6 above. 
7. Same as step 7 above. 
8. Same as step 8 above. 
9. Same as step 9 above. 
10. Same as step 10 above. 
10a. Plotting the estimated function for PV3 shows that the shape of the curve does not match well with the data. 
The parameters (aand n) are adjusted by hand to improve the subjective match between the shape of the curve 
and the data. This adjustment results in an increase of 0.14 (16%) in the sum of the squared error. 
11. Same as step 11 above. 
12. Same as step 12 above. 
13. Same as step 13 above. 
14. Same as step 14 above. 
15. Same as step 15 above. 
16. In worksheet PV2v only, appropriate unsaturated conductivity data from worksheets 007 S K and 99.008 S 
K are identified as in step 1d above, added to columns V, W, and X, and are plotted as circles connected by a 
dashed line. 
17. In worksheet PV2v only, same as step 17 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW, except 
Se,0 = 0.9. 
18. In worksheet PV2v only, same as step 18 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
19. In worksheet PV2v only, same as step 19 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
20. In worksheet PV2v only, same as step 20 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
21. In worksheet PV2v only, same as step 21 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
22. In worksheet PV2v only, same as step 22 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
23. In worksheet PV2v only, same as step 23 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
24. In worksheet PV2v only, same as step 24 under spreadsheet MRC_Q_TCw_fin.xls, worksheet CMW. 
MRC_Q_CHCF_fin.xls (OUTPUT-DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
Worksheets in-situ, 039 wc, 039 wp, 007 S wp, 007 Ks, 007 S K, 99.008 S wp, 99.008 Ks, 99.008 S 
K, 012 wc, 012 wp, 95.008 wc, and 95.008 wp are prepared as for MRC_Q_TSw_fin.xls. 
Worksheet 001 wc wp 
 Import DTN: GS010608312242.001 [160822] (Busted Butte water retention data). 
MDL-NBS-HS-000014 REV00 Attachment III-15 April 2003 

Analysis of Hydrologic Properties Data U0090 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column F). 
Worksheet 001 wc K 
 Import DTN: GS010608312242.001 [160822] (Busted Butte relative permeability data). 
 Use data from DTN: LA0207SL831372.001 [160824] to make HGU assignments for each sample 
(column G). 
 In column I, calculate the saturation from water content in column D assuming that the highest water 
content listed for the same sample in worksheet 001 wc wp (if measured) is the water content at full 
saturation. 
 In column J, calculate the saturation from water content in column D assuming that the highest water 
content listed for each sample is the water content at full saturation. 
 In column K, calculate relative permeability from conductivity in column E assuming that the highest 
conductivity for each sample is the saturated conductivity. 
Worksheet 96.003 wp wc 
 Import DTN: GS960808312231.003 [147590] (USW SD-7, USW SD-9, USW SD-12, and UE-25 UZ#16 
water retention data). Note: for the most part, these data are already included under DTN: 
MO0109HYMXPROP.001 [155989], however, there are some additional data under this DTN. 
Worksheets BT1v, BT1a, CHV, CHZ, BTv, BTa, PP4, PP3, PP2, PP1, BF3, and BF2 
1. Import DTN: MO0109HYMXPROP.001 [155989] (water retention Hydrologic Properties data from File 
MRCQ, Worksheet-BT1aQ, Worksheet-CHZQ, Worksheet-BTQ, Worksheet-PP4Q, Worksheet-PP3Q, 
Worksheet-PP2Q, Worksheet-PP1Q, Worksheet-BF3Q, Worksheet-BF2Q). Note: Data from Worksheet-BTQ 
are assigned to HGU BTa; no data are identified in this DTN for HGUs BT1v, CHV, and BTv. 
1a. Appropriate data from worksheets 039 wc and 039 wp are added to columns B, C, and E of worksheets 
BT1v, CHV, BTv, PP4, PP3, and BF3. 
1b. Appropriate data from worksheet 99.008 S wp are added to columns B, D, and E of worksheets BT1v and 
CHV. 
1c. Appropriate data from worksheet 007 S wp are added to columns B, D, and E of worksheet BT1v. 
1d. Appropriate data from worksheets 001 wc wp are added to columns B, C, and E of worksheets BT1v and 
CHV. 
