Seepage Calibration Model and Seepage Testing Data Rev 03, ICN 00

MDL-NBS-HS-000004
  
September 2004 

1. PURPOSE 
The purpose of this Model Report is to document the Seepage Calibration Model (SCM). The 
SCM was developed (1) to establish the conceptual basis for the Seepage Model for Performance 
Assessment (SMPA), and (2) to derive seepage-relevant, model-related parameters and their 
distributions for use in the SMPA and seepage abstraction in support of the Total System 
Performance Assessment for License Application (TSPA-LA). This Model Report has been 
revised in response to a comprehensive, regulatory-focused evaluation performed by the 
Regulatory Integration Team [Technical Work Plan for: Regulatory Integration Evaluation of 
Analysis and Model Reports Supporting the TSPA-LA (BSC 2004 [DIRS 169653])]. 
The SCM is intended to be used only within this Model Report for the estimation of seepage-
relevant parameters through calibration of the model against seepage-rate data from 
liquid-release tests performed in several niches along the Exploratory Studies Facility (ESF) 
Main Drift and in the Cross-Drift. The SCM does not predict seepage into waste emplacement 
drifts under thermal or ambient conditions. Seepage predictions for waste emplacement drifts 
under ambient conditions will be performed with the SMPA [Seepage Model for PA Including 
Drift Collapse (BSC 2004 [DIRS 167652])], which inherits the conceptual basis and model-
related parameters from the SCM. Seepage during the thermal period is examined separately in 
the Thermal Hydrologic (TH) Seepage Model [see Drift-Scale Coupled Processes (DST and TH 
Seepage) Models (BSC 2004 [DIRS 170338])]. 
The scope of this work is (1) to evaluate seepage rates measured during liquid-release 
experiments performed in several niches in the Exploratory Studies Facility (ESF) and in the 
Cross-Drift, which was excavated for enhanced characterization of the repository block (ECRB); 
(2) to evaluate air-permeability data measured in boreholes above the niches and the Cross-Drift 
to obtain the permeability structure for the seepage model; (3) to use inverse modeling to 
calibrate the SCM and to estimate seepage-relevant, model-related parameters on the drift scale; 
(4) to estimate the epistemic uncertainty of the derived parameters, based on the goodness-of-fit 
to the observed data and the sensitivity of calculated seepage with respect to the parameters of 
interest; (5) to characterize the aleatory uncertainty of the parameters as a result of spatial 
variability; (6) to evaluate prediction uncertainty based on linear uncertainty-propagation 
analyses and Monte Carlo simulations; (7) to validate the SCM during model development, and 
validate the SCM using the post-development activities outlined in the Technical Work Plan 
(TWP, see below); (8) to provide the technical basis for the resolution of unconfirmed issues 
previously labeled to be verified (TBV); and (9) to provide the technical basis for screening of 
certain seepage-related features, events, and processes (FEPs). 

The primary caveats and limitations in the scope of this Model Report and the results from the 
SCM are as follows: 
1. The seepage models are intended to provide estimates of the seepage flux averaged 
over a 5 m drift segment (the approximate length of a waste package). The seepage 
models are not expected to quantitatively predict individual seepage events or the 
precise spatial seepage distribution along the drift. 
2. By definition, the derived parameters are related to the specific model structure used, 
i.e., these parameters are only applicable to a conceptual and numerical model similar 
to the SCM. (Note that the SCM and the SMPA are compatible in this sense.) The 
parameters are also process specific and scale dependent, i.e., while they can be 
considered optimal for seepage calculations on the drift scale, they are not necessarily 
applicable to other processes on different scales. 
3. The effective parameters derived in this Model Report capture many processes and 
features leading to dripping of formation water into a large underground opening. 
However, this does not include water dripping as a result of condensate accumulation 
on the drift surface or other in-drift moisture redistribution processes. 
More detailed discussions of the appropriateness of the modeling approach, the sufficiency of the 
data, and the inherent limitations and caveats can be found throughout this Model Report. 
The technical scope, content, and management of this Model Report are described in the 
planning document Technical Work Plan for: Unsaturated Zone Flow Model Report Integration 
(BSC 2004 [DIRS 169654], Section 2). This document does not deviate from the TWP; no 
additional criteria were identified in the TWP. 
Direct inputs to this Model Report are listed in Section 4.1. These source data include the 
air-permeability and liquid-release test data described in the report In Situ Field Testing of 
Processes (BSC 2004 [DIRS 170004], Sections 6.2 and 6.11), calculated percolation flow fields 
described in the report UZ Flow Models and Submodels (BSC 2001 [DIRS 158726]) and the 
related numerical grid described in the report Development of Numerical Grids for UZ Flow and 
Transport Modeling (BSC 2001 [DIRS 159356]), fracture property data described in the reports 
Analysis of Hydrologic Properties Data (BSC 2004 [DIRS 170038]) and Calibrated Properties 
Model (CRWMS M&O 2000 [DIRS 144426]). 
This Model Report mainly supports the reports that document the SMPA (BSC 2004 
[DIRS 167652]) and seepage abstraction [Abstraction of Drift Seepage (BSC 2004 
[DIRS 169131])]. In addition, the discussions and results are used in the reports Features, 
Events, and Processes in UZ Flow and Transport (BSC 2004 [DIRS 170012], Section 6.1.24), 
and Drift-Scale Coupled Processes (DST and TH Seepage) Models (BSC 2004 [DIRS 170338]). 
This report also addresses the following issues: The development of a collection system in 
Niche 5 (also referred to as Niche 1620) for mass balance considerations (see Sections 6.5.3 and 
6.8); monitoring and estimation of evaporation effects (see Sections 6.3.3.4, 6.5.4, 6.6.1.3, 
6.6.1.4, 6.6.2.3, and 6.6.3.3); inclusion of film flow effects (see Sections 6.1.2, 6.3.3, 6.3.3.2, 
6.3.4, and 6.6.3.1); inclusion of effects from small-scale irregularities at the drift surface (see 

Sections 6.3.3, 6.3.3.2, 6.3.3.3, 6.3.3.5, 6.3.4, 6.6.2.2, 6.6.3, 6.6.3.3, 8.2, and Appendices CE); 
justification of the continuum approach (see Sections 6.3.2, 6.3.3.2, 6.3.4, and 6.4.1); discussion 
of differences between continuum models and discrete fracture network models (see Sections 
6.3.2 and 6.4.1); and the use of Niche 5 data to improve parameter estimates (see Sections 1, 4.1, 
6.5, 6.6.2, 6.6.3, 7.3, 7.4, 8.1, and 8.2). 

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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 program as indicated in 
Technical Work Plan for: Unsaturated Zone Flow Model Report Integration (BSC 2004 [DIRS 
169654], Section 8.1). Approved quality assurance procedures identified in the TWP (BSC 2004 
[DIRS 169654], 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 2004 [DIRS 169654], Section 8.4) during the modeling and 
documentation activities. 
This model report examines the properties of natural barriers identified that are classified in the 
Q-List (BSC 2004 [DIRS 168361]) as Safety Category because they are important to waste 
isolation, as defined in AP-2.22Q, Classification Analyses and Maintenance of the Q-List. The 
report contributes to the analysis and modeling data used to support performance assessment 
(PA). The conclusions of this model report do not affect the proposed repository design or 
engineered features important to safety, as defined in AP-2.22Q. 

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3. USE OF SOFTWARE 
The software programs used in this study are listed in Table 3-1. These programs were selected 
because they are appropriate for the intended application. They were used only within the range 
of validation; there are no limitations on outputs due to the selected software. The software 
programs were obtained from Software Configuration Management; their qualification and 
baseline status is given in the Document Input Reference System (DIRS). 
Table 3-1. Qualified Software Used in this Report 
Software Name 
Version 
Software Tracking Number 
Reference 
iTOUGH2 
4.0 
10003-4.0-00 
LBNL 1999 [DIRS 139918] 
iTOUGH2 
5.0 
10003-5.0-00 
LBNL 2002 [DIRS 160106] 
GSLIB Module SISIM 
1.203 
10001-1.0MSISIMV1.203-00 
LBNL 1999 [DIRS 134136] 
GSLIB Module SISIM 
1.204 
10397-1.0SISIMV1.204-00 
LBNL 2000 [DIRS 153100] 
GSLIB Module GAMV2 
1.201 
10087-1.0MGAMV2V1.201-00 
LBNL 1999 [DIRS 134139] 
GSLIB Module GAMV3 
1.201 
10398-1.0GAMV3V1.201-00 
LBNL 2000 [DIRS 153099] 
EarthVision 
4.0 
10174-4.0-00 
Dynamic Graphics 2003 [DIRS 162369]
AddCoord 
1.0 
10355-1.0-00 
LBNL 2000 [DIRS 152814] 
MoveMesh 
1.0 
10358-1.0-00 
LBNL 2000 [DIRS 152824] 
AddBound 
1.0 
10357-1.0-00 
LBNL 2000 [DIRS 152823] 
Perm2Mesh 
1.0 
10359-1.0-00 
LBNL 2000 [DIRS 152826] 
CutNiche 
1.2 
10356-1.2-00 
LBNL 2000 [DIRS 152815] 
CutNiche 
1.3 
10402-1.3-00 
LBNL 2000 [DIRS 152828] 
CutDrift 
1.0 
10375-1.0-00 
LBNL 2000 [DIRS 152816] 
AddBorehole 
1.0 
10373-1.0-00 
LBNL 2000 [DIRS 152822] 
ECRB-XYZ 
.03 
30093-V.03 
CRWMS M&O 1999 [DIRS 147402] 
EXT 
1.0 
10047-1.0-00 
LBNL 1999 [DIRS 134141] 
The use of the software programs identified in Table 3-1 is documented in Section 6 and in the 
supporting scientific notebooks (SNs). A summary description of the programs and their use is 
given below. 
The software program iTOUGH2 V4.0 (LBNL 1999 [DIRS 139918]) provides forward and 
inverse modeling capabilities for unsaturated and multiphase flow in fractured porous media. 
The iTOUGH2 V5.0 (LBNL 2002 [DIRS 160106]) program hasamong other featuresthe 
extended capability of efficiently simulating evaporation effects [Requirements Document (RD) 
for iTOUGH2 V5.0-00 (BSC 2002 [DIRS 161067], Section 1.2)]. Both programs are used in this 
Model Report for simulating liquid-release experiments and predicting seepage rates. Moreover, 
they solve the inverse problem by automatically calibrating the model against measured data, and 
calculate prediction uncertainties for model validation. 
The GSLIB modules GAMV2 V1.201 and GAMV3 V1.201 (LBNL 1999 [DIRS 134139]; 
LBNL 2000 [DIRS 153099]) analyze spatial correlation of, respectively, two-dimensional (2-D) 
and three-dimensional (3-D), irregularly spaced datasets. These programs are used for the 
geostatistical analysis of air-permeability data. 

The GSLIB module SISIM V1.203 (LBNL 1999 [DIRS 134136]) generates 3-D spatially 
correlated random fields by means of sequential indicator simulations. It is used in this Model 
Report to generate spatially correlated fields of log-permeability modifiers. Module SISIM 
V1.204 (LBNL 2000 [DIRS 153100]) is an extended version of SISIM V1.203 (LBNL 1999 
[DIRS 134136]), in which coordinates are directly output along with the log-permeability 
modifiers, making the use of software program AddCoord V1.0 (see below; LBNL 2000 
[DIRS 152814]) unnecessary. 
The following utility programs support the generation of computational meshes. The software 
program MoveMesh V1.0 (LBNL 2000 [DIRS 152824]) adds a constant to the coordinates of a 
mesh file, translating the coordinate system. The software program AddBound V1.0 
(LBNL 2000 [DIRS 152823]) adds boundary elements to a mesh file. The software program 
AddCoord V1.0 (LBNL 2000 [DIRS 152814]) adds coordinates to the output file of SISIM 
V1.203 (LBNL 1999 [DIRS 134136]) in preparation for its use by the software program 
Perm2Mesh V1.0 (LBNL 2000 [DIRS 152826]), which maps a field of log-permeability 
modifiers onto a mesh file. The visualization software EarthVision V4.0 (Dynamic Graphics 
2003 [DIRS 162369]) is used to extract coordinates of the rough ceilings of Niches 3 
(also referred to as Niche 3107) and Niche 4 (also referred to as Niche 4788) in preparation for 
the use of the software program CutNiche V1.2 (LBNL 2000 [DIRS 152815]), which cuts a 
niche with a rough ceiling from a mesh file. The software program CutNiche V1.3 (LBNL 2000 
[DIRS 152828]) cuts a smooth niche from a mesh file. The software program CutDrift 
V1.0 (LBNL 2000 [DIRS 152816]) cuts a cylindrical drift from a mesh file. The software 
program AddBorehole V1.0 (LBNL 2000 [DIRS 152822]) inserts a borehole into a mesh file. 
The software program ECRB-XYZ V.03 (CRWMS M&O 1999 [DIRS 147402]) calculates the 
coordinates of a given ECRB station number, so the location of ECRB test beds can be related to 
the coordinates of the computational mesh. 
The software program EXT V1.0 (LBNL 1999 [DIRS 134141]) takes the forward output file 
from iTOUGH2 (V4.0 or V5.0) and converts it into a Tecplot (all versions, see Table 3-2) input 
file; this software is used for visualization purposes only. 
Table 3-2 summarizes the commercial off-the-shelf software used in support of this Model 
Report. This software is exempt from software qualification. Computations performed using the 
standard functions of the software products listed in Table 3-2 are described in the model 
documentation (Section 6) and the cited appendices. For visualization purposes, certain units 
have been converted using the equation utility of Tecplot. A factor of 1/86,400 was used to 
convert time from seconds to days; a factor of 1/60,000 was used to convert water flow rates 
from milliliter per minute (ml/min) to kilograms per second (kg/s), which implies a water density 
of 1 gram per milliliter (g/ml). Information needed to reproduce the work, including the input, 
formulae or algorithm, and output, is included in this Model Report and the cited references. 

Table 3-2. Software Products Exempt from Software Qualification 
Software Name 
Version 
Platform Information 
Used for 
Microsoft 
EXCEL 
97 (SR-2) 
PC, Windows 98 
Data reduction, computation, 
graphical representation of output 
2000 (9.0.3821 SR-1) 
PC, Windows 98 
2000 (9.0.3821 SR-1) 
PC, Windows 2000 Professional 
Microsoft 
WORD 
2000 (9.0.3821 SR-1) 
PC, Windows 98 
Word processing 
2000 (9.0.3821 SR-1) 
PC, Windows 2000 Professional 
vim 
6.0.12 
PC, Linux 
Text editing 
Adobe Illustrator 
V8.0.1 
Mac, MacOS 9.0.4 
Schematic figures 
Microsoft 
PowerPoint 
2000 (9.0.3821 SR-1) 
PC, Windows 98 
Tecplot 
8.0-1-0 
Sun, SunOS 5.5.1 
Technical figures 
8.0-0-6 
PC, Windows 98 
7.5 
PC, Windows 98 
9.0-3-0 
PC, Windows 2000 Professional 
Exceed 
V6.1/V5.3 
PC, Windows 98 
Communication and file transfer 
between PC and Unix workstation 
F-Secure 
V5.1 (Build 21) 
PC, Windows 2000 Professional 

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4. INPUTS 
4.1 DIRECT INPUT 
Input data and parameters needed for the development of the Seepage Calibration Model (SCM) 
are obtained from the Technical Data Management System (TDMS). As stated in Section 1, the 
SCM is used to estimate seepage-relevant parameters through model calibration. In general, 
calibration is a process of fixing certain parameters considered known, relatively certain, or 
insensitive, and adjusting others that are unknown, uncertain, or highly sensitive to minimize the 
misfit between measured data and model output. Input data were measured in or refer to the 
middle nonlithophysal and the lower lithophysal zones of the Topopah Spring welded unit 
(the repository units). Appropriate data for the middle nonlithophysal zone have been measured 
in Niches 2, 3, and 4, and appropriate data for the lower lithophysal zone have been measured in 
Niche 5 and in boreholes SYBT-ECRB-LA#1#3 drilled into the ceiling of the ECRB Cross-
Drift. Specific input data sets and the associated Data Tracking Numbers (DTNs) are listed in 
Table 4-1; specific input parameters are listed in Table 4-2; Technical Product Output (TPO) 
used as input to calculate local percolation fluxes is summarized in Table 4-3. These data and 
parameters are considered appropriate as input for the development of the SCM for the following 
reasons: 
1. Profile alignments and borehole (BH) survey information (Table 4-1). These survey 
data are accurate and thus considered appropriate as a basis for defining niche 
geometry and identifying injection elements in the numerical mesh. 
2. Air-permeability data (Table 4-1). These data are used as a basis for the geostatistical 
analysis and generation of spatially correlated permeability fields near the niches and 
the ECRB Cross-Drift. The data are location-specific and on the appropriate scale, 
and thus suitable for representing the local rock properties and the structure of sub-
drift-scale heterogeneities. 
3. Liquid-release test data (Table 4-1). These data are used for calibration and validation 
of the SCM. Liquid-release test data are appropriate for the calibration of the SCM 
and the estimation of seepage-relevant parameters, because they reflect the salient 
processes and features affecting seepage. Moreover, they are taken on a representative 
scale comparable to that of a waste emplacement drift. 
4. Calibrated drift-scale fracture properties for the middle nonlithophysal and lower 
lithophysal zone of the Topopah Spring welded unit. Because they are directly 
measured or derived from data collected at Yucca Mountain, these fracture parameters 
are considered appropriate to be used as reference input parameters. Only the 
parameters that are fixed during an inversion, and for which no location-specific data 
are available, are needed as input; this subset is summarized in Table 4-2. Because of 
their small sensitivity on predicted seepage rates (see Section 6.6.3.1), a minor change 
in any of these input parameters has a negligible impact on the estimated model 
parameters or the conclusions of this Model Report. 
5. Coordinates of the Unsaturated Zone Flow and Transport Model (UZ Model) grid and 
calculated flow rates for extraction of background percolation flux (Table 4-3). In the 