1e. Appropriate data from worksheets 012 wc and 012 wp are added to columns B, C, and E of worksheets 
BT1a and CHZ. 
1f. Appropriate data from worksheets 95.008 wc and 95.008 wp are added to columns B, C, and E of 
worksheets CHZ, PP4, PP2, and PP1. 
1g. Appropriate data from worksheet 96.003 wc wp are added to columns B, C, and E of worksheet CHV. 
2. Same as step 2 above. 
MDL-NBS-HS-000014 REV00 Attachment III-16 April 2003 

Analysis of Hydrologic Properties Data U0090 
2a. Sample SD9-1440.5r, which was not within PV2 (see step 2a above) is within the BT1v and is added. 
3. Same as step 3 above except for data from worksheets 007 S wp and 99.008 S wp where data are given in 
terms of saturation rather than water content. 
3a. The saturation for the data from 1d is calculated assuming that the highest water content from either worksheet 
001 wc wp or 001 wc K for that sample represents the water content at full saturation. 
4. Same as step 4 above. 
4a. Same as step 4a above. 
5. Same as step 5 above. 
5a. Same as step 5a above. 
6. Same as step 6 above. 
7. Same as step 7 above. 
8. Same as step 8 above. 
9. Same as step 9 above. 
10. Same as step 10 above. 
10a. Plotting the estimated function for PP4 shows that the shape of the curve does not match well with the data. The 
parameters (a and n) are adjusted by hand to improve the subjective match between the shape of the curve and 
the data. This adjustment results in an increase of 0.951 (65%) in the sum of the squared error. 
11. Same as step 11 above. 
12. Same as step 12 above. 
13. Same as step 13 above. 
14. Same as step 14 above. 
15. Same as step 15 above. 
16. In worksheets BT1v and CHV only, appropriate unsaturated conductivity data from worksheets 99.008 S 
K, 007 S K, and 001 wc K are identified as in steps 1b, 1c, and 1d above, added to columns V, W, and X, 
and are plotted as circles connected by a dashed line. 
16a. As in step 3a above, the saturation for the data from 001 wc K is calculated assuming that the highest water 
content from either worksheet 001 wc wp or 001 wc K for that sample represents the water content at full 
saturation. 
17. In worksheets BT1v and CHV only, same as step 17 above. 
18. In worksheets BT1v and CHV only, same as step 18 above. 
19. In worksheets BT1v and CHV only, same as step 19 above. 
20. In worksheets BT1v and CHV only, same as step 20 above. 
21. In worksheets BT1v and CHV only, same as step 21 above. 
22. In worksheets BT1v and CHV only, same as step 22 above. 
23. In worksheets BT1v and CHV only, same as step 23 above. 
24. In worksheets BT1v and CHV only, same as step 24 above. 
MDL-NBS-HS-000014 REV00 Attachment III-17 April 2003 

Analysis of Hydrologic Properties Data U0090 
vG_Summary_fin.xls (OUTPUT-DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
Note: This spreadsheet contains links to hydroprops_fin.xls, MRC_Q_TCw_fin.xls, MRC_Q_PTn_fin.xls, 
MRC_Q_TSw_fin.xls, and MRC_Q_CHCF_fin.xls; these spreadsheets must be open at the same time this 
spreadsheet is open. 
Worksheet vG Summary 
All values shown in columns C, D, E, G, H, and J are linked to the spreadsheets (above) where they are estimated. 
Values of . and s.(columns L and M) for CMW, CNW, BT4, BT3, TPP, BT2, PV2v, BT1v, and CHV are those 
estimated by fitting relative permeability data. All other values of . and s. are estimated as 
k.= Am - B log()+ C 
s
and 
.=(A +s )m -(B +s )log(k )+(C +s )-.
AB C 
where m is the van Genuchten parameter, k is upscaled permeability, and A, B, and C are fitting parameters. Note: 
the minus sign before B is because . is correlated to log(k-1). The fitting parameters are estimated in worksheet 
fitted kr. 
Worksheet fitted kr 
For each layer, column A, where ., column D, has been estimated from relative permeability data, the estimates of 
upscaled permeability, column B, and the van Genuchten parameter m, column C, are given. 
The parameter . is predicted in column E using the equation given above and the parameters in cells F1:F3. The 
squared difference between the two values of . is calculated in column F and summed in cell F4. 