absence of direct observations of percolation flux, the percolation fluxes calculated by 
the UZ Flow Model, which is based on site-specific data, are considered appropriate 
for their intended use in the SCM. 
Table 4-1. Input Data 
DTNa
Data Description 
Niche Geometry 
MO0003GSC00096.000 [DIRS 152167] 
ESF Niche 2 (Niche 3650) profile alignment 
MO0002GSC00076.000 [DIRS 152623] 
ESF Niche 2 (Niche 3650) borehole as-built information 
MO0003GSC00103.000 [DIRS 152176] 
ESF Niche 3 (Niche 3107) profile alignment 
MO0002GSC00064.000 [DIRS 152625] 
ESF Niche 3 (Niche 3107) borehole as-built information 
MO0008GSC00273.000 [DIRS 152626] 
ESF Niche 4 (Niche 4788) profile alignment 
MO0107GSC01069.000b [DIRS 156941] 
ESF Niche 4 (Niche 4788) borehole as-built information 
MO0009GSC00332.000 [DIRS 155370] 
ECRB Niche 5 (Niche 1620) profile survey data 
MO0107GSC01061.000 [DIRS 155369] 
ECRB Niche 5 (Niche 1620) slot survey data 
MO0312GSC03176.000 [DIRS 169532] 
ECRB Niche 5 (Niche 1620) survey data for collars, bottoms, and 
intervals 
LB0301N5CEILNG.001 [DIRS 161733] 
ECRB Niche 5 (Niche 1620) detailed niche ceiling roughness data 
Air-Permeability Data 
LB0011AIRKTEST.001 [DIRS 153155] 
Air permeability data from ESF Niche 2 (Niche 3650) 
LB990601233124.001 [DIRS 105888] 
Air permeability data from ESF Niche 3 (Niche 3107) and Niche 4 
(Niche 4788) 
LB0110AKN5POST.001 [DIRS 156904] 
Air permeability data from ECRB Niche 5 (Niche 1620) 
LB00090012213U.001 [DIRS 153141] 
Air permeability data from ECRB borehole SYBT-ECRB-LA#2 
Liquid-Release Test Data 
LB0010NICH3LIQ.001 [DIRS 153144] 
Liquid-release test data from ESF Niche 3 (Niche 3107), March 1999 
LB0010NICH4LIQ.001 [DIRS 153145] 
Liquid-release test data from ESF Niche 4 (Niche 4788), Nov. 1999 
LB0207NICH5LIQ.001 [DIRS 160408] 
Liquid-release test data from ECRB Niche 5 (Niche 1620), June 2000 
LB0209NICH5LIQ.001 [DIRS 160796] 
Liquid-release test data from ECRB Niche 5 (Niche 1620), June 2002 
LB0211NICH5LIQ.001 [DIRS 160792] 
Liquid-release test data from ECRB Niche 5 (Niche 1620), August 2002 
LB0110ECRBLIQR.002 [DIRS 156879] 
Liquid-release test data from ECRB borehole SYBT-ECRB-LA#1, Feb. 
2001 
LB00090012213U.002 [DIRS 153154] 
Liquid-release test data from ECRB borehole SYBT-ECRB-LA#2, May 
2000 
LB0110SYST0015.001 [DIRS 160409] 
Liquid-release test data from ECRB borehole SYBT-ECRB-LA#2, Oct. 
2000 
LB0203ECRBLIQR.001 [DIRS 158462] 
Liquid-release test data from ECRB borehole SYBT-ECRB-LA#3, May 
2001 
a Traceability to the specific information extracted from these DTNs is given in the appendices and cited Scientific 
Notebooks. 
b This DTN superseded MO0008GSC00310.000 [DIRS 152627], which was the source for borehole coordinates 
available at the time of model development for Niche 4. Borehole coordinates in both DTNs are identical, i.e., 
there is no impact on the models, analyses, and conclusions presented in this Model Report. 
DTN=Data Tracking Number 

Table 4-2. Hydrogeologic Input Parameters 
DTN 
Parameter 
Value 
Units 
Middle Nonlithophysal Zone of Topopah Spring Welded Unit (Fracture Parameter for tsw34) 
LB997141233129.001a [DIRS 104055] 
van Genuchten parameter, m 
0.608 
[dimensionless] 
LB997141233129.001a [DIRS 104055] 
Residual liquid saturation, Slr
0.01 
[dimensionless] 
LB997141233129.001a [DIRS 104055] 
Satiated saturation, Sls
1.00 
[dimensionless] 
Lower Lithophysal Zone of Topopah Spring Welded Unit (Fracture Parameters for tsw35) 
LB0205REVUZPRP.001 [DIRS 159525] 
Porosity 
0.96 
[%] 
LB997141233129.001a [DIRS 104055] 
van Genuchten parameter, m 
0.611 
[dimensionless] 
LB997141233129.001a [DIRS 104055] 
Residual liquid saturation, Slr
0.01 
[dimensionless] 
LB997141233129.001a [DIRS 104055] 
Satiated saturation, Sls
1.00 
[dimensionless] 
a The superceded fracture parameters of DTN: LB997141233129.001 [DIRS 104055] [which is a qualified 
product output from a previous revision of the Calibrated Properties Model (CRWMS M&O 2000 [DIRS 
144426])] are suitable for their intended use within this Model Report. The superceded and superceding 
values are identical with the exception of the van Genuchten parameter m; the superceding value for both 
units is 0.633. The difference between the superceded and superceding values are inconsequential for the 
estimation of drift seepage, because (1) the sensitivity of seepage to the m parameter (or the related n 
parameter) is very limited (as discussed in Section 6.6.3.1), and (2) consistent values are used in the 
calibration and prediction models. Moreover, the superceded values, which originated from a reliable source, 
have been used in previous analyses of flow, transport, and seepage for the same units; the superceded value 
is considered pertinent to the property of interest. The values were superceded because (1) the numerical grid 
was modified and (2) a new inversion methodology was employed. 
DTN=Data Tracking Number 
Table 4-3. Mesh Coordinates and Flow Field Used to Calculate Local Percolation Flux 
DTN 
TPO Description 
LB990701233129.001a [DIRS 106785] 
3-D UZ model grid, including coordinates 
LB990801233129.003 a [DIRS 122757] 
Calculated percolation flux, flow field #3 
a The calculated percolation fluxes from the superceded DTN: LB990801233129.003 [DIRS 122757] (which are 
based on the related numerical grid contained in the superceded DTN: LB990701233129.001 [DIRS 106785]) 
are considered suitable for the intended use in this Model Report, because (1) the estimated parameters, 
output, and conclusions presented in this Model Report are insensitive to the specified background 
percolation flux, (2) the superceded data originated from a reliable source, and (3) the superceded data have 
been used in previous analyses of flow, transport, and seepage in the unsaturated zone, i.e., they are 
pertinent to the properties of interest. The UZ Flow model providing percolation fluxes was revised (1) to 
accommodate a new repository design (requiring a new numerical grid), (2) to incorporate revised property 
sets, and (3) to employ a finer vertical discretization of the PTn hydrogeologic unit. The flow fields calculated 
with the revised UZ Flow model (see DTN: LB03023DSSCP9I.001 [DIRS 163044]) yield local percolation 
fluxes at Niches 3650, 3107, 4788, and 1620 of 4.0, 4.7, 2.4, and 6.2 mm/year, respectively. Given the low 
sensitivity of the estimated parameters to the background percolation flux (see discussion in Section 6.6.2.3), 
the differences between these and the superceded values are inconsequential. 
3-D=three-dimensional: DTN=Data Tracking Number; UZ=Unsaturated Zone; PTn=Paintbrush nonwelded tuff; 
TPO=Technical Product Output 
Equations are discussed in the context of model development in Section 6 with appropriate 
citations to their sources. The collection of the input data used for the development and 
calibration of the SCM is described in detail in the report In Situ Field Testing of Processes 
(BSC 2004 [DIRS 170004], Sections 6.2 and 6.11) and is summarized in Section 6.5. The 
analysis of the seepage-rate data is described in Section 6.6.3. Uncertainties in the input data and 
parameters are addressed throughout Section 6 and are summarized in Section 8.2. 

4.2 CRITERIA 
The licensing criteria for postclosure performance assessment are stated in 10 CFR 63 
[DIRS 156605]. The requirements to be satisfied by TSPA are identified in the Yucca Mountain 
Project Requirements Document (Canori and Leitner 2003 [DIRS 166275]). The acceptance 
criteria that will be used by the U.S. Nuclear Regulatory Commission (NRC) to determine 
whether the technical requirements have been met are identified in Yucca Mountain Review Plan, 
Final Report (YMRP; NRC 2003 [DIRS 163274]). The pertinent requirements and criteria for 
this Model Report are summarized in Table 4-4. Section 8.5 provides cross-references to 
demonstrate how the acceptance criteria are addressed. 
Table 4-4. Project Requirements and Yucca Mountain Review Plan Acceptance Criteria Applicable to 
this Model Report 
Requirement 
Number 
Requirement Title 
10 CFR 63 Link 
YMRP Acceptance Criteria 
PRD-002/T-015 
(Canori and 
Leitner 2003 
[DIRS 166275]) 
Requirements for 
Performance Assessment 
10 CFR 63.114 
(a-c) 
[DIRS 156605] 
Criteria 1 to 4 for Quantity and Chemistry of 
Water Contacting Waste Packages and 
Waste Forms (NRC 2003 [DIRS 163274], 
Section 2.2.1.3.3.3). 
Criteria 1 to 4 for Flow Path in the 
Unsaturated Zone (NRC 2003 [DIRS 
163274], Section 2.2.1.3.6.3). 
YMRP=Yucca Mountain Review Plan 
Where a subcriterion includes several components, only some of those components may be 
addressed. How these components are addressed is summarized in Section 8.5. 
The acceptance criteria identified in Section 2.2.1.3.3.3 of the YMRP (NRC 2003 [DIRS 
163274]) are given below. 
Section 2.2.1.3.3.3, Quantity and Chemistry of Water Contacting Waste Packages and Waste 
Forms 
Acceptance Criterion 1, System Description and Model Integration are Adequate: 
Subcriterion (2): The abstraction of the quantity and chemistry of water contacting engineered 
barriers and waste forms uses assumptions, technical bases, data, and models, 
that are appropriate and consistent with other related U.S. Department of 
Energy abstractions. For example, the assumptions used for the quantity and 
chemistry of water contacting engineered barriers and waste forms are 
consistent with the abstractions of Degradation of Engineered Barriers 
(Section 2.2.1.3.1); Mechanical Disruption of Engineered Barriers 
(Section 2.2.1.3.2); Radionuclide Release Rates and Solubility Limits 
(Section 2.2.1.3.4); Climate and Infiltration (Section 2.2.1.3.5); and Flow 
Paths in the Unsaturated Zone (Section 2.2.1.3.6). The descriptions and 
technical bases provide transparent and traceable support for the abstraction of 
quantity and chemistry of water contacting engineered barriers and waste 
forms. 

Subcriterion (8): Adequate technical bases are provided, including activities such as 
independent modeling, laboratory or field data, or sensitivity studies, for 
inclusion of any thermal-hydrologic-mechanical-chemical couplings and 
features, events, and processes 
Acceptance Criterion 2, Data are Sufficient for Model Justification: 
Subcriterion (1): Geological, hydrological, and geochemical values used in the license 
application are adequately justified. Adequate description of how the data 
were used, interpreted, and appropriately synthesized into the parameters is 
provided. 
Subcriterion (2): Sufficient data were collected on the characteristics of the natural system and 
engineered materials to establish initial and boundary conditions for 
conceptual models of thermal-hydrologic-mechanical-chemical coupled 
processes, that affect seepage and flow and the engineered barrier chemical 
environment. 
Acceptance Criterion 3, Data Uncertainty is Characterized and Propagated Through the 
Model Abstraction: 
Subcriterion (1): Models use parameter values, assumed ranges, probability distributions, and 
bounding assumptions that are technically defensible, reasonably account for 
uncertainties and variabilities, and do not result in an under-representation of 
the risk estimate. 
Subcriterion (2): Parameter values, assumed ranges, probability distributions, and bounding 
assumptions used in the total system performance assessment calculations of 
quantity and chemistry of water contacting engineered barriers and waste 
forms are technically defensible and reasonable, based on data from the Yucca 
Mountain region (e.g., results from large block and drift-scale heater and 
niche tests), and a combination of techniques that may include laboratory 
experiments, field measurements, natural analog research, and process-level 
modeling studies. 
Subcriterion (4): Adequate representation of uncertainties in the characteristics of the natural 
system and engineered materials is provided in parameter development for 
conceptual models, process-level models, and alternative conceptual models. 
The U.S. Department of Energy may constrain these uncertainties using 
sensitivity analyses or conservative limits. For example, the U.S. Department 
of Energy demonstrates how parameters used to describe flow through the 
engineered barrier system bound the effects of backfill and 
excavation-induced changes. 
Acceptance Criterion 4, Model Uncertainty is Characterized and Propagated Through the 
Model Abstraction: 
Subcriterion (1): Alternative modeling approaches of features, events, and processes are 
considered and are consistent with available data and current scientific 

understanding, and the results and limitations are appropriately considered in 
the abstraction. 
Subcriterion (2): Alternative modeling approaches are considered and the selected modeling 
approach is consistent with available data and current scientific understanding. 
A description that includes a discussion of alternative modeling approaches 
not considered in the final analysis and the limitations and uncertainties of the 
chosen model is provided. 
Subcriterion (3): Consideration of conceptual model uncertainty is consistent with available site 
characterization data, laboratory experiments, field measurements, natural 
analog information and process-level modeling studies; and the treatment of 
conceptual model uncertainty does not result in an under-representation of the 
risk estimate. 
The acceptance criteria identified in Section 2.2.1.3.6.3 of the YMRP (NRC 2003 
[DIRS 163274]) are given below. 
Section 2.2.1.3.6.3, Flow Paths in the Unsaturated Zone 
Acceptance Criterion 1, System Description and Model Integration are Adequate: 
Subcriterion (1): The total system performance assessment adequately incorporates important 
design features, physical phenomena, and couplings, and uses consistent and 
appropriate assumptions throughout the flow paths in the unsaturated zone 
abstraction process. Couplings include thermal-hydrologic-mechanical-
chemical effects, as appropriate. 
Subcriterion (2): The aspects of geology, hydrology, geochemistry, physical phenomena, and 
couplings that may affect flow paths in the unsaturated zone are adequately 
considered. Conditions and assumptions in the abstraction of flow paths in 
the unsaturated zone are readily identified and consistent with the body of data 
presented in the description. 
Subcriterion (6): Adequate spatial and temporal variability of model parameters and boundary 
conditions are employed in process-level models to estimate flow paths in the 
unsaturated zone, percolation flux, and seepage flux. 
Subcriterion (7): Average parameter estimates used in process-level models are representative of 
the temporal and spatial discretizations considered in the model. 
Acceptance Criterion 2, Data are Sufficient for Model Justification: 
Subcriterion (1): Hydrological and thermal-hydrological-mechanical-chemical values used in 
the license application are adequately justified. Adequate descriptions of how 
the data were used, interpreted, and appropriately synthesized into the 
parameters are provided. 

Subcriterion (5): Sensitivity or uncertainty analyses are performed to assess data sufficiency, 
and verify the possible need for additional data. 
Subcriterion (6): Accepted and well-documented procedures are used to construct and calibrate 
the numerical models. 
Subcriterion (7): 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 of results from different mathematical models is compared. 
Acceptance Criterion 3, Data Uncertainty is Characterized and Propagated Through the 
Model Abstraction: 
Subcriterion (1): Models use parameter values, assumed ranges, probability distributions, and 
bounding assumptions that are technically defensible, reasonably account for 
uncertainties and variables, and do not result in an under-representation of the 
risk estimate. 
Subcriterion (4): 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. 
Subcriterion (6): Uncertainties in the characteristics of the natural system and engineered 
materials are considered. 
Acceptance Criterion 4, Model Uncertainty is Characterized and Propagated Through the 
Model Abstraction: 
Subcriterion (1): Alternative modeling approaches of features, events, and processes, consistent 
with available data and current scientific understanding, are investigated. The 
results and limitations are appropriately considered in the abstraction. 
4.3 CODES, STANDARDS, AND REGULATIONS 
No specific, formally established standards have been identified as applying to this modeling 
activity. 

INTENTIONALLY LEFT BLANK 

5. ASSUMPTIONS 
This section contains a list of assumptions used for the development of the Seepage Calibration 
Model (SCM). Each statement of an assumption is immediately followed by the rationale for 
why the assumption is considered valid or reasonable. Assumptions in immediately preceding 
upstream documentations have no significant impact on the results of the present model or they 
are discussed in the following subsections. 
5.1 CONTINUUM APPROACH 
Assumption: The continuum approach is assumed to be a valid concept to calculate percolation 
flux and drift seepage at Yucca Mountain. 
Rationale: The continuum approach can be considered appropriate (1) if it appropriately 
represents the key features and processes determining seepage into large underground openings, 
and (2) if it is capable of reproducing and predicting seepage rates into a drift in a fractured 
formation. 
As discussed in detail in Section 6.3.2, diversion of water around an underground opening on 
account of the capillary barrier effect predominantly occurs within fracture planes that are 
oriented approximately perpendicular to the drift axis. Flow within a fracture plane (or a 
collection of fracture planes) can be described by a continuum model with a heterogeneous 
permeability field. 
As demonstrated in Sections 6.6.3 and 7.2.2.1, a continuum model is capable of reproducing and 
predicting seepage rates into a drift section, i.e., on the scale of interest. The continuum 
approach is therefore considered appropriate for seepage studies if applied within the framework 
described in this Model Report. Inverse modeling should be used for the estimation of process-
specific, model-related, and scale-dependent parameters, and the same or similar conceptual 
model should be used for the subsequent seepage predictions, specifically the SMPA. 
No further confirmation is required for this assumption, which is used throughout Sections 6 
and 7. 
5.2 UNSATURATED FLOW 
Assumption: Water flow under unsaturated conditions is assumed to be governed by Richards 
equation [Capillary Conduction of Liquids Through Porous Mediums (Richards 1931 
[DIRS 104252], pp. 318333)]. 
Rationale: This assumption is justified because (1) gravitational force is ubiquitous, and (2) 
rough-walled or partially filled fractures exert varying degrees of capillary pressure at different 
saturation levels. The constant of proportionalityrelative permeabilityis saturation-
dependent because (1) porous-medium continuum laws also apply to water flow through 
fractures filled with porous material, and (2) in the absence of fracture fillings, the thickness of 
the water film and connectivity of liquid islands on the fracture surface are saturation dependent 
[Water Film Flow Along Fracture Surfaces of Porous Rock. (Tokunaga and Wan 1997 
[DIRS 139195], pp. 12871295). 

Richards equation follows from (1) the continuity equation and (2) the Buckingham-Darcy 
equation [Dynamics of Fluids in Porous Media (Bear, 1972 [DIRS 156269], pp. 487502)]. 
Richards equation states that isothermal flow of water in a porous medium or rough-walled 
fracture occurs under the combined effect of gravitational and capillary forces, that flow 
resistance is a function of saturation, and that, for the purposes of this representation, movement 
of the nonwetting air phase can be neglected. 
This general concept, which is further discussed in Section 6.6.1.1 and used throughout Sections 
6 and 7, is reasonable for unsaturated water flow through both porous matrix as well as partially 
filled or rough-walled fractures and does not require further confirmation. 
5.3 CHARACTERISTIC CURVES 
Assumption: Relative permeability and capillary pressure are assumed to be described as 
continuous functions of effective liquid saturation, following the expressions given by the van 
Genuchten-Mualem model [A Closed-Form Equation for Predicting the Hydraulic Conductivity 
of Unsaturated Soils. (van Genuchten 1980 [DIRS 100610], pp. 892893)]. 
Rationale: The van Genuchten-Mualem model is the standard model used in the suite of UZ 
models; it was chosen in this work to ensure consistency. Furthermore, the applicability of 
relative permeability and capillary pressure functions is consistent with the continuum 
assumption (see Sections 5.1 and 6.6.1.1) and is appropriate to represent fractures that are rough-
walled and/or partially filled with porous material. The calibration process and the consistent 
conceptualization in the downstream models (specifically the SMPA) make this assumption a 
valid approach. 
No further confirmation is required for this assumption, which is used throughout Sections 6 
and 7. 
5.4 EFFECTIVE CAPILLARY-STRENGTH PARAMETER 
Assumption: The effective, seepage-relevant capillary-strength parameter to be estimated for 
each test location is assumed to be spatially uniform on the drift scale and thus not correlated to 
the small-scale heterogeneous permeability field. 
Rationale: The capillary-strength parameter to be estimated by calibration of the model against 
seepage-rate data is considered an effective parameter that includes a number of seepage-relevant 
features and processes, such as (1) the continuum capillarity of a network of rough-walled 
fractures, (2) capillary rise within finite fracture segments intersected by the underground 
opening, (3) small-scale drift-wall roughness (including effects of lithophysal cavities; see 
Section 5.7), and (4) capillary adsorption of water along drift wall leading to film flow. 
The capillary strength of the fracture system is correlated to the fracture aperture distribution. 
Similarly, permeability may be correlated to aperture, suggesting that capillarity and 
permeability are (negatively) correlated. However, given that these parameters describe 
continuum properties of a fracture network (rather than those of a single fracture), it should be 
noted that an increase in permeability might be associated with an increase in fracture density 

(rather than an increase in aperture). An increase in fracture density does not affect capillarity. 
Consequently, capillarity and permeability are not necessarily correlated. 
Items (2) through (4) are features and processes related to capillarity, and are thus well 
represented by a capillary-strength parameter; however, they are not related to permeability. 
Finally, since capillary strength is an effective parameter estimated by inverse modeling for a 
given conceptual model, its value is appropriate for use in a prediction model that has the same 
model structure, i.e., that uses the same assumption regarding the uniformity of this parameters. 
Given that (1) capillarity and permeability are not necessarily correlated, (2) seepage-relevant 
features and processes not related to permeability are represented by the capillary-strength 
parameter, and (3) the effective parameter is estimated and used within a suite of conceptually 
consistent models, it is appropriate to consider the capillary-strength parameter uniform on the 
drift scale and not correlated to the small-scale heterogeneous permeability field. This 
assumption, which is used throughout Sections 6 and 7, does not require further confirmation. 
5.5 EVAPORATION IN CLOSED-OFF NICHES 
Assumption: The effect of evaporation on the seepage rates observed in closed-off niches in the 
middle nonlithophysal zone is assumed to be insignificant, i.e., water removal from the 
formation, at the drift surface, and from the capture system by evaporation and vapor diffusion is 
assumed to be small. 
Rationale: Under isothermal conditions, potential evaporation at the wall or in the capture system 
of a closed-off and humidified niche is small compared to the amount of water being released. 
Seepage experiments in the middle nonlithophysal zone of the Topopah Spring welded unit were 
conducted in niches that were closed off by a bulkhead, which leads to comparatively high 
relative humidity and low air circulation. Moreover, a humidifier was used in some of the 
experiments to ensure high relative humidity. For these conditions, Ho [Evaporation of Pendant 
Water Droplets in Fractures. (1997 [DIRS 141521])] and Or and Ghezzehei [Dripping into 
Subterranean Cavities from Unsaturated Fractures under Evaporative Conditions. 
(2000 [DIRS 144773])] provide a detailed description of evaporation mechanisms on the scale of 
individual water droplets within fractures or emerging from fractured formations. The 
evapo-infiltration threshold calculated by Ho (1997 [DIRS 141521], p. 2670) is significantly 
lower than the applied injection rates, suggesting a very minor influence of evaporation on 
measured seepage rates in experiments conducted in the niches. 
Evaporation effects were included in the modeling of liquid-release tests performed in Niche 5, 
which exhibited relative humidity conditions slightly lower than those expected to have prevailed 
in Niches 2, 3, and 4. The impact of evaporation on seepage rates and thus on the estimation of 
seepage-relevant parameters is minor (as demonstrated in Section 6.7), confirming that 
neglecting evaporation effects in closed-off niches is appropriate. 
No further confirmation is required for this assumption, which is used in Sections 6.6.3 and 
7.2.2.1. Note that evaporation effects in the open, ventilated ECRB Cross-Drift are considered 
significant and are taken into account in the model based on relative humidity and evaporation 
rate measurements (see Sections 5.6, 6.5.4, and 6.6.1.4). 

5.6 EVAPORATION IN OPEN DRIFT 
Assumption: Evaporation from the drift surface is assumed to be governed by one-dimensional 
vapor diffusion across an evaporative boundary layer, the thickness of which can be estimated 
from measurements of relative humidity and evaporation rate from a free water surface. 
Rationale: As water injected during a liquid-release test reaches the opening, it spreads along the 
surface on account of capillarity within the rough surface. As a result, water potentially seeping 
into the opening may not only form droplets or lines along fracture traces with a small surface 
area, but may spread across the drift surface over a relatively large area. This phenomenon is 
qualitatively confirmed by the geometry of the wet spot observed at the niche ceiling during 
seepage experiments [(BSC 2004 [DIRS 170004], Section 6.2.1.3.4, Figure 6.2.1-7); 
[Seepage into an Underground Opening Constructed in Unsaturated Fractured Rock under 
Evaporative Conditions. (Trautz and Wang 2002 [DIRS 160335], Figures 7 and 9)]. The 
geometry of the wet spot does not have a clear correlation with the visible fractures traces. Even 
though water first appears along fracture traces (Trautz and Wang (2002 [DIRS 160335], Figure 
10), the wet spot grows in an areal fashion. The short arrival time and the average speed at 
which the leading edge of the plume moves across the ceiling makes it obvious that the water is 
not transmitted through the matrix, but spreads along the ceiling as a surface film, possibly 
supported by flow through microfractures. Evaporation from such wet areas is similar to 
evaporation from a free water surface, where the evaporation rate is governed by one-
dimensional vapor diffusion across a relatively thin boundary layer of linearly decreasing vapor 
concentration. A detailed description of the corresponding conceptual and mathematical model 
and the estimation of the evaporation boundary-layer thickness is given in Sections 6.6.1.3 and 
6.6.1.4. 
No further confirmation is required for this assumption, which is used in Sections 6.6.3 and 
7.2.2.1. 
5.7 LITHOPHYSAL CAVITIES 
Assumption: The impact of lithophysal cavities on seepage is assumed to be appropriately 
captured in the estimation of an effective capillary-strength parameter. 
Rationale: The impact of lithophysal cavities on flow and seepage is twofold: (1) lithophysal 
cavities are essentially obstacles to water flow because they act as capillary barriers, focusing the 
water that flows around them; (2) lithophysal cavities intersected by the drift lead to a rough drift 
wall, potentially creating seepage points at local topographic lows. Both effects tend to promote 
seepage. 
The assumption states that the effect of lithophysal cavities on seepage can be captured through 
the estimation of an effective capillary-strength parameter, making the explicit inclusion of 
lithophysal cavities into the process model unnecessary. This approach is considered appropriate 
for the following reasons: (1) omitting lithophysal cavities in the process model used for inverse 
modeling yields lower estimates of the capillary-strength parameter and is thus conservative; 
(2) consistency between the calibration model (the SCM) and the prediction model (the SMPA) 
removes the impact of a potential estimation bias; (3) the approach followed allows for the 

development of a single SMPA conceptual model for both the middle nonlithophysal and lower 
lithophysal zones, yielding a single look-up table for TSPA to sample from; and (4) explicit 
modeling of lithophysal cavities is not warranted because of insufficient information regarding 
their location, shape, and frequency. 
No further confirmation is required for this assumption, which is used in Sections 6.6.3 and 
7.2.2.1. 

INTENTIONALLY LEFT BLANK 

6. MODEL DISCUSSION 
6.1 MODELING OBJECTIVES AND DEFINITIONS 
6.1.1 Objectives 
The following sections describe the development, calibration, and validation of the Seepage 
Calibration Model (SCM). The purpose of the SCM is to provide a methodological and 
conceptual basis for the subsequent development of the Seepage Model for Performance 
Assessment (SMPA). Furthermore, seepage-relevant parameters are derived as input to the 
abstraction for drift seepage. 
The seepage models are not expected to accurately predict individual seepage events or the 
precise spatial distribution along the drift. Instead, the seepage models are intended to provide 
estimates of the seepage flux averaged over a 5 m drift segment (the approximate length of a 
waste package) as a function of the percolation flux on the drift scale. The seepage experiments 
and modeling approach are designed to address seepage on this specific scale. 
A list of the data corroborating and supporting the SCM (including the corresponding source 
DTNs) is provided in Table 6-5 below. 
6.1.2 Definitions 
Seepage is defined as flow of liquid water into an underground opening such as a niche, the 
ECRB Cross-Drift, or a waste emplacement drift; the water originates from the rock mass and 
forms drops that subsequently detach from the opening surface. According to this definition, 
seepage does not include advective or diffusive vapor flow into the opening or condensation of 
water vapor on surfaces, which may lead to drop formation and drop detachment. Some of the 
water entering an underground opening may also evaporate or flow along the wall, thus not 
contributing to seepage in the narrow sense defined here. Note, however, that evaporation, 
condensation, and film flow along the surface of the opening affect the moisture conditions in the 
waste emplacement drift and may thus impact repository performance. 
Seepage rate is the amount of water seeping into the opening per unit of time. 
Seepage flux is defined as the seepage rate per unit area of the projected drift outline. 
Seepage percentage is defined as the ratio of seepage flux divided by percolation flux. As 
outlined in Section 6.1.1, a five-meter long drift section (the approximate length of a waste 
package) is used as the reference scale for calculating percolation and seepage fluxes. In the 
context of liquid-release tests, seepage percentage is the ratio of the rate or amount of water that 
seeped into the niche divided by the rate or amount of water released. 
Seepage threshold is defined here as the critical percolation flux below which no seepage occurs, 
i.e., all percolating water is diverted around the opening, evaporates, or flows along the drift 
surface as a thin water film. Note that Philip et al. [Unsaturated Seepage and Subterranean 
Holes: Conspectus, and Exclusion Problem for Circular Cylindrical Cavities. (1989 

[DIRS 105743])] did not consider evaporation and film-flow effects when defining the critical 
seepage conditions. 
Seepage fraction is defined as the fraction of waste packages affected by seepage. This is 
equivalent to the fraction of 5 m drift sections that exhibit a nonzero seepage percentage. 
Capillary barrier is a technical term used to describe the fact that water is diverted around an 
underground opening, preventing seepage or reducing the seepage flux below the incident 
percolation flux. This technical definition of a barrier is different from regulatory definition of 
10 CFR 63.2 [DIRS 156605]. However, the usage of the term in the context of the capillary 
barriers discussed in this Model Report is unambiguous. 
6.1.3 Scientific Notebooks 
The scientific notebooks (SN) listed in Table 6-1 provide details potentially needed to reproduce 
the modeling work discussed in this Model Report. 
Table 6-1. Scientific Notebooks 
LBNL Scientific 
Notebook ID 
M&O Scientific 
Notebook ID 
Relevant Pages 
Citation 
YMP-LBNL-SAF-1 
SN-LBNL-SCI-087-V1 
14, 100102, 139 
Finsterle 1999 [DIRS 153448] 
YMP-LBNL-SAF-2 
SN-LBNL-SCI-171-V1 
12, 3442, 4795 
Finsterle 2002 [DIRS 161043] 
YMP-LBNL-SAF-3 
SN-LBNL-SCI-228-V1 
126, 3137 
Wang 2003 [DIRS 161456] 
YMP-LBNL-SAF-TG-1 
SN-LBNL-SCI-223-V1 
944 
Wang 2003 [DIRS 161456] 
YMP-LBNL-RCT-DSM-1 
SN-LBNL-SCI-157-V1 
137 
Trautz 2001 [DIRS 161044] 
YMP-LBNL-RCT-2 
SN-LBNL-SCI-156-V1 
3545 
Trautz 2001 [DIRS 156903] 
YMP-LBNL-JSW-6C 
SN-LBNL-SCI-122-V1 
108123 
Wang 1999 [DIRS 153449] 
YMP-LBNL-DSM-CFA-1 
SN-LBNL-SCI-180-V1 
46, 810, 13, 1558 
Ahlers 2002 [DIRS 161045] 
YMP-LBNL-YSW-JH-2 
SN-LBNL-SCI-143-V1 
124 
Hinds 2001 [DIRS 155955] 
YMP-LBNL-RCT-RH-1 
SN-LBNL-SCI-175-V1 
2729 
Hedegaard 2002 [DIRS 161046] 
LBNL=Lawrence Berkeley National Laboratory 
6.2 FEATURES, EVENTS, AND PROCESSES 
Table 6-2 contains a list of FEPs taken from the LA FEP List (DTN: MO0407SEPFEPLA.000 
[DIRS 170760]). The selected FEPs are those taken from the LA FEP List that are associated 
with the subject matter of this report (BSC 2004 [DIRS 169654], Table 2.1.5-1). 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 2003 
[DIRS 166296], Section 3.2.2). The cross-reference for each FEP to the relevant sections of this 
report is also given in Table 6-2. 