The Jacobian matrix (see step 11 under spreadsheet MRC_Q_TCw_fin.xls worksheet CUC) is calculated in columns 
G  I using the perturbed parameter values in cells G1:G3. The covariance matrix (see step 12 under spreadsheet 
MRC_Q_TCw_fin.xls worksheet CUC) is calculated in cells K5:M7. The standard error (see step 13 under 
spreadsheet MRC_Q_TCw_fin.xls worksheet CUC) is calculated in cells K1:K3. The parameter values perturbed by 
one standard error are given in cells N1:N3. 
PV2 deep borehole data.xls (OUTPUT DTN: LB0207REVUZPRP.002) 
This file was used to develop matrix properties for UZ model layers. 
MDL-NBS-HS-000014 REV00 Attachment III-18 April 2003 

Analysis of Hydrologic Properties Data U0090 
This spreadsheet is a copy of cells B3756:U3793 from hydroprops_fin.xls worksheet hydroprops. Note: column A 
in this spreadsheet should be ignored. 
lecan97.xls (output DTN: LB0205REVUZPRP.001) 
This file was used for estimating fracture parameters. 
In this spreadsheet, only the workbook New Layers was used to calculated fracture permeability for UZ model 
layers that have been documented in the Model Report. Therefore, the documentation is limited to this workbook. 
In this spreadsheet, columns A, B, and C contain data from DTN: GS960908312232.013 [105574]. Column D 
corresponds to log of air permeability calculated from column C. Columns H, I, J contain total number of air k 
measurements, average of log of air permeability and standard deviation of log(air permeability) for each geologic 
unit within a single borehole, respectively. Columns L, M, N contain total number of air k measurements, average of 
log of air permeability and standard deviation of log(air permeability) for each geologic unit for all the boreholes, 
respectively. 
UTCA_BRFA.xls (output DTN: LB0205REVUZPRP.001) 
This file was used for estimating fracture parameters. 
In this spreadsheet, there are two notebooks UPCA and UTCA_BRFA. In UPCA, columns A-E contain data from 
Alcove 3 (DTN: GS970183122410.001 [105580]). Column F corresponds to log of air permeability calculated from 
column C. Columns H, I and J contain total number of air k measurements, average of log of air permeability and 
standard deviation of log(air permeability) for each geologic unit within a single borehole (labeled as location), 
respectively. Columns M, N and O contain total number of air k measurements, average of log of air permeability 
and standard deviation of log(air permeability) for each geologic unit for all the boreholes, respectively. 
In UTCA_BRFA, columns C-E contain data from Alcove 1 (DTN: GS970183122410.001 [105580]). Column F 
corresponds to log of air permeability calculated from column E. Columns H, I, J, K contain total number of air k 
measurements, geometric mean of air k, log of geometric mean of air permeability and standard deviation of log(air 
permeability) for each geologic unit within a single borehole (labeled as location), respectively. Columns V, W 
and X contain total number of air k measurements, average of log of air permeability and standard deviation of 
log(air permeability) for each geologic unit for all the boreholes, respectively. 
drift.xls (Output DTN: LB0205REVUZPRP.001) 
In this spreadsheet, column D contains air permeability data from DTNs: LB970600123142.001 [105589], 
LB980120123142.004 [105590], LB980120123142.005 [114134], and LB960500834244.001 [105587]. Column E 
corresponds to log of permeability values calculated from column D. Columns J and K contain total number of air 
permeability measurements, geometric mean of log of air permeability and standard deviation of log(air 
permeability), respectively. 
airk.xls (output DTN: LB0205REVUZPRP.001) 
This file was used for estimating fracture parameters. 
MDL-NBS-HS-000014 REV00 Attachment III-19 April 2003 

Analysis of Hydrologic Properties Data U0090 
Since the equations used in the spreadsheet are simple standard functions of MS Excel, the supplementation needed 
here is a description of the specific cells that contain the input, output and calculation used in the spreadsheet 
application. A description of the equations is provided. This spreadsheet is linked to lecan97.xls, drift.xls and 
UTCA_BRFA.xls that discussed above. 
In this spreadsheet, only the work book New Layers was used to calculated fracture permeability for UZ model 
layers that has been documented in the Model Report. Therefore, the documentation is limited to this workbook. 