Table 6-2. FEPs Addressed in this Model Report 
FEP No. 
FEP Name 
Relevant Sections of this AMR 
1.2.02.01.0A 
Fractures 
Air-permeability and seepage testing as well as the heterogeneous 
fracture continuum model characterize and account for flow through and 
seepage from fractures (see Sections 6.3.2, 6.3.3.2, and 6.5) 
2.1.08.02.0A 
Enhanced influx at 
the repository 
The impact of an underground opening on the unsaturated flow field 
(including dry-out from evaporation, capillary-barrier effect, and flow 
diversion around the drift) is captured in the seepage process model by 
solving the equations governing unsaturated flow in fractured porous 
media and by specifying appropriate boundary conditions at the drift wall. 
It leads to reduced (not enhanced) influx (see Sections 6.3, 6.3.2, 6.3.3, 
6.6, and 6.8). 
2.2.01.01.0A 
Mechanical effects of 
excavation/ construc-
tion in the near field 
Excavation effects are taken into account through the use of post-
excavation air-permeability data and the estimation of a capillary-strength 
parameter determined from seepage data that reflect seepage from an 
excavation-disturbed zone around a large opening (see Sections 6.3.3.2, 
6.3.4, 6.5.2, 6.6.3.1, and 6.6.3.3). 
2.2.03.02.0A 
Rock properties of 
host rock and other 
units 
Location-specific rock properties are (1) taken from supporting reports 
(see Table 4-2), (2) determined from local air-permeability data (including 
measures of heterogeneity and spatial correlation), and (3) determined 
through inverse modeling. Variability is accounted for on various scales 
(see Sections 4.1, 6.5.2, and 8.2). 
2.2.07.02.0A 
Unsaturated ground-
water flow in the 
geosphere 
Unsaturated flow processes are accounted for in the conceptual and 
mathematical model (see Sections 6.3.2 and 6.6.1.1). 
2.2.07.04.0A 
Focusing of unsatu-
rated flow (fingers, 
weeps) 
Explicitly modeled heterogeneity induces flow focusing. Impact of small-
scale flow focusing effects on seepage is included in effective parameter 
(see Sections 6.3, 6.3.3.1, 6.6.2.1, and 6.6.3.3). 
2.2.07.08.0A 
Fracture flow in the 
UZ 
Liquid flow through unsaturated fractures is simulated using site-specific 
fracture properties; explicit inclusion of heterogeneity leads to flow 
channeling (see Sections 6.3.2, 6.3.3.2, and 6.6.2.1) 
2.2.07.09.0A 
Matrix imbibition in 
the UZ 
Matrix imbibition is considered small under near-steady seepage 
conditions and is therefore neglected (see Section 6.3.3.2). 
2.2.07.18.0A 
Film flow into the 
repository 
If water originating from film flow seeps into the opening during a liquid-
release test, it is reflected in the corresponding seepage data point used 
for model calibration, i.e., film flow is automatically accounted for in the 
estimated seepage-related parameter and thus in the prediction of 
seepage into waste emplacement drifts (see Sections 6.1.2, 6.3.2, 
6.3.3.2, 6.3.4, and 6.6.3.1). 
2.2.07.20.0A 
Flow diversion around 
repository drifts 
The impact of an underground opening on the unsaturated flow field 
(including dry-out from evaporation, capillary-barrier effect, and flow 
diversion around the drift) is captured in the seepage process model by 
solving the equations governing unsaturated flow in fractured porous 
media and by specifying appropriate boundary conditions at the drift wall. 
Drift shadow is simulated as a result of seepage exclusion (see Sections 
6.3, 6.3.2, 6.3.3, 6.6, and 6.8). 
FEP=features, events, and processes; UZ=Unsaturated Zone 

6.3 BASE-CASE CONCEPTUAL MODEL 
6.3.1 Seepage Phenomena and Processes 
To understand the seepage process and to identify the factors affecting seepage, a description is 
given of the fate of water percolating through the unsaturated zone of Yucca Mountain, 
eventually encountering the immediate vicinity of a waste emplacement drift. This description is 
based on and consistent with the related discussion found in the scientific literature (see, for 
example, Philip et al. (1989 [DIRS 105743]) and Using the Continuum Approach to Model 
Unsaturated Flow in Fractured Rock. (Finsterle 2000 [DIRS 151875]) and references therein). 
Water that penetrates the ground surface and reaches a depth that is unaffected by 
evapotranspiration starts to percolate downwards, driven by gravity and capillary forces. The 
detailed flow path is determined by the degree of fracturing, fracture geometry, orientation, and 
connectivity, as well as the hydrogeologic properties of the fractures and the matrix. Depending 
on these factors, the water phase in the unsaturated fracture network will either disperse or focus 
along the flow path. Tilted contacts between hydrogeologic units (especially between welded 
and nonwelded tuffs) may affect the overall flow pattern or lead to a change in the frequency and 
spacing of flow channels. However, the channeling process (i.e., the redistribution of water 
leading to local fluxes in portions of the fracture network that are higher and lower than the 
average flux) is likely to diminish with depth. As flow concentration continues to occur, the 
distance between the individual channels carrying focused flow increases, so the likelihood of 
two channels meeting and merging decreases with depth. Flow focusing and dispersion of flow 
paths also happens within a rough-walled fracture, where asperity contacts and locally larger 
fracture openings lead to small-scale redistribution of water within the fracture. A general 
discussion of channeling effects under unsaturated flow conditions can be found in Solute 
Channeling in Unsaturated Heterogeneous Porous Media (Birkholzer and Tsang 1997 
[DIRS 119397]). Flow focusing is important for seepage, because seepage depends on the local 
rather than average percolation flux. 
As water approaches the potential waste emplacement drift (one to several meters above the drift 
ceiling), conditions change in several ways, all affecting the amount of water that will eventually 
seep into the opening. The water may first encounter a dry-out zone caused by drift ventilation. 
The dry-out zone may also develop as a result of increased temperatures, in which case it is 
referred to as a boiling zone. Under these thermal conditions, the dry-out zone may be 
surrounded by a two-phase zone in which heat-pipe effects determine water, vapor, and heat 
fluxes, and a condensation zone with increased saturation. (Note that ventilation and elevated 
temperatures are limited in time and thus do not affect long-term seepage.) 
In addition, formation properties around the openings are likely to be altered as a result of stress 
redistribution during drift excavation, which leads to local opening or partial closing of fractures 
and potentially the creation of new fractures. Thermal expansion of the rock matrix may also 
induce changes in apertures. Finally, the local chemical environment, which is altered by 
evaporation and thermal effects, may lead to dissolution and precipitation of minerals, again 
affecting porosity, permeability, and capillarity of the fracture system as well as fracture-matrix 
interaction. Such thermally and geochemically induced alterations were of no significance 
during the ambient liquid-release tests analyzed by the SCM. In general, however, all the 

conditions discussed above lead to a flow pattern in the vicinity of a waste emplacement drift 
different from that in the undisturbed formation under ambient conditions. 
Provided that liquid water penetrates the boiling or dry-out zone (for details, see Drift-Scale 
Coupled Processes (DST and TH Seepage) Models (BSC 2004 [DIRS 170338])), it reaches the 
immediate vicinity of the drift wall, where (at least under ambient conditions) a boundary layer 
of increased saturation is expected to develop as a result of the capillary barrier effect (Philip et 
al. 1989 [DIRS 105743]). The water is prevented from seeping into the drift because of 
capillary suction, which retains the wetting fluid in the pore space. If permeability and 
capillarity of the fracture network within this boundary layer are sufficiently high, all or a portion 
of the water is diverted around the drift under partially saturated conditions. Locally, however, 
the water potential in the formation may be higher than that in the drift, and water appears at the 
drift surface. At the drift surface, the water either evaporates, follows the inclined, rough wall in 
a water film, or forms a drop that grows and eventually detaches (Or and Ghezzehei 2000 
[DIRS 144773]). Only this last mechanism is considered drift seepage according to the 
definition of Section 6.1.2. 
To summarize, the rate of water dripping into an opening in an unsaturated geologic formation is 
expected to be less than the downward percolation rate because (1) the cavity acts as a capillary 
barrier, (2) water may flow along the drift surface without dripping into the opening, and 
(3) water may evaporate. Even if the seepage threshold was exceeded and seepage occurred, the 
seepage flux would be lower than the percolation flux. 
Section 6.3.2 describes the rationale and justification for using a heterogeneous continuum model 
for the simulation of drift seepage. Section 6.3.3 discusses specific factors and properties 
affecting seepage during liquid-release tests and how they are incorporated into the conceptual 
model. 
6.3.2 Continuum Approach 
The Seepage Calibration Model is conceptualized as a heterogeneous continuum model. The 
continuum approach can be considered appropriate for seepage studies if it is capable of 
predicting seepage rates for a drift in a fractured formation. 
Water flow through the Topopah Spring welded unit (TSw) and seepage into openings at Yucca 
Mountain occurs predominantly through the fracture network, suggesting that a discrete fracture 
network model is more appropriate than a fracture continuum model for the reproduction and 
prediction of drift seepage. However, it is important to recognize that flow diversion around the 
opening occurs primarily within the fracture plane (in-plane diversion). The need to engage 
multiple fractures arises only if the fracture is too short and the flow path within the fracture 
plane is interrupted. In this case, water is diverted into the next connected fracture. This fracture 
is again unlikely to be parallel to the drift axis, allowing the in-plane flow-diversion process to 
continue. The situation is schematically illustrated in Figure 6-1, which shows two fractures 
intersected by a drift. In Figure 6-1a, the two fractures are aligned with the drift axis (which is 
an implicit assumption made in two-dimensional fracture network models used to predict drift 
seepage). As an artifact of this specific and unrealistic fracture orientation, in-plane flow 
diversion is prevented, and the resulting impact of discreteness on seepage is exaggerated. 

Two-dimensional fracture network models (including those shown by Finsterle (2000 
[DIRS 151875], Plate 1) and in A Note on Unsaturated Flow in Two-Dimensional Fracture 
Networks (Liu et al. 2002 [DIRS 160230], Figures 16)) represent extreme cases that are not 
representative of and appropriate for site-specific seepage modeling. (The advantages and 
disadvantages of the discrete fracture network model are further discussed in Section 6.4.1). 
In Figure 6-1b, the fractures are approximately perpendicular to the drift axis. Flow diversion 
occurs within the fracture plane, a process that is appropriately captured by a heterogeneous 
fracture continuum model even for a single fracture. In-plane flow occurring in multiple 
fractures can be readily combined and described by an effective fracture continuum. 
(a) (b) 
NOTE: (a) Drift intersected by network of fractures that are parallel to drift axis; note that a 2-D fracture network 
model (see, for example, Figure 6-3a) assumes that all fractures are parallel to the drift axis, preventing 
flow diversion within the fracture plane: (b) Drift intersected by randomly oriented fractures; note that a 2-
D (and 3-D) fracture continuum model considers flow diversion occurring within multiple, randomly oriented 
fracture planes. 
Figure 6-1. Schematic Showing Two Fractures Intersecting a Drift 
In Geology of the ECRB Cross-Drift  Exploratory Studies Facility, Yucca Mountain Project, 
Yucca Mountain, Nevada, Mongano et al. (1999 [DIRS 149850], pp. 6572, 7679) documented 
in their detailed line survey report that fracture frequencies observed in the Topopah Spring 
upper lithophysal (Tptpul), middle nonlithophysal (Tptpmn), and lower lithophysal (Tptpll) 
zones range from 3.2 m-1 to 4.3 m-1, and that fractures are predominantly developed in two or 
three orientations resulting in well connected networks. Connectivity of fractures is further 
enhanced by the presence of numerous microcracks as observed at the site. Fracture network 
connectivity has been determined at a drift scale through air-injection tests, which indicate that 
fractures networks are well connected within the moderately to densely welded rocks selected to 
host the repository (see Section 6.5.2). 

Given the significance of in-plane flow diversion around the drift in combination with relatively 
high fracture density of variable orientation, a three-dimensional, heterogeneous fracture 
continuum model is an appropriate conceptualization. The continuum concept captures the 
relevant processes more realistically than, for example, a two-dimensional discrete fracture 
network model. 
In addition, the appropriateness of the continuum approach to simulate flow through fractured 
rock was studied by Jackson et al. [Self-Consistency of a Heterogeneous Continuum Porous 
Medium Representation of a Fractured Medium (2000 [DIRS 141523])] using synthetic and 
actual field data. They concluded that heterogeneous continuum representations of fractured 
media are self-consistent; i.e., appropriately, estimated effective continuum parameters are able 
to represent the underlying fracture-network characteristics. 
Finsterle (2000 [DIRS 151875]) demonstrated that seepage into underground openings excavated 
from a fractured formation could be simulated using a model based on the continuum concept, 
provided that the model is calibrated against seepage-relevant data (such as data from 
liquid-release tests). Synthetically generated data from a model that exhibits discrete flow and 
seepage behavior were used to calibrate a simplified fracture continuum model. The calibrated 
continuum model was used to predict average seepage rates into a sufficiently large section of an 
underground opening for low percolation fluxes, i.e., conditions significantly different from 
those encountered during calibration. The extrapolation from high-rate liquid-release tests to 
low-rate percolation fluxes is equivalent to the extrapolation from the calibration runs performed 
with the SCM to the predictive simulations that will be performed by the SMPA. As discussed 
by Finsterle (2000 [DIRS 151875]), the extrapolated seepage predictions performed with the 
continuum model were consistent with the synthetically generated data from the discrete-feature 
model under low percolation conditions. This demonstrates that (1) the calibrated continuum 
model and discrete-feature model yield consistent estimates of average seepage rates, and (2) that 
the continuum approach is appropriate for performing seepage predictions even if extrapolated to 
percolation fluxes that are significantly lower than those induced by liquid-release tests. The 
tests were performed at relatively high injection rates to generate seepage data useable for model 
calibration. Note that the discrete-feature model used in the study makes the extreme assumption 
that all fractures are oriented parallel to the drift axis, as discussed above and illustrated in Figure 
6-1a. Even under these unfavorable conditions, the continuum approach proved to be 
appropriate. 
Note that the fracture density and hydraulic parameters used by Liu et al. (2002 [DIRS 160230]; 
see also Figure 6-3a below) result in very little flow diversion around the opening. This is a 
direct result of the unrealistic assumption that all fractures are parallel to the drift axis, which 
prevents in-plane flow diversion. In such a two-dimensional discrete fracture network model, 
flow diversion occurs only if the fracture density and/or the capillary-strength parameter are 
high. This was recognized by Liu et al. (2002 [DIRS 160230], p. 15-8), who concluded that 
fracture network models need to be three-dimensional for them to be able to realistically evaluate 
the capillary barrier effects in fractured formations. As discussed above, in-plane flow diversion 
in a three-dimensional fracture network can be appropriately represented by a heterogeneous 
continuum model. A calibrated continuum model is appropriate even in the extreme case where 
all fractures are perfectly parallel to the drift axis, as demonstrated by Finsterle (2000 
[DIRS 151875]) and discussed in the previous paragraph. Note that the synthetic fracture 

network and hydraulic parameters used in the discrete model by Finsterle (2000 [DIRS 151875]; 
see also Figure 6-2a below) induced some flow diversion. This difference in flow diversion 
capability between the models by Finsterle (2000 [DIRS 151875]) and Liu et al. (2002 
[DIRS 160230]) is caused by their respective parameter choices. This difference, however, does 
not affect the finding that the continuum approach captures the seepage-relevant processes more 
appropriately than two-dimensional discrete fracture network models. The advantages and 
disadvantages of the discrete fracture network model are further discussed in Section 6.4.1. 
The continuum approach is considered appropriate for seepage studies if applied within the 
framework described in this Model Report. Inverse modeling should be used for the estimation 
of process-specific, model-related, and scale-dependent parameters, and the same or similar 
conceptual model should be used for the subsequent seepage predictions, specifically the SMPA. 
Adopting the continuum approach, water flow under unsaturated conditions is governed by 
Richards equation (Richards 1931 [DIRS 104252]), which states that (1) isothermal flow of 
water in a porous medium or rough-walled fracture occurs under the combined effect of 
gravitational and capillary forces, (2) flow resistance is a function of saturation, and (3)for the 
purposes of this representationmovement of the nonwetting air phase can be neglected. This 
general concept is reasonable, because gravitational force is ubiquitous, and rough-walled or 
partially filled fractures exert varying degrees of capillary pressure at different saturation levels. 
Relative permeability and capillary pressure are described as continuous functions of effective 
liquid saturation, following the expressions given by the van Genuchten-Mualem model 
(van Genuchten 1980 [DIRS 100610], pp. 892893) as implemented in the iTOUGH2 code 
[Users Manual (UM) for iTOUGH2 V5.0 (BSC 2002 [DIRS 161066], Section 4.3.2)]. The 
applicability of relative permeability and capillary pressure functions is appropriate also for 
fractures that are rough-walled and/or partially filled with porous material. The constant of 
proportionalityrelative permeabilityis saturation-dependent because porous-medium 
continuum laws also apply to water flow through fractures filled with porous material, and in the 
absence of fracture fillings, the thickness of the water film and connectivity of liquid islands on 
the fracture surface are saturation dependent (Tokunaga and Wan 1997 [DIRS 139195]). 
Capillary strength (represented by the parameter) and permeability are considered 
uncorrelated. The functional relationship describing the potential correlation between 
permeability and capillary strength is unknown. An increase in the effective (continuum) 
permeability of a fracture block may be attributed to either larger fracture apertures (which 
would reduce capillary strength) or to an increase in fracture density (which would not affect 
capillary strength). The capillary-strength parameter is taken to be constant for a given test 
bed, and will be estimated by inverse modeling. 
The van Genuchten-Mualem model is the standard model used in the suite of UZ models; it is 
appropriate for modeling of unsaturated flow and seepage (as discussed in Section 5.3) and was 
chosen in this work to ensure consistency. The mountain-scale models may use a modified 
version of the van Genuchten-Mualem functions to account for the fact that unsaturated flow is 
restricted to a limited number of (active) fractures and that flow within a fracture is likely to be 
channelized. Both effects lead to different effective saturations determining capillary pressure 
and relative permeability, and they reduce fracture-matrix interaction. This revised model was 

developed by Liu et al. [An Active Fracture Model for Unsaturated Flow and Transport in 
Fractured Rocks (1998 [DIRS 105729])] and is referred to as the Active Fracture Model 
(AFM). For drift-scale seepage models under ambient conditions, the standard van 
Genuchten-Mualem model is employed rather than the AFM, because (1) flow segregation into 
active and inactive portions of the fracture network is a large-scale effect not engaged during the 
short-distance liquid-release tests; (2) flow channeling within fractures is partially accounted for 
through explicit modeling of small-scale heterogeneity; (3) the correction of the fracture-matrix 
interface area (the main effect captured by the AFM) is insignificant for seepage because of 
insignificant matrix imbibition during the calibration period (see Section 6.3.3.2); and (4) the 
potential impact of AFM effects on seepage are automatically reflected in the observed seepage-
rate data, which are used to estimate an effective capillary-strength parameter suitable for 
simulations with a conceptually consistent seepage-prediction model. 
This general model conceptualization is consistent with that of the UZ Model. The calibration 
process and the consistent conceptualization in the downstream models (specifically the SMPA) 
make this a valid and reasonable approach. 
6.3.3 Factors and Properties Affecting Seepage During Liquid-Release Tests 
Seepage is a process that occurs at the interface between the natural and engineered systems. 
Consequently, seepage is not only affected by hydrogeologic factors (such as formation 
properties and flow conditions in the natural environment), but also by the engineered system 
itself. This second set of factors affecting the amount and distribution of seepage includes the 
design of the repository and waste emplacement drifts (location and geometry), the method of 
construction (excavation effects, drift surface roughness, ground support, backfill), and the 
conditions within the drifts (heat load and ventilation, which determine the relative humidity, 
evaporation potential, and the extent of the dry-out zone). 
The engineered barriers in the waste emplacement drift (specifically the drip shield and waste 
packages) will be exposed to seeping water only if (1) a flow channel exists that carries water 
through the (potentially dry) zone around the drift, (2) the local percolation flux in this flow 
channel is high enough to overcome the local seepage threshold, and (3) the water droplets 
forming at the drift wall do not evaporate or dissipate in a water film flowing along the surface. 
The following subsections describe in more detail the key factors affecting drift seepage and how 
they are included in the base-case conceptual model. 
Based on theoretical insights discussed in the scientific literature [see, e.g., Philip et al. 
(1989 [DIRS 105743]); Modeling Studies and Analysis of Seepage into Drifts at Yucca 
Mountain (Birkholzer et al. 1999 [DIRS 105170]); Seepage into Drifts with Mechanical 
Degradation (Li and Tsang 2003 [DIRS 163714])] as well as the sensitivity analyses presented 
in Section 6.6.3.1, the most important factors affecting seepage are the magnitude of the local 
percolation flux in relation to the formations permeability, the strength of the capillary forces in 
the fractures, the connectivity of the fracture network in the boundary layer, the local topography 
of the rough drift wall, and the thermodynamic conditions in the drift. 

6.3.3.1 Percolation Flux 
General Description 
The magnitude of the percolation flux is a key factor determining seepage. Seepage is initiated if 
the local percolation fluxes in individual flow channels and their accumulation near the drift 
ceiling exceeds the diversion capacity of the capillary barrier (which is caused by the presence of 
the drift), the evaporation potential of the atmosphere in the drift, and the capacity of water films 
to carry water along the drift surface. Because the localrather than averagepercolation flux 
controls the onset of seepage, the distribution of flow channels on all scales becomes a critical 
aspect for drift seepage. Flow focusing could concentrate water onto a particular drift segment 
and lead to a flux that exceeds the seepage threshold. On the other hand, if flow is concentrated 
in one location, flow will be reduced in other areas (potentially below the prevalent seepage 
threshold), leading to overall less seepage. Therefore, the distribution of flow channels, their 
frequency, width, and hydrologic properties determine the seepage probability and seepage 
amounts. 
The spatial distribution of flow channels may change with the average percolation flux and 
potentially with time. The flux in a flow channel may be near steady state or episodic with a 
wide spectrum, ranging from high-frequency fluctuations triggered by flow instabilities, to 
intermediate variabilities in percolation fluxes in response to changing weather conditions, to 
long-term variations from climate changes. 
In summary, the local (rather than average) percolation flux reaching the drift is the most 
important factor determining whether seepage occurs, the seepage rate, and the spatial and 
temporal distribution of seepage events. 
Model Conceptualization 
The actual percolation flux and its distribution cannot be measured directly. Estimates of the 
average, steady-state percolation fluxes at the locations of the liquid-release tests are taken from 
the UZ Model (see Section 6.6.2.3) and applied at the top of the corresponding drift seepage 
models. Note that large-scale redistribution of infiltration and percolation fluxes is captured in 
the mountain-scale UZ Model; intermediate-scale flow concentration is accounted for in the 
TSPA calculations through the use of a probabilistic flow focusing factor (BSC 2004 
[DIRS 169131], Section 6.6.4.2). Small-scale flow concentration is included in the SCM by 
explicitly modeling small-scale heterogeneities (see Section 6.6.2.1). 
The transient SCM simulations capture the time-dependent boundary conditions, saturation, and 
seepage-rate changes induced by the intermittent water release during seepage testing. Potential 
occurrence of small-scale, high-frequency episodic flow events is reflected in the seepage-rate 
data used for calibration. The cumulative effect of these episodic events on seepage is therefore 
appropriately captured in the estimation of an effective capillary-strength parameter. 
Low-frequency fluctuations in the background percolation flux on account of weather-condition 
or climate changes are insignificant because of the comparatively short duration of the 
liquid-release tests. In summary, the high-frequency episodic flow events are captured in the 
effective, seepage-relevant capillary-strength parameter, whereas the low-frequency transient 

events are accounted for in the UZ Model, which provides a time-dependent percolation flux as 
input to the seepage TSPA calculations. Additional issues related to the amount, variability, and 
uncertainty of percolation flux, lateral flow diversion, as well as large- and intermediate-scale 
flow concentration are also addressed by the UZ Model, seepage abstraction, and TSPA 
calculations. 
6.3.3.2 Formation Properties 
General Description 
The key formation properties determining the effectiveness of the capillary barrier are (1) the 
capillary strength and (2) the tangential conductivity in the boundary layer near the drift wall. 
Geologic formations with strong capillarity and high tangential conductivity exhibit a high 
seepage threshold (i.e., low seepage), whereas a weak capillary barrier effect (i.e., high seepage) 
is expected if water retention is small or if the tangential permeability is insufficient to promote 
flow diversion. 
Porous formations with strong capillarity tend to have low permeability and vice versa, which is 
a correlation that reduces the probability of encountering parameter combinations conducive to 
extreme (low or high) seepage behavior, making seepage relatively uniform across different 
geologic units. However, this negative correlation between conductivity and capillary strength 
may not apply to a fractured system, specifically if considering the seepage process. A certain 
hydraulic conductivity may result from a network consisting of a few, large fractures or, 
alternatively, many small, well-connected fractures. The first network would exhibit weak 
capillarity, whereas the second network has strong capillarity, i.e., capillarity is not necessarily 
correlated to permeability. Moreover, if the predominant fracture orientation happens to be 
aligned with the drift axis (see Figure 6-1a), little or no tangential conductivity is available, flow 
diversion is reduced or prevented, and seepage is increased. Even if fractures are normal to the 
drift axis, they may be too small or poorly connected, i.e., they would not be able to facilitate a 
continuous flow path from the apex of the drift to its spring line. For flow diversion to occur, the 
fracture system must have sufficient connectivity and permeability to provide the necessary 
effective conductivity in tangential direction around the drift. 
In the repository units, matrix permeability is low, and the potential for imbibition of substantial 
amounts of water into the matrix is limited because of relatively low porosity and relatively high 
initial liquid saturation. In a fracture-matrix system, the transient effects from matrix imbibition 
are restricted to intermediate times, i.e., they are insignificant (1) for a short-term liquid-release 
test with insufficient time for matrix imbibition, and (2) for a long-term seepage experiment, 
when near-steady late-time data are no longer affected by matrix imbibition. Most liquid-release 
tests analyzed in this Model Report are sufficiently long to yield near-steady seepage rates that 
are insignificantly affected by potential matrix imbibition. Finally, potential seepage from the 
matrix during a liquid-release test is captured by the seepage-rate data used for calibration, and is 
thus reflected in the effective, seepage-relevant parameter. 
Heterogeneities in formation properties impact seepage as they promote flow concentration and 
increase the probability of locally breaching the capillary barrier. 

Model Conceptualization 
Seepage-related fracture properties on all relevant scales are not available and cannot be reliably 
derived from fracture-trace maps, considering that the mapped geometric characteristics and 
hydraulic properties are not related in a simple or unique way. However, as discussed in 
Sections 6.3.2 and 6.4.1 and demonstrated by Finsterle (2000 [DIRS 151875]), adequate average 
seepage prediction on the scale of a waste package does not require a discrete fracture network 
model. 
In this work, the capillarity and the conductivity are conceptualized as effective properties that 
are specifically determined for their intended use in a drift seepage model. The corresponding 
model parameters must represent the average hydraulic characteristics of individual fractures as 
well as the connectivity, density, geometry, and orientation of the fracture network as it relates to 
the geometry and orientation of the underground opening. Moreover, they must account for 
seepage processes that cannot be explicitly implemented in the conceptual model (such as film 
flow and small-scale roughness in the drift ceiling), and compensate for certain artifacts related 
to the finite discretization of the numerical model. Model calibration using data that reflect all 
relevant seepage processes is the approach relied upon to determine these effective parameters. 
The SCM is conceptualized as a heterogeneous fracture continuum model (see also 
Section 6.3.2). Given the specifics of the seepage process, the overall modeling approach, the 
purpose of the SCM, and the consistency with the downstream model (the SMPA), a 
single-continuum representation of the fractured formation is appropriate. (Note that the impact 
of the rock matrix may not be ignored when considering other processes; a dual-continuum 
model is selected in these instances.) 
The seepage-relevant capillary-strength parameter is determined by calibrating the model against 
seepage-rate data from liquid-release tests (see Section 6.5.3). These data reflect the seepage 
process and contain information about seepage-relevant capillary properties of the fractured 
formation in the vicinity of an open drift. Thus, the inversely determined effective 
capillary-strength parameter is considered pertinent and appropriate for the intended use of the 
model. 
The simulated seepage can be increased by decreasing capillary strength or permeability. 
Consequently, the two parameters are negatively correlated if inversely determined from 
seepage-rate data. Because only seepage data are available for calibration, the parameters are 
expected to be strongly correlated. That is, it is unlikely that they can be determined 
independently from one another and with a reasonably low estimation uncertainty. To reduce 
correlations and to improve the conditioning of the inverse problem, only the capillary-strength 
parameter is estimated through inverse modeling, whereas the permeability is fixed during the 
inversion. The choice of this calibration parameter is further discussed in Section 6.6.3.1. 
The permeability field is considered the result of a stochastic process. The geostatistical 
properties of the field are determined from air-injection tests (see Section 6.5.2). Multiple 
realizations of the permeability field are generated and used in the inversions of data from the 
lower lithophysal zone. 

The permeability fields generated for simulations with the SCM are representative of the 
conditions currently encountered at the test locations of Yucca Mountain. Therefore, thermally 
and geochemically induced property changes do not need to be considered in this Model Report. 
They are addressed by the TH Seepage Model (BSC 2004 [DIRS 170338]) and the 
Thermal-Hydrologic-Chemical (THC) Seepage Model [Drift-Scale Coupled Processes (DST and 
THC Seepage) Models (BSC 2004 [DIRS 168848])]. 
6.3.3.3 Drift Geometry 
General Description 
The overall drift size and geometry impact the seepage threshold and the seepage amount. 
Generally, a larger drift exhibits a significantly lower seepage threshold because more water 
accumulates in the boundary layer as it migrates over a longer diversion distance around the 
wide opening. Because of the nonlinear impact of cavity size on seepage (Philip et al. 1989 
[DIRS 105743]), seepage into large openings cannot be easily inferred from cumulative seepage 
into small cavities. 
The effectiveness of a capillary barrier is highest if the shape of the cavity follows an 
equipotential surface. In a homogeneous medium, parabolic cavities are more efficient in 
preventing seepage than circular or flat-roofed openings. Breakouts in the drift ceiling, as a 
result of rock fall and general drift degradation, may change the overall drift geometry and lead 
to local topographic lows, which may trap water, reduce or prevent flow diversion, and thus 
initiate seepage. In addition, small-scale surface roughness tends to increase seepage if the 
amplitude of the irregularity is on the order of boundary-layer thickness. The latter is determined 
by the capillary strength of the formation. 
In a heterogeneous, fractured formation, the importance of drift shape and drift geometry may be 
diminished relative to that of flow channeling and local ponding conditions (see Birkholzer et al. 
(1999 [DIRS 105170], pp. 372379) and Section 6.4.2). 
Model Conceptualization 
The impact of the overall geometry of the underground opening (ECRB Cross-Drift or niche) on 
seepage is accounted for through explicit discretization of the cavity. The ECRB Cross-Drift is 
approximated as being cylindrical, with a diameter of 5.0 m. The overall geometry of the niches 
is taken from survey data, thus including some medium-scale roughness from rock fall and large 
lithophysal cavities. 
Small-scale roughness is indirectly included through a discretization effect. The length of the 
last vertical connection from the gridblocks representing the formation and the interface denoting 
the drift surface is 0.05 m (see Appendix C, Appendix D, and Appendix E; see also related 
discussion in Section 6.6.1.2). The choice of this nodal distance affects seepage because 
(in the model) no horizontal flow diversion can occur closer than 0.05 m from the drift wall. 
Since water is laterally diverted only if capillary suction is on the order of 0.05 m or higher, the 
discretization has an effect similar to that of (1) drift-wall roughness of amplitude of 0.05 m, 
with troughs at the gridblock centers and ridges along the gridblock interfaces, or (2) short 
fractures cutting into the opening, with a distance to the next fracture intersection of 0.05 m. 

Consequently, the effective capillary-strength parameter estimated by inverse modeling depends 
on the chosen discretization; it contains a geometric component related to the length of the nodal 
distance between the formation and the drift. The estimate is thus model-related, and the 
discretization between the calibration model and the prediction model must be consistent. 
In summary, the geometric factors affecting seepage are accounted for through (1) explicit 
discretization of the opening (which includes the overall shape as well as medium-scale 
roughness from break-outs lithophysal cavities), (2) by preventing flow diversion in a 0.05 m 
thick layer around the drift (mimicking small-scale surface roughness with a 0.05 m amplitude of 
the irregularities), and (3) the estimation of an effective capillary-strength parameter. The 
inclusion of small-scale surface roughness (exceeding an amplitude of 0.05 meters) and discrete 
effects from small fractures into an effective capillary-strength parameter is appropriate because 
their impact on seepage rates is directly related to capillarity. 
Note that the nominal diameter of a repository drift is 5.5 m, which is slightly larger than that of 
the ECRB Cross-Drift (5.0 m). This difference is of no significance, because the seepage-related 
parameters are determined using a model with the correct diameter (5.0 m) to be used for the 
analysis of liquid-release tests in the ECRB Cross-Drift. These parameters are then applied in 
the prediction model, which simulates seepage into an opening with a 5.5 m diameter. The 
impact of drift-shape changes as a result of drift degradation is discussed in Seepage Model for 
PA Including Drift Collapse (BSC 2004 [DIRS 167652]). 
6.3.3.4 Evaporation Conditions 
General Description 
Reduced relative humidity in the underground opening leads to evaporation of water at the drift 
surface and the development of a dry-out zone in the vicinity of the cavity. Part or all of the 
water reaching the ceiling of the opening during a liquid-release test may evaporate, depending 
on the evaporation potential in the drift and the wet area exposed to evaporation. The 
evaporation potential depends on the relative humidity in the opening and the thickness of a 
diffusive boundary layer at the drift surface, which in turn is governed by the air velocity in the 
ventilated drift. 
The size of the wet spot developing at the drift ceiling as a result of liquid release above the drift 
depends on the formation properties, the spreading mechanism along the drift surface, and 
evaporation itself. As water injected during a liquid-release test reaches the opening, it spreads 
along the surface on account of capillary tension within the rough drift wall. As a result, water 
potentially seeping into the opening may not only form droplets or lines of water along fracture 
traces with a small surface area, but may spread across the drift surface over a relatively large 
area. This phenomenon is qualitatively confirmed by the geometry of the wet spot observed at 
the niche ceiling during seepage experiments (BSC 2004 [DIRS 170004], Section 6.2.1.3.4, 
Figure 6.2.1-7; Trautz and Wang 2002 [DIRS 160335], Figures 7 and 9). The geometry of the 
wet spot does not have a clear correlation with the visible fracture traces. Even though water 
first appears along fracture traces (Trautz and Wang 2002 [DIRS 160335], Figure 10), the wet 
spot grows in an areal fashion. It is obvious from the short arrival time and the average speed at 
which the leading edge of the plume moves across the ceiling that the water is not transmitted 

through the matrix, but spreads along the ceiling as a surface film, possibly supported by flow 
through microfractures. Evaporation from such wet areas is similar to evaporation from a free 
water surface, where the evaporation rate is governed by one-dimensional vapor diffusion across 
a relatively thin boundary layer of linearly decreasing vapor concentration. Temporal shrinkage 
of the wet spot can be correlated to increased evaporation as a result of changed ventilation 
regime, highlighting the coupled nature of the process. 
In a closed-off and humidified niche, potential evaporation at the wall or in the capture system is 
expected to be small compared to the amount of water being released. Seepage experiments in 
the middle nonlithophysal zone of the Topopah Spring welded unit were conducted in niches that 
were closed off by a bulkhead, which leads to comparatively high relative humidity and low air 
circulation. Moreover, a humidifier was used in some of the experiments to ensure high relative 
humidity. For these conditions, Ho (1997 [DIRS 141521]) and Or and Ghezzehei 
(2000 [DIRS 144773]) provide a detailed description of evaporation mechanisms on the scale of 
individual water droplets within fractures or emerging from fractured formations. The 
evapo-infiltration threshold calculated by Ho (1997 [DIRS 141521], p. 2670) is significantly 
lower than the applied injection rates, suggesting a very minor influence of evaporation on 
measured seepage rates in experiments conducted in the niches. 
Model Conceptualization 
Evaporation effects are included in the modeling of liquid-release tests performed in the 
ventilated ECRB Cross-Drift as well as in Niche 5. Evaporation effects are neglected in the 
modeling of liquid-release tests conducted in the closed-off niches in the middle nonlithophysal 
zone, i.e., Niches 2, 3, and 4. As demonstrated in Section 6.7, the impact of slight evaporation in 
a closed-off and moisturized niche on seepage ratesand thus on the estimation of 
seepage-relevant parametersis minor. 
Evaporation effects are accounted for in the model by prescribing the measured relative humidity 
in the opening as a temporally varying water-potential boundary condition. Evaporation is 
calculated as a function of the water-potential gradient at the drift surface, the vapor diffusion 
coefficient, and the thickness of the diffusive boundary layer, which is estimated from 
evaporation pan measurements. A detailed description of the corresponding conceptual and 
mathematical model and the estimation of the evaporation boundary-layer thickness is given in 
Sections 6.6.1.3 and 6.6.1.4. 
Predictions of long-term seepage using the SMPA are based on the presumption of 100 percent 
relative humidity in the waste emplacement drifts, yielding higher seepage estimates than those 
expected in a ventilated environment. 
6.3.3.5 Lithophysal Cavities 
General Description 
The impact of lithophysal cavities on flow and seepage is twofold: (1) lithophysal cavities are 
essentially obstacles to water flow because they act as capillary barriers, focusing the water that 
flows around them; (2) lithophysal cavities intersected by the drift lead to a rough drift wall, 

potentially creating seepage points at local topographic lows. Both effects tend to promote 
seepage. 
Model Conceptualization 
The effect of lithophysal cavities on seepage can be captured through the estimation of an 
effective capillary-strength parameter, making the explicit inclusion of lithophysal cavities into 
the process model unnecessary. This approach is considered appropriate for the following 
reasons: (1) omitting lithophysal cavities in the process model used for inverse modeling yields 
lower estimates of the capillary-strength parameter; (2) consistency between the calibration 
model (the SCM) and the prediction model (the SMPA) removes the impact of a potential 
estimation bias; (3) the approach allows for the development of a single SMPA conceptual model 
for both the middle nonlithophysal and lower lithophysal zones, yielding a single look-up table 
for TSPA to sample from; and (4) explicit modeling of lithophysal cavities is not warranted 
because of insufficient information regarding their location, shape, and frequency. Note that the 
impact of lithophysal cavities on surface roughness in Niche 5 is accounted for through explicit 
discretization of the niches geometry, based on survey data (see Appendix E). 
6.3.4 General Modeling and Data-Analysis Approach 
The key element of the approach chosen to simulate seepage and determine seepage-relevant 
parameters is the reliance on inverse modeling. Given the complexity of the seepage process in a 
fractured porous medium, it is considered unfeasible to develop a detailed process model with a 
deterministic calculation of unsaturated water flow, through a fracture network that exhibits 
multiscale variabilities in hydraulic properties. Such a model would also require an accurate 
representation of the seepage process, which includes effects from small-scale roughness and 
small-scale heterogeneities, film flow within fractures and along the drift surface, drop formation 
and detachment, and other processes. The necessary characterization data needed to carry out 
such a detailed simulation are not available. As discussed in the following paragraph, such a 
detailed simulation is not necessary for an adequate treatment of the issue. 
The difficulties mentioned above can be effectively overcome by recognizing that (1) detailed 
simulation of individual seeps is not necessary to estimate average seepage rates into waste 
emplacement drifts, (2) certain factors affecting seepage can be lumped into an effective 
parameter, (3) calibrating a model against data from seepage experiments ensures that the model 
captures the relevant processes, (4) estimating effective parameters partly compensates for 
processes and features that are not explicitly considered in the model, and (5) the estimated 
parameters are optimal and can be directly used in the prediction model. 
The main advantage of this approach is that it relies directly on seepage-rate data, which 
inherently contain information about the relevant processes. Moreover, the calibration data 
(seepage rates on the scale of a drift section) are very similar to the measure of interest for the 
subsequent predictions. The consistency between the calibration model used to derive 
seepage-relevant parameters and the prediction model used to forecast seepage minimizes 
potential conceptual differences and large systematic errors. The advantages of the selected 
method over alternative approaches are further evaluated in Section 6.4. 