This spreadsheet calculates layer-averaged fracture permeability information based on air k data contained in 
lecan97.xls, UTCA_BRFA.xls and drift.xls. 
In the workbook New Layers, columns A, B, C, and D contain model layer name, total number of air k 
measurements from different sources, mean of log(air permeability), and the standard deviation of log(air 
permeability), respectively. 
To demonstrate the calculation procedure, model layer tcw12 is used as an example. In this discussion subscripts 1, 
2, 3 refer to data from Lecan97.xls, UTCA (UTCA_BRFA.xls) and UPCA (UTCA_BRFA.xls), respectively. 
1. Cell B5 contains the total number of measurements, denoted by N, that was calculated as follows: Cells J4-J6 
contain the number of measurements from the three data sources (N1, N2 and N3), respectively, by linking with the 
relevant cells in lecan97.xls and UTCA_BRFA.xls). The total number is given in Cell J7 calculated by 
N=N1+N2+N3 
The value in Cell J7 was then assigned to B5. 
2. Cell C5 contains the mean of log(air permeability) (Xm) that was calculated as follows: Cells K4-K6 contain the 
means from the three data sources (Xm1, Xm2 and Xm3), respectively, by linking with the relevant cells in lecan97.xls 
and UTCA_BRFA.xls. Values in cells P4-P6 are calculated as N1Xm1, N2Xm2, and N3Xm3. Cell P7 summates values 
in the three cells. Then, the mean Xm was stored in cell K7 and calculated by 
Xm = (N1Xm1+N2Xm2+N3Xm3)/N= (the value in cell P7)/(the value in cell J4) 
The value in K7 was assigned to C5. 
3. Cell D5 contains standard deviation of log(air permeability) (s) that was calculated as follows: Cells L4-L6 
contain the standard deviation values from the three data sources ( s1, s2 and s3), respectively, by linking with the 
2
relevant cells in lecan97.xls and UTCA_BRFA.xls. Variances in cells N4-N6 (s1, s22 and s32) were then 
calculated from cells L4-L6. Cells Q4 was calculated by 
N1
(X N 1m )2 +s 2 N1(N1 -1)=. xi 
2
11 
N1 i =1 
where xi refers to log(air permeability) for measurement i. Similar calculations were performed for the other two 
data sources. Cell Q7 summates values in the three cells Q4-Q6. The value in Q7 corresponds to 
MDL-NBS-HS-000014 REV00 Attachment III-20 April 2003 

Analysis of Hydrologic Properties Data U0090 
N 
. xi 
2 
i =1 
where N is the total number of measurements from the three data sources. If using the cell names to represent values 
in them, N7 was calculated as 
NN
Q J 7 - (P 7)2 N . xi 
2 - (NXm )2 . (xi - Xm )2
7 = i =1 = i =1
N 7 = 
7( (
J J 7 - 1)N N - 1)N - 1 
By definition, N7 is the overall variance of log(air permeability) for air k data from the three data sources. L7 
contains the corresponding standard deviation calculated from N7. Its value was assigned to D5. 
The similar procedure was used for all the model layers that have measurement from different sources. If a model 
layer only has data from a single source (lecan97.xls, drift.xls and UTCA_BRFA.xls), the above calculation 
procedure was not needed. The relevant value can be directly obtained from the data source. Also in the spreadsheet, 
some standard deviation values were assigned to model layers that only contain one measurement. These values 
were not used. 
Fpor.xls (output DTN: LB0205REVUZPRP.001) 
This file was used for estimating fracture parameters. 
Sheet 1 contains fracture porosity values calculated from other sources (Section 6.1.3.4). The input sources and 
calculations are explicitly indicated in the workbook. 
In sheet2, Column A contains names of UZ model layers and Column C contains the corresponding aperture 
values (output DTN: LB0205REVUZPRP.001). Column E contains van Genuchten acalculated using Equation 
(9). Column G contains fracture intensity or frequency values (DTN: LB990501233129.001 [106787]). Column I 
contains fracture porosity values calculated from Equation (12). Column M contains log(a) values. Below Row 32 
was used for estimating fracture porosity for tsw34. The input sources and calculations are explicitly indicated in the 
workbook for this estimation. 
MDL-NBS-HS-000014 REV00 Attachment III-21 April 2003