The SCM is conceptualized as a three-dimensional, heterogeneous continuum model. The 
continuum mainly represents the dense fracture network that dominates the seepage process. 
The SCM is conceptually consistent with the site-scale model of the unsaturated zone at Yucca 
Mountain and submodels thereof, specifically the SMPA and TH Seepage Model 
(BSC 2004 [DIRS 170338]). This makes it straightforward to embed the SCM into the current 
modeling framework. 
As will be discussed in Section 6.6.3.2, the SCM is calibrated against late-time seepage-rate data 
from liquid-release tests. Early-time seepage data are discarded because they are affected by 
storage effects and the properties of a few fractures connecting the injection interval with the 
opening. These fractures are not necessarily representative of the fracture network that is 
engaged in flow diversion around the entire opening under steady-state conditions. Late-time 
data are more representative of near-steady conditions and are less influenced by storage effects. 
Moreover, the relatively large amount of released water at late time has likely encountered a 
significant portion of the capillary barrier. As a result, the late-time seepage data better reflect 
average conditions on the scale of interest. 
The duration of the liquid-release tests is on the order of days and weeks, whereas the calibrated 
parameters are intended to be used in a steady-state prediction model (the SMPA). Nevertheless, 
the late-time seepage-rate data are considered suitable for calibrating a model that subsequently 
will be used for the prediction of long-term seepage behavior. The approach is appropriate for 
the following reasons. First, any data that are sensitive to the parameters of interest are generally 
adequate for model calibration. There is no inherent requirement that the data used for model 
calibration have to reflect steady-state conditions if the ultimate purpose of the model is to 
predict steady-state behavior. If the model is capable of capturing the transient effects occurring 
during the liquid-release tests, no unwanted bias is introduced; such a bias would only be 
introduced if a steady-state model were calibrated against non-steady-state data. The SCM is a 
transient model that simulates time-dependent liquid release, flow, storage, and seepage 
processes. If the SCM can be successfully calibrated and validated, the parameters determined 
by inverse modeling are not affected by the transient nature of the underlying data, and thus they 
are also suitable for the prediction of steady-state seepage. Second, the late-time seepage-rate 
data used for model calibration show near-steady behavior, i.e., they do not change significantly 
with time. They closely reflect the processes governing steady-state seepage and are thus 
suitable as calibration data for a prediction model of long-term seepage into waste emplacement 
drifts. 
The capillary-strength parameter will be determined by calibrating the model against multiple 
tests using different liquid-release rates. Some of these release rates induced a local percolation 
flux above the seepage threshold, i.e., water dripped into the opening and yielded seepage-rate 
data valuable for calibration. However, the joint inversion of multiple data sets also included 
data from tests performed below the seepage threshold. Moreover, the model was validated 
against tests conducted above and below the seepage threshold. That is, the system is probed and 
the model will be calibrated and validated for the critical range of percolation rates about the 
seepage threshold. Seepage predictions for natural percolation fluxes, that are even lower than 
the low fluxes (below the seepage threshold) induced during the low-rate tests, will yield the 
correct result, namely zero seepage. As a result of a high-infiltration climate or strong flow 
focusing, the natural percolation flux may be high and exceed the seepage threshold. This would 

be the critical scenario for performance. Obviously, the parameters estimated from the 
liquid-release tests would be most suitable for those critical circumstances, because they were 
determined under similar high-rate conditions. In summary, the parameters determined from 
relatively high-rate liquid-release tests are appropriate and provide a solid basis for seepage 
predictions under low and higher natural percolation fluxes. 
Liquid-release tests directly supporting the SCM were conducted in two different hydrogeologic 
units, in multiple test beds, boreholes, and intervals. Each test event probes a different portion of 
the rock and a different section of the underground opening. The question arises how the 
available data should be combined to yield suitable averages and reasonable measures of 
variability and uncertainty, which are needed for model validation and the subsequent PA 
calculations. The goal is to obtain a probability density function of the seepage-relevant 
parameters that reflects both estimation uncertainty and spatial variability. These two aspects are 
discussed separately in the following paragraphs. 
Parameter estimates determined by inverse modeling are uncertain because they are derived from 
limited data, which exhibit random and potentially systematic measurement errors, and because 
the model is a simplification of the real system, which introduces systematic and random 
modeling errors. As discussed above, estimating model-related parameters mitigates the impact 
of some of the residual systematic errors. Estimation uncertainty as a result of random noise in 
the seepage data is relatively minor (see Sections 6.6.3.3 and 8.2). However, there remains 
irreducible uncertainty because of small-scale heterogeneity that affects individual seepage tests. 
The details of these small-scale heterogeneities are unknown (i.e., they cannot be described 
deterministically) and vary from location to location (i.e., they are spatially variable). 
Consequently, they are considered the result of a stochastic process that must be described by 
geostatistical parameters and modeled by performing multiple geostatistical simulations. Each 
seepage data set is obtained from a certain test bed (niche or section of the ECRB Cross-Drift); it 
can be considered one realization from a number of statistically similar geologic systems. The 
lack of knowledge regarding the details of this specific realization makes the inversely 
determined parameter estimate uncertain. This uncertainty is examined by performing multiple 
inversions of the same data set using different realizations of the underlying heterogeneous 
permeability field, yielding a distribution of estimated capillary-strength parameters rather than a 
single value. In addition to capturing the random nature of the permeability field and its impact 
on seepage, each realization will induce some ergodic fluctuations, which reflect the fact that the 
model statistics are inferred from sparse air-permeability sampling (i.e., they are not deemed 
exactly representative of the population statistics). The average of all inversions performed with 
different permeability fields for a given interval yields one estimate representative of that 
location. 
The average parameters obtained from multiple simultaneous inversions of one or more seepage 
events conducted in a certain test interval are considered independent, each reflecting the 
seepage-relevant properties at a given location on the drift scale. These estimates are then 
combined to yield a parameter distribution for the entire hydrogeologic unit. This distribution 
reflects spatial variability. By sampling from the distribution of the resulting parameter 
estimates, probabilistic predictions of seepage across the repository horizon can capture the 
spatial variability of average seepage on the scale of the 5 m long drift segment. Uncertainty in 
this average seepage rate as a result of small-scale heterogeneity is calculated based on multiple 

seepage prediction runs by the SMPA, using multiple realizations of the underlying permeability 
field. 
A comparison of seepage predictions with observable data (such as seepage data from transient 
liquid-release tests involving a finite amount of water) is a necessary step in model development 
and confidence building. However, models are often developedand most usefullyto infer 
behavior that cannot be directly observed (such as long-term near-steady seepage under naturally 
low percolation fluxes). The appropriateness of such an extrapolation of the model beyond its 
tested grounds needs to be assessed. While rigorous model testing is fundamentally not possible 
[Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences 
(Oreskes et al. 1994 [DIRS 152512])], validation of the model for a limited purpose can be 
accomplished. The SCM is partially tested against observable data from seepage experiments 
that were not used for model calibration. The purpose of this validation exercise is to determine 
whether the model is appropriate and adequate for its intended use. Remaining uncertainty will 
be quantified during the seepage abstraction process and propagated through the PA models. 
Additional remarks about model validation can be found in Section 7.1. 
The development of the SCM involves the following steps (note that this is a general description; 
details about the implementation and execution of these steps can be found in Sections 6.6.2, 
6.6.3, and the appendices): 
1. Geostatistical parameters of the permeability field are determined from the results of 
air-injection test data. 
2. Multiple realizations of the permeability field are generated, each being consistent 
with the geostatistical properties of the measured air permeabilities. 
3. A numerical mesh is generated. This step involves (a) making a primary 3-D grid, (b) 
translating coordinates to center the mesh, (c) mapping the permeability field onto the 
mesh, (d) cutting out the opening (niche or drift) from the mesh, (e) adding top and 
bottom boundary elements as well as an evaporation boundary in the opening, and (f) 
modifying elements representing injection intervals. 
4. An input file defining the forward problem is prepared. This step involves (a) 
assembling parameters representing hydrogeologic properties, (b) assigning 
appropriate properties to elements representing the excavation and borehole intervals, 
(c) extracting the background percolation flux from the UZ Model, (d) assigning 
appropriate initial and boundary conditions, and (e) selecting computational 
parameters and program options. 
5. Simulations with the background percolation flux applied at the top of the model are 
run to steady state to obtain initial conditions for the subsequent simulation of transient 
seepage experiments. 
6. Injection rates are specified as time-dependent source terms. 
7. Test events are selected for calibration. Seepage rates are calculated from the 
cumulative seepage data. 

8. An input file defining the inverse problem is prepared. This step involves (a) selecting 
the parameters to be estimated and their initial guesses, (b) selecting points in time at 
which calibration should occur, (c) specifying the data against which calibration 
should occur, and (d) selecting computational parameters and program options. 
9. Seepage-relevant, model-related parameters are determined by automatic model 
calibration using iTOUGH2 V4.0 (LBNL 1999 [DIRS 139918]) and V5.0 
(LBNL 2002 [DIRS 160106]). In each inversion, seepage-rate data from multiple test 
events are jointly inverted. 
10. The model is tested by comparing predicted seepage rates to observed data from 
seepage experiments not used during model calibration. Prediction uncertainty is 
calculated by iTOUGH2 V4.0 (LBNL 1999 [DIRS 139918]) and V5.0 
(LBNL 2002 [DIRS 160106]) using first-order-second-moment (FOSM) uncertainty 
propagation analysis and Monte Carlo simulations. 
6.4 ALTERNATIVE CONCEPTUAL MODELS 
The following subsections contain short descriptions of potential alternative ways to evaluate 
seepage into waste emplacement drifts at Yucca Mountain. These alternative conceptual models 
are discussed in a qualitative manner, and references to more detailed analyses are given, if 
available. No quantitative evaluations of these alternative conceptual models are presented in 
this Model Report. 
Natural analogues for seepage also support the concepts of the base-case model; they are briefly 
reviewed in Section 7.2.1. 
In general, the choice of a conceptual model should be based on a careful consideration of the 
study objectives, the available database in comparison with the data needs, the uncertainty in the 
input parameters and the corresponding prediction uncertainties and computational aspects. 
6.4.1 Discrete Fracture Network Model 
A discrete fracture network model (DFNM) is an alternative conceptual model to the 
heterogeneous continuum model used in this Model Report [Alternative Concepts and 
Approaches for Modeling Flow and Transport in Thick Unsaturated Zones of Fractured Rocks 
(Pruess et al. 1999 [DIRS 117112], pp. 307309)]. A high-resolution DFNM is capable of 
generating channelized flow and discrete seepage events, as demonstrated by Finsterle 
(2000 [DIRS 151875], Plate 1) and Liu et al. (2002 [DIRS 160230], Figure 5). Note that 
two-dimensional DFNMs (such as those shown in Figure 6-2 and Figure 6-3) make the implicit 
assumption that the fractures are oriented parallel to the drift axis. This assumption exaggerates 
the discreteness of the flow and seepage behavior as flow diversion within the fracture plane is 
not possible (see also Figure 6-1 and related discussion in Section 6.3.2). 
In a DFNM, an individual fracture is discretely represented by appropriately small computational 
grid blocks. This is often considered the defining feature of such a model. (Note, however, that 
the flow equations solved in and between grid blocks are essentially identical to those solved by 
a continuum model. As outlined in Section 6.3.2, in-plane flow diversion is the key mechanism 

characterizing the capillary barrier effect and determining the seepage threshold. In comparison, 
the geometrical details and discreteness of fracture-to-fracture flow are secondary aspects for 
seepage.) 
(a) High-Resolution Permeability Field b) D 
(b) Flow Paths 
(a) Fracture Network 
Liu et al. (2002 [DIRS 160230], Figures 1 and 2). 
Figure 6-3. Two-Dimensional Discrete Fracture Network Model 

The development of a defensible DFNM requires collecting a very large amount of geometric 
and hydrologic data. While part of the required geometric information can be obtained from 
fracture mappings, the description of the network remains incomplete and potentially biased 
towards fractures of a certain orientation and a certain size. Moreover, unsaturated hydrological 
parameters on the scale of individual fractures are required, along with conceptual models and 
simplifying assumptions regarding unsaturated flow within fractures and across fracture 
intersections. The databases required to develop a defensible DFNM are currently not available 
and are generally difficult or even impossible to obtain for site-specific simulations. The 
cumulative effect of the input uncertainties is likely to outweigh the apparent advantage of a 
detailed representation of the fracture network, specifically since the DFNM must be calibrated 
against hydrogeologic data to reduce prediction uncertaintythat is, an approach very similar to 
that outlined in Section 6.3.4 must be followed. 
The appropriateness of using a continuum model for the prediction of average seepage quantities 
was demonstrated by Finsterle (2000 [DIRS 151875]). In that study, seepage predictions with a 
calibrated fracture continuum model were compared to those of a DFNM, yielding consistent 
results even when applied outside the range of calibration. Given these results, the parsimony of 
the continuum model is considered a key advantage over the complexity of the DFNM, which is 
difficult to support or justify despite its visual appeal. Moreover, a two-dimensional DFNM is 
not capable of capturing flow diversion within the fracture plane, a mechanism appropriately 
represented by a 2-D (or 3-D) continuum model. 
For the reasons outlined above, the full development of a DFNM as a potential alternative to the 
base-case continuum model was considered unwarranted, infeasible, and unnecessary. While 
seepage calculations with a calibrated DFNM are likely to corroborate the findings of this Model 
Report, this approach is not further considered. 
6.4.2 Seepage Governed by Ponding Probability 
As an alternative conceptual model to a seepage process model, Birkholzer et al. (1999 
[DIRS 105170], pp. 372379) related seepage to the local ponding probability, which was 
derived from the variability of the permeability field. Their approach assumed thatin strongly 
heterogeneous formationsseepage is predominantly affected by pressure variations governed 
by local heterogeneity rather than the presence and geometry of the capillary barrier. This is 
different from the behavior in a homogeneous system, where the geometry of the capillary 
barrier has a strong impact on seepage (Philip et al. 1989 [DIRS 105743]). Strong medium- to 
small-scale heterogeneities tend to increase seepage because they increase channeling and local 
ponding. This effect is included in the current seepage process models through the estimation of 
effective, seepage-specific parameters for a heterogeneous medium with a heterogeneous 
permeability field. While the approach presented by Birkholzer et al. (1999 [DIRS 105170], pp. 
372379) may provide guidelines for how to extrapolate seepage predictions to other units or 
drift geometries, it nonetheless requires a calibration step similar to that described in this Model 
Report. The approach is therefore not further considered. Nevertheless, the concept that 
ponding probability affects seepage is consistent with and thus corroborates the base-case model, 
which produces random seepage locations as a result of local ponding in a stochastic 
permeability field. 

6.4.3 Inferring Seepage from Geochemical Data 
Observations of calcite and opal in lithophysal cavities could be used to estimate long-term 
seepage rates into these small openings [Analysis for Geochemical Data for the Unsaturated 
Zone (BSC 2003 [DIRS 168343], Section 6.10.1)]. Calcite is assumed to precipitate from 
downward-percolating meteoric water because of (1) evaporation, (2) CO2 outgassing as a result 
of the geothermal gradient, and (3) interaction with a gas phase containing less CO2 than the gas 
with which the water was last equilibrated. Considering these calcite-precipitation mechanisms 
and assuming certain water-to-calcite ratios, seepage into lithophysal cavities can be estimated 
from calcite-deposition data. The analysis of calcite and opal precipitation data shows that (1) 
not all lithophysal cavities encountered seepage, and (2) seepage flux derived from mineral 
deposits is a very small fraction of percolation flux [Estimation of Past Seepage Volumes from 
Calcite Distribution in the Topopah Spring Tuff, Yucca Mountain, Nevada (Marshall et al., 
2003 [DIRS 162891], Section 5)]. Both conclusions corroborate the general concept of a 
capillary barrier reducing seepage below the value of the percolation flux. 
The advantage of using geochemical information to infer seepage is the fact that calcite and opal 
were deposited over a long period of time under natural percolation conditions. The 
disadvantage of this approach is that (1) seepage is inferred in an indirect manner, requiring a 
number of geochemical models with their associated assumptionsin addition to hydrogeologic 
model assumptions; (2) the calcite depositions on lithophysal cavity floors may not originate 
from dripping water (i.e., seepage); in fact, there is a lack of evidence of dripping from cavity 
ceilings (absence of stalactites or stalagmites), even where fractures containing coatings intersect 
lithophysae ceilings [Physical and Stable-Isotope Evidence for Formation of Secondary Calcite 
and Silica in the Unsaturated Zone (Whelan et al. 2002 [DIRS 160442], p. 744)]; (3) the data 
reflect seepage into (small) cavities instead of seepage into a (large) waste emplacement drift; 
since the size of the underground opening impacts seepage in a nonlinear fashion, a hydrological, 
physically based process model is required to estimate seepage on the scale of interest; 
(4) seepage into lithophysal cavities does not include potential impacts from the excavation-
disturbed zone around a mechanically constructed drift; and (5) the historic record and the 
approach does not allow making predictions into the future under changed conditions. 
As shown by Marshall et al. [Seepage Flux Conceptualized from Secondary Calcite in 
Lithophysal Cavities in the Topopah Spring Tuff, Yucca Mountain, Nevada (2000 
[DIRS 151018], Figure 1), the seepage rates estimated from the calcite-deposition data are 
significantly lower than those predicted by TSPA using data derived from the SMPA, which is 
based on the methodology outlined in this Model Report. Inferring seepage from secondary 
mineral depositions in lithophysal cavities is not further considered as an approach to 
quantitatively estimate seepage into waste emplacement drifts. 
6.4.4 Inferring Seepage Threshold Directly From Liquid-Release Tests 
Trautz and Wang (2002 [DIRS 160335], Section 5) estimated the seepage threshold directly from 
the liquid-release test data, based on a number of simplifying assumptions (with regard to the 
cross-sectional area of the flow path between the borehole and the ceiling, evaporation, and the 
steady-state flow field). Once the seepage threshold was determined, a capillary-strength 
parameter was derived assuming seepage into a cylindrical cavity excavated from a 

homogeneous porous medium (Trautz and Wang 2002 [DIRS 160335], Section 6). The base-
case model outlined in this Model Report relies on fewer assumptions than the simplified 
alternative conceptual model and predicts a lower seepage threshold; the base-case model 
described in this Model Report is therefore the preferred conceptualization. 
6.5 DESCRIPTION OF SEEPAGE EXPERIMENTS 
6.5.1 Test Location and Borehole Configuration 
The data used for the development, calibration, and validation of the SCM were collected as part 
of the ESF Drift Seepage Test and Niche Moisture Study, an ongoing field-testing program. 
Drift-scale seepage tests were initiated in 1997 to investigate potential seepage into an 
underground opening representing a waste emplacement drift. Short drifts ranging from 6.3 m to 
15.0 m in length were constructed at various locations along the ESF and the ECRB Cross-Drift. 
Boreholes were installed prior to and after the drifts were excavated to facilitate characterization 
of the rock using air-injection tests and investigation of seepage processes using liquid-release 
tests. The short excavations are called niches, and the drift-scale seepage tests are collectively 
referred to as the Niche Study. In Niche 5, a horizontal slot on the side of the niche 
(also referred to as batwing) was excavated to obtain direct evidence of the flow-diversion 
capability of the capillary barrier (see Section 6.8). 
A second study referred to as the Systematic Borehole Testing Program was initiated in 2000 to 
complement the niche seepage experiments. The purpose of the program is to provide broad, 
systematic coverage and characterization of the lower lithophysal zone (Tptpll) of the Topopah 
Spring welded unit (TSw). Systematic characterization of the Tptpll is accomplished by 
performing air-injection and liquid-release tests in approximately 20 m long boreholes drilled 
into the ceiling approximately every 30 m along the ECRB Cross-Drift. 
The data used in this Model Report are a subset of seepage tests from the Niche Studies and the 
Systematic Borehole Testing Program. A few tests failed and their data are not used in this 
Model Report (see discussion of Table 6-5 below). Data include air permeabilities and 
seepage-rate values from tests conducted at three niche sites located along the Main Drift of the 
ESF, one niche in the ECRB Cross-Drift, and in three systematic testing boreholes drilled into 
the ceiling of the Cross-Drift (see Figure 6-4). The first three niche sites are located along the 
west side of the ESF in the Tptpmn and they were selected for study based on fracture and 
hydrologic data collected in the ESF. The first niche site at construction station 
(CS) 31+07 (Niche 3107, hereafter referred to as Niche 3) consists of a 6.3 m long drift located 
in an area of relatively low fracture density. Niche 3 is located in close proximity to CS 30+62, 
where the Cross-Drift crosses over the Main Drift of the ESF. The second niche site, at 
CS 36+50 (Niche 3650, hereafter referred to as Niche 2), consists of a 9 m long drift located in a 
competent rock mass exhibiting relatively moderate fracture density. The third niche site, at 
CS 47+88 (Niche 4788, hereafter referred to as Niche 4), consists of an 8.2 m long drift located 
in a 950 m long exposure of an intensely fractured zone. Fractures in this zone are not uniformly 
spaced, but instead they occur in clusters of closely spaced fractures. The 15.0 m-long Niche 5 is 
located on the south side of the ECRB Cross-Drift in the Tptpll. 

NOTE: The shape of the openings is approximate. 
Figure 6-4. Schematic Geologic Map Showing Approximate Location of Niches and Systematic 
Testing Boreholes SYBT-ECRB-LA#13 
Prior to niche excavation, horizontal boreholes were drilled to gain access to the rock for testing 
and monitoring purposes. The boreholes above each niche are approximately one meter apart 
and within the same horizontal plane. Table 6-3 provides the correlation between the borehole 
designations shown in the schematic cross sections of Figure 6-4 (and used throughout this 
document) and their respective designations in the survey DTN. Note that throughout Project 
documents, the systematic testing boreholes are designated as either SYBT-ECRB-LA#x or 
ECRB-SYBT-LA#x; these designations are unambiguous and thus interchangeable without loss 
of traceability. The format SYBT-ECRB-LA#x is used in this Model Report, consistent with 
most DTN entries. 

Table 6-3. Borehole Designations in Niches 
Niche 
Borehole 
Designation in DTN 
DTN of Borehole Survey 
3107 
(Niche 3) 
UL 
UM 
UR 
ESF-MD-NICHE 3107 #5 
ESF-MD-NICHE 3107 #6 
ESF-MD-NICHE 3107 #7 
MO0002GSC00064.000 
[DIRS 152625] 
3650 
(Niche 2) 
UL 
UM 
UR 
ESF-MD-NICHE 3650 #1 
ESF-MD-NICHE 3650 #2 
ESF-MD-NICHE 3650 #3 
MO0002GSC00076.000 
[DIRS 152623] 
4788 
(Niche 4) 
UL 
UM 
UR 
ESF-MD-NICHE 4788 #5 
ESF-MD-NICHE 4788 #6 
ESF-MD-NICHE 4788 #7 
MO0107GSC01069.000 
[DIRS 156941] 
1620 
(Niche 5) 
#2 
#3 
#4 
#5 
#6 
#7 
ECRB-NICHE 1620 #2 
ECRB-NICHE 1620 #3 
ECRB-NICHE 1620 #4 
ECRB-NICHE 1620 #5 
ECRB-NICHE 1620 #6 
ECRB-NICHE 1620 #7 
MO0312GSC03176.000 
[DIRS 169532] 
NOTE: No liquid-release tests were performed in Niche 3566 (Niche 1). 
DTN=Data Tracking Number; UL=upper left; UM=upper middle; UR=upper right 
The boreholes listed in Table 6-3 are approximately parallel to the niche axis. Air-injection tests 
were conducted in several, 1 ft (0.3 m) long, packed-off intervals, both prior to and after niche 
excavation, to determine the permeability distribution of the formation, as well as to study 
potential permeability changes as a result of stress relief during niche excavation. After niche 
construction, water was injected at a specified rate into intervals of the same boreholes to 
observe, document, and quantify any water migrating to and seeping into the niche. 
The systematic testing boreholes SYBT-ECRB-LA#1, 2, and 3 are drilled from the ECRB and 
located in the moderately to densely welded, devitrified, and vapor-phase altered lower 
lithophysal zone (Tptpll). 
Borehole SYBT-ECRB-LA#1 is collared from the drift crown at ECRB construction station CD 
17+49. It is upward-inclined at nominal 15 from the drift axis. Packers are set to isolate an 
injection zone between 10 ft (3.0 m) and 16 ft (4.9 m) (zone 2) from the collar 
(DTN: LB0110ECRBLIQR.002 [DIRS 156879]). Borehole SYBT-ECRB-LA#2 is collared 
from the drift crown at ECRB construction station CD 17+26. It is upward-inclined at nominal 
15 from the drift axis. Packers are set to isolate three 6 ft (1.8 m) long injection zones between 
17 ft (5.2 m) and 23 ft (7.0 m) (zone 1), 33 ft (10.1 m) and 39 ft (11.9 m) (zone 2), and 49 ft 
(15.0 m) and 55 ft (16.8 m) (zone 3) from the collar (DTN: LB00090012213U.002 
[DIRS 153154]). Borehole SYBT-ECRB-LA#3 is collared from the drift crown at ECRB 
construction station CD 16+95. It is upward-inclined at nominal 15 from the drift axis. Packers 
are set to isolate three 6 ft (1.8 m) long injection zones between 18 ft (5.5 m) and 24 ft (7.3 m) 
(zone 1), 34 ft (10.4 m) and 40 ft (12.2 m) (zone 2), and 50 ft (15.2 m) and 56 ft (17.1 m) (zone 
3) from the collar (DTN: LB0203ECRBLIQR.001 [DIRS 158462]). 

6.5.2 Air-Injection Tests 
The purpose of the air-injection tests was to estimate permeabilities as a basis for the stochastic 
generation of heterogeneous permeability fields. The tests were performed by isolating a short 
section of the boreholes (1 ft [0.3 m] in niches, 6 ft [1.8 m] in systematic testing borehole 
SYBT-ECRB-LA#2), using an inflatable packer system, and then injecting compressed air at a 
constant rate into the isolated injection interval. The pressure buildup in the injection interval 
and in nearby observation intervals was monitored with time until steady-state conditions were 
reached, which typically occurred within a few minutes. Air injection was terminated after 
reaching steady-state pressures, and the decline in air pressure was then monitored as it 
recovered to its initial pre-test condition. Air-permeability values were derived from the 
steady-state pressure data (BSC 2004 [DIRS 170004], Section 6.1.2) based on a commonly used 
analytical solution [Pneumatic Testing in 45-Degree-Incline Boreholes in Ash-Flow Tuff Near 
Superior, Arizona (LeCain 1995 [DIRS 101700], p. 10, Eq. (15))]. 
The air permeabilities around the niches and the ECRB Cross-Drift are affected by excavation 
(BSC 2004 [DIRS 170004], Section 6.1.2.2; Permeability Changes Induced by Excavation in 
Fractured Tuff (Wang and Elsworth 1999 [DIRS 104366]), pp. 752756). Since seepage is 
determined by the formation properties in the immediate vicinity of the opening, it is reasonable 
to use post-excavation air-permeability data for seepage calculations. Note that the perturbation 
of the permeability in the drift vicinity depends on the excavation method. A tunnel-boring 
machine is used for the excavation of the ECRB, whereas a road header is used to mine out the 
niches. Since local post-excavation air-permeability values are directly used for the analysis of 
seepage-rate data, no bias is introduced. The permeabilities used during TSPA-LA are sampled 
from a distribution that describes variability and uncertainty, including uncertainty induced by 
excavation effects (BSC 2004 [DIRS 169131], Section 6.6). 
These permeabilities are considered representative of the absolute permeability of the 
excavation-disturbed zone around the opening, because the post-excavation air-injection tests 
were conducted in a network of essentially dry fractures, i.e., no empirical relative permeability 
function is needed to translate air conductivity into absolute permeability. Since air-injection 
tests are a standard method to obtain permeability values, the use of these values during both 
calibration and prediction of seepage ensures consistency. 
The distributions representing variability and uncertainty in permeability (BSC 2004 
[DIRS 169131], Section 6.6) were developed also based on air-permeability data. This 
consistency reduces the impact of a potential bias. Data that are located outside the footprint of 
the niches were removed from the data set (Ahlers 2002 [DIRS 161045], p. 20; Trautz 2001 
[DIRS 161044], p. 20) because they represent a separate population of air permeabilities 
performed in an area of relatively undisturbed, lower-permeability rock. Mean and standard 
deviations for each of the four locations are summarized in Table 6-4. Here, standard deviations 
reflect spatial variability within the test bed. The number of log-permeability values available is 
indicated in the last column. Mean permeabilities and their spatial variability as calculated for 
the three niches located in the middle nonlithophysal zone are consistent with one another. 
Permeability in the lower lithophysal zone is approximately one order of magnitude larger. The 
variability as measured in Niche 5 is significantly larger than that obtained in borehole SYBT-
ECRB-LA#2. This is partly a result of the injection intervals of borehole SYBT-ECRB-LA#2 

being six times longer than those in Niche 5 are. Note no air-permeability data are available 
from boreholes SYBT-ECRB-LA#1 and SYBT-ECRB-LA#3 because of equipment problems 
during air-injection testing. 
Table 6-4. Mean and Standard Deviation of Post-Excavation Log-Air-Permeability Values 
Location 
Input DTN 
Scientific Notebook Reference 
Mean 
Log 
(k [m2]) 
Std. 
Dev.
Na
Niche 2 
LB0011AIRKTEST.001 
[DIRS 153155] 
Trautz 2001 [DIRS 161044], pp. 1925 
-11.66 
0.72
84 
Niche 3 
LB990601233124.001 
[DIRS 105888] 
Ahlers 2002 [DIRS 161045], pp. 3940 
-12.14 
0.80
78 
Niche 4 
LB990601233124.001 
[DIRS 105888] 
Ahlers 2002 [DIRS 161045], pp. 1521 
-11.79 
0.84
63 
Niche 5 
LB0110AKN5POST.001 
[DIRS 156904] 
Wang 2003 [DIRS 161456], 
SN-LBNL-SCI-223-V1, pp. 1920 
-10.95 
1.31
61 
SYBT-ECRB-LA#2 
LB00090012213U.001 
[DIRS 153141] 
Finsterle 2002 [DIRS 161043], pp. 5455 
-10.73 
0.21
6 
a Number of log-permeability values 
DTN=Data Tracking Number; Std Dev=Standard Deviation 
6.5.3 Liquid-Release Tests 
Multiple liquid-release tests were performed in the niches and the ECRB Cross-Drift to 
characterize seepage into a large underground opening (BSC 2004 [DIRS 170004], Sections 6.2 
and 6.11). The tests were performed by sealing a short section of the borehole above the opening 
using an inflatable packer system and then releasing water at a specified rate into the isolated test 
interval. Any water that migrated from the borehole to the ceiling and dripped into the opening 
was captured and weighed. Only a small amount of water (approximately one liter per test 
event) was released during testing at Niche 2, and only the total amount of water that seeped into 
the capture system was recorded. Seepage experiments at Niches 3, 4, 5, and in the systematic 
testing boreholes SYBT-ECRB-LA#13 involved significantly more water, which was injected 
over longer periods, and cumulative seepage was recorded as a function of time. 
In many intervals, multiple liquid-release tests were conducted using different injection rates 
with different lengths of inactivity between individual test events. The reason for using different 
injection rates and different injection schedules was to collect data that are sensitive to 
percolation rate and water storage effects. While the inverse modeling approach pursued in this 
Model Report does not require data above and below the seepage threshold, increasing the 
sensitivity of the data to seepage-related effects improves the identifiability of seepage-relevant 
parameters. 
Table 6-5 summarizes the test events used for the calibration and validation of the SCM. The 
approximate release rate (defined as the injection rate minus the return flow) is indicated in 
Column 4. As shown in Column 5, 53 out of 90 test events led to seepage into the capture 
system. Potential seepage was not recorded in two cases (Events 5 and 46) because of an 
equipment failure. While no data are available to be used for calibration or validation from these 

three test events, the injections that occurred were nevertheless modeled because the released 
water has a potential impact on subsequent test events. Column 6 indicates whether a specific 
test event was used for calibration (C) or validation (V). The selection of each test event for 
calibration or validation purposes is discussed in detail in Section 6.6.3.2. A few additional 
seepage tests were conducted in Niche 2 that were not used, because only a very small amount of 
water was released and generally, no seepage was observed. Injection attempts at zone 3 of 
borehole SYBT-ECRB-LA#3 (Event 77) failed because the zone was too tight. A few test 
events in Niche 5 were not analyzed because of various difficulties (Events 79 and 80: packer 
problem; Event 82: seepage partially bypassed capture system; Event 83: pump problem). 
During Events 87 and 88 in borehole SYBT-ECRB-LA#5, water from Events 83 and 
85 (conducted in the neighboring borehole SYBT-ECRB-LA#3) entered the seepage collection 
system, interfering with the test results (see detailed discussion of Figure 6-17 below). 
The events without any seepage cannot be used for calibration (unless jointly inverted with other 
tests that exhibit seepage), because the corresponding inverse problem would be ill posed. These 
tests (along with tests showing seepage) are therefore used for validation of the SCM. The small 
amount of water released during the short-term tests performed in Niche 1 makes it difficult to 
reliably estimate seepage parameters on the drift scale. If used for calibration, these tests yield 
small-scale parameter values that are biased towards the properties of the few fractures 
connecting the release point with the niche ceiling. These fractures may not be representative of 
the fracture network taking part in the diversion of water around the entire niche, which is the 
behavior to be modeled under steady-state flow conditions. Moreover, storage effects are 
significant in short-term tests but are also poorly identifiable. For these reasons, the Niche 2 
liquid-release tests are used for validation purposes only. 
The calculation of seepage rates from cumulative seepage data is described in Appendix F. 
Table 6-5. Liquid-Release Test Events, Approximate Release Rate, Occurrence of Seepage, and Their 
Use for Calibration or Validation Purposes 
Event 
Starting Date 
of Test 
Borehole, Interval
Approximate 
Release Rate 
[ml/min] 
Seepage? 
Calibration, 
Validation 
Niche 2, DTN: LB980001233124.004 [DIRS 136583] 
13 
12/11/97 
UL, 5.185.49 m 
4.7 
No 
V 
14 
02/12/98 
UL, 5.185.49 m 
0.4 
No 
V 
15 
12/11/97 
UL, 5.796.10 m 
12.1 
No 
V 
16 
12/11/97 
UL, 6.406.71 m 
12.7 
No 
V 
17 
12/10/97 
UL, 7.017.32 m 
116.9 
Yes 
V 
18 
01/06/98 
UL, 7.017.32 m 
11.4 
No 
V 
19 
11/13/97 
UM, 4.274.57 m 
121.1 
Yes 
V 
20 
12/03/97 
UM, 4.274.57 m 
30.2 
Yes 
V 
21 
12/03/97 
UM, 4.274.57 m 
30.4 
Yes 
V 
22 
01/07/98 
UM, 4.274.57 m 
2.8 
Yes 
V 
23 
02/10/98 
UM, 4.274.57 m 
1.0 
No 
V 
24 
11/12/97 
UM, 4.885.18 m 
173.5 
Yes 
V 

Table 6-5. Liquid-Release Test Events, Approximate Release Rate, Occurrence of 
Seepage, and their Use for Calibration or Validation Purposes (Continued) 
Event 
Starting Date 
of Test 
Borehole, Interval
Approximate 
Release Rate 
[ml/min] 
Seepage? 
Calibration, 
Validation 
25 
12/04/97 
UM, 4.885.18 m 
30.4 
Yes 
V 
26 
12/05/97 
UM, 4.885.18 m 
8.6 
Yes 
V 
27 
01/08/98 
UM, 4.885.18 m 
2.8 
No 
V 
28 
03/06/98 
UM, 4.885.18 m 
0.8 
No 
V 
29 
11/13/97 
UM, 5.495.79 m 
124.1 
Yes 
V 
30 
12/04/97 
UM, 5.495.79 m 
30.2 
Yes 
V 
31 
01/09/98 
UM, 5.495.79 m 
3.5 
Yes 
V 
32 
02/11/98 
UM, 5.495.79 m 
0.8 
No 
V 
33 
11/13/1997 
UM, 6.106.40 m 
30.8 
No 
V 
34 
12/04/1997 
UM, 6.106.40 m 
11.5 
No 
V 
35 
01/12/1998 
UM, 6.106.40 m 
47.5 
No 
V 
36 
01/14/1998 
UR, 4.274.57 m 
11.9 
Yes 
V 
37 
02/05/1998 
UR, 4.274.57 m 
3.3 
No 
V 
38 
01/15/1998 
UR, 4.885.18 m 
11.4 
Yes 
V 
39 
02/06/1998 
UR, 4.885.18 m 
3.2 
No 
V 
Niche 3, DTN: LB0010NICH3LIQ.001 [DIRS 153144] 
1 
03/10/99 
UL, 5.495.80 m 
1.5 
No 
V 
2 
03/30/99 
UL, 5.495.80 m 
2.0 
No 
V 
3 
09/17/99 
UL, 5.495.80 m 
1.5 
No 
V 
4 
03/04/99 
UM, 4.885.18 m 
0.9 
No 
C 
5 
04/07/99 
UM, 4.885.18 m 
5.8 
 a
 a
6 
04/27/99 
UM, 4.885.18 m 
2.4 
Yes 
C 
Niche 3, DTN: LB0010NICH3LIQ.001 [DIRS 153144] (Continued) 
7 
04/30/99 
UM, 4.885.18 m 
0.8 
No 
V 
8 
05/06/99 
UM, 4.885.18 m 
5.4 
Yes 
C 
9 
09/21/99 
UM, 4.885.18 m 
5.0 
Yes 
V 
10 
09/23/99 
UM, 4.885.18 m 
5.3 
Yes 
V 
11 
09/27/99 
UM, 4.885.18 m 
5.4 
Yes 
V 
12 
10/11/99 
UM, 4.885.18 m 
5.4 
Yes 
V 
Niche 4, DTN: LB0010NICH4LIQ.001 [DIRS 153145] 
40 
11/03/1999 
UL, 7.627.93 m 
5.5 
Yes 
V 
41 
11/30/1999 
UL, 7.627.93 m 
3.1 
Yes 
C 
42 
01/24/2000 
UL, 7.627.93 m 
0.5 
No 
V 
43 
06/26/2000 
UL, 7.627.93 m 
1.2 
Yes 
C 
44 
11/16/1999 
UM, 6.106.40 m 
5.5 
Yes 
V 
45 
12/10/1999 
UM, 6.106.40 m 
2.3 
Yes 
C 
46 
02/09/2000 
UM, 6.106.40 m 
0.5 
 a
 a
47 
03/14/2000 
UM, 6.106.40 m 
0.5 
No 
V 
48 
06/08/2000 
UM, 6.106.40 m 
1.2 
Yes 
C 
49 
12/07/1999 
UR, 5.185.48 m 
5.5 
Yes 
V 

50 
01/05/2000 
UR, 5.185.48 m 
2.4 
Yes 
C 
51 
02/14/2000 
UR, 5.185.48 m 
0.5 
Yes 
C 
SYBT-ECRB-LA#2, DTN: LB00090012213U.002 [DIRS 153154] 
52 
05/11/2000 
LA#2, zone 1 
>450 
Yes 
V 
53 
05/17/2000 
LA#2, zone 1 
34.9 
Yes 
V 
54 
05/23/2000 
LA#2, zone 1 
26.3 
Yes 
V 
55 
05/23/2000 
LA#2, zone 2 
29.5 
Yes 
V 
56 
06/01/2000 
LA#2, zone 2 
31.6 
Yes 
V 
57 
05/17/2000 
LA#2, zone 3 
16.8 
No 
V 
58 
05/23/2000 
LA#2, zone 3 
26.1 
No 
V 
59 
06/01/2000 
LA#2, zone 3 
35.6 
No 
V 
60 
06/14/2000 
LA#2, zone 3 
37.8 
Yes 
V 
SYBT-ECRB-LA#2, DTN: LB0110SYST0015.001 [DIRS 160409] 
61 
10/23/2000 
LA#2, zone 2 
33.0 
Yes 
C 
62 
11/27/2000 
LA#2, zone 2 
35.3 
Yes 
C 
63 
10/23/2000 
LA#2, zone 3 
38.0 
Yes 
C 
64 
11/27/2000 
LA#2, zone 3 
40.8 
Yes 
C 
SYBT-ECRB-LA#1, DTN: LB0110ECRBLIQR.002 [DIRS 156879] 
65 
02/28/2001 
LA#1, zone 2 
17.0 
No 
C 
66 
04/03/2001 
LA#1, zone 2 
41.2 
Yes 
C 
67 
04/09/2001 
LA#1, zone 2 
43.9 
Yes 
C 
68 
04/17/2001 
LA#1, zone 2 
44.5 
Yes 
C 
69 
04/25/2001 
LA#1, zone 2 
43.1 
Yes 
C 
SYBT-ECRB-LA#3, DTN: LB0203ECRBLIQR.001 [DIRS 158462] 
70 
05/17/2001 
LA#3, zone 1 
36.4 
No 
C 
71 
05/23/2001 
LA#3, zone 1 
24.7 
Yes 
C 
72 
05/17/2001 
LA#3, zone 2 
71.2 
No 
V 
73 
06/20/2001 
LA#3, zone 2 
31.2 
No 
V 
74 
07/05/2001 
LA#3, zone 2 
65.7 
No 
V 
75 
07/13/2001 
LA#3, zone 2 
47.9 
No 
V 
76 
07/16/2002 
LA#3, zone 2 
32.4 
No 
V 
77 
05/17/2001 
LA#3, zone 3 
0.0b
No 
 b
Niche 5, DTN: LB0207NICH5LIQ.001 [DIRS 160408] 
78 
05/06/2002 
#5, 2829 ft 
72.0 
Yes 
V 
79 
05/06/2002 
#2, 2122 ft 
120.0 
No 
 c
80 
05/17/2002 
#2, 2122 ft 
120.0 
No 
 c
81 
05/16/2002 
#5, 2829 ft 
60.0 
Yes 
V 
82 
05/21/2002 
#5, 2829 ft 
72.0 
Yes 
 d

Niche 5, DTN: LB0209NICH5LIQ.001 [DIRS 160796] 
83 
07/17/2002 
#3, 2122 ft 
55.0 
Yes 
 e, f
84 
07/29/2002 
#3, 2122 ft 
33.0 
Yes 
 f
85 
08/14/2002 
#3, 2122 ft 
9.0 
Yes 
 g
86 
07/15/2002 
#5, 2829 ft 
25.8 
Yes 
C 
87 
07/31/2002 
#5, 2829 ft 
25.8 
Yes 
V 
88 
08/05/2002 
#5, 2829 ft 
11.3 
Yes 
V 
Niche 5, DTN: LB0211NICH5LIQ.001 [DIRS 160792] 
89 
09/17/2002 
#4, 1011 ft 
9.9 
Yes 
C 
90 
10/01/2002 
#4, 1011 ft 
4.8 
No 
V 
a Events 5 and 46: Potential seepage could not be determined because of an equipment failure 
b Event 77: Return flow from injection interval indicates that no water was released to the formation 
c Events 79 and 80: Packer problem 
d Event 82: Seepage partially bypassed capture system 
e Event 83: Pump problem 
f Events 83 and 84: Test interference (see discussion of Figure 6-17) 
g Event 85: Test too short for analysis 
UL=upper left; UM=upper middle UL=upper left; UM=upper middle 
6.5.4 Relative Humidity and Evaporation Rate Measurements 
Reduced relative humidity in the ESF Main Drift, the ECRB Cross-Drift, and the niches lead to 
partial evaporation of the water that reaches the opening, effectively reducing seepage. Note that 
a conservative treatment of a process in a forward model is nonconservative in an inverse model 
(and vice versa). Specifically, neglecting evaporation effects in a seepage prediction model 
(forward model) leads to higher seepage rates and is thus conservative. However, an 
overestimation of seepage in a model used for parameter determination (inverse model) is 
compensated by an increase in the estimated capillary-strength parameter, which is 
nonconservative if this parameter is subsequently used for seepage predictions. Following the 
recommendations made to address the evaporation issue [Seepage Calibration Model and 
Seepage Testing Data (CRWMS M&O 2001 [DIRS 153045], Section 7.5)], humidity in the 
closed-off Niches 3 and 4 was artificially increased to reduce the evaporation potential, and 
relative humidity was monitored (BSC 2004 [DIRS 170004], Figures 6-27 and 6-28). In the 
systematic testing area, additional curtains were installed on the two ends of the V-shaped 
seepage capture PVC curtains to reduce air circulation in the ventilated ECRB Cross-Drift (after 
June 2000). In addition, relative humidity and evaporation rates from an open pan were 
measured (see, for example, In Situ Field Testing of Processes (BSC 2004 [DIRS 170004], 
Figure 6-139). Relative humidity and evaporation rate were also measured in Niche 5 
(DTN: LB0207NICH5LIQ.001 [DIRS 160408] and DTN: LB0211NICH5LIQ.001 
[DIRS 160792]). 

The evaporation-rate data will be used to estimate the thickness of the diffusive boundary layer 
(see Section 6.6.1.4). The relative-humidity data will be applied as a time-dependent boundary 
condition determining the water potential in the opening. 
6.6 MODEL FORMULATION 
6.6.1 Mathematical Model 
The mathematical model for unsaturated flow is based on the conceptual model outlined in the 
previous sections. The basic theoretical foundation for unsaturated flow in a continuum is 
outlined first, with a short discussion of the capillary pressure curve and its relevance for seepage 
(Sections 6.6.1.1 and 6.6.1.2). The incorporation of evaporation from a wetted porous surface is 
described in Sections 6.6.1.3 and 6.6.1.4. Section 6.6.1.5 contains a summary description of the 
inverse modeling methodology. 
6.6.1.1 Unsaturated Flow 
Flow in unsaturated porous or fractured media is described by the rate of change in liquid 
saturation and the flow rate at any given point. The continuum concept (see Section 6.3.2) 
stipulates the following equation of continuity, which describes the rate at which liquid 
saturation changes at a given point (Bear 1972 [DIRS 156269], pp. 496, Eq. 9.4.39): 
(Eq. 6-1) 
Here, [s] is time, [dimensionless] is porosity, [dimensionless] is liquid saturation, [kg 
m-3] is liquid density, and [kg m-2 s-1] is the flow rate along the principal axes (, , and ). 
Considering that liquid flow is driven by gravity and pressure gradients (see Section 6.3.2), the 
liquid-flow rate is described by the Buckingham-Darcy law as follows (after Bear 1972 
[DIRS 156269], pp. 487488, Eqs. 9.4.20 and 9.4.21): 
, , and (Eq. 6-2) 
Here, [m2], [m2], and [m2] are the absolute permeabilities along the three coordinate 
axes [m], [m], and [m] (where is positive upward), [dimensionless] is relative 
permeability, [Pas] is liquid viscosity, [m s-2] is gravitational acceleration, and [Pa] is 
the capillary pressure defined as the difference between the liquid and gas pressure. Substituting 
Eq. 6-2 into Eq. 6-1 leads to the governing equation of flow in unsaturated porous media (after 
Bear 1972 [DIRS 156269], p. 496, Eq. 9.4.41): 
(Eq. 6-3) 

In Richards equation, the relative permeability () and capillary pressure () are functions of 
liquid saturation as given, for example, by van Genuchtens model (after van Genuchten 1980 
[DIRS 100610], after Eqs. [8] and [3]): 
(Eq. 6-4) 
(Eq. 6-5) 
In van Genuchtens equations, the effective saturation, , is defined as 
(Eq. 6-6) 
where is residual liquid saturation, and [Pa] and [dimensionless] are 
fitting parameters. The roles of the parameters in the capillary pressure and relative permeability 
functions are illustrated in Figure 6-5. The parameter describes the point of inflection in the 
capillary-pressure function (Eq. 6-5) shown in Figure 6-5a. The factor scales the capillary 
pressure curve and is therefore referred to as the capillary-strength parameter. 
The parameter determines the slopes of the capillary pressure and relative permeability 
functions. It is a measure of the spread of the effective pore size distribution; a large m value 
implies a narrow pore size distribution. The use of continuous relative-permeability and 
capillary-pressure functions, which apply to porous media, is considered appropriate also for 
small fracture segments that are rough-walled and/or partially filled with porous material. 

0.4
iveSatuS
Figure 6-5. (a) Capillary-Pressure Curves and (b) Relative-Permeability Curves for Different 
Illustrative van Genuchten Parameters. 
6.6.1.2 Onset of Seepage 
For a circular opening in a homogeneous medium, the threshold for liquid entry into the cavity is 
full saturation at the apex (Philip et al. 1989 [DIRS 105743]). For the liquid that enters the 
opening to form a drop at the opening wall and detach (see definition of seepage in 
Section 6.1.2), a positive pressure that offsets the drop pressure is required (Or and Ghezzehei 
2000 [DIRS 144773], pp. 390-392). 
For a numerical model in which the continuum is subdivided into discrete gridblocks, the 
condition for seepage is determined by the total water-potential gradient at the connection 
between the fractured medium and the opening as depicted in Figure 6-6. From Eq. 6-2 it 
follows that downward seepage in a discrete numerical mesh, , occurs only when the 
following condition is satisfied: 
0>.+ (Eq. 6-7) 
where is the capillary pressure at the last node adjacent to the opening. Given that the 
capillary pressure in the opening is zero, the numerical threshold capillary pressure is defined as 
, where is the distance between the last node and the opening. The numerical 
threshold capillary pressure therefore depends on the nodal distance between the last node 
and the opening. The opening surface does not need to be fully saturated for seepage to 
commence as given by the analytical solutions of Philip et al. (1989 [DIRS 105743]). As 
indicated in Figure 6-6, given a numerical grid, the seepage-threshold liquid saturation is lower 

for larger and for lower capillary strength (). Consequently, parameter determines 
whether liquid that reaches the surface seeps or is diverted around the opening (effectiveness of 
the capillary barrier). Note that the relative permeability function (Eq. 6-4) does not depend on 
. Hence, the capillary-strength parameter is the main subject of the SCM presented in this 
report (see also discussion in Section 6.6.3.1). The fact that the seepage threshold depends on 
the length of the nodal distance to the opening makes the values of the estimated 
capillary-strength parameter () applicable only to numerical models of comparable 
discretization (see Point 0 of Section 8.4). 
Figure 6-6 shows that a reasonable variation in the parameter has only a limited effect on the 
seepage threshold saturation; a stronger effect is seen for a change in , which tends to vary 
more than . Therefore, fixing the parameter appears reasonable as confirmed by the formal 
sensitivity analysis (see Section 6.6.3.1 below). Moreover, any potential variability of is 
accounted for in the calibrated parameter. The relative sensitivity and potential 
identifiability of seepage-relevant parameters are further discussed in Section 6.6.3.1. 
a<b
b
Sa*Sb*
Effectofm
.zPc*
c
qzSa*
Sb* 
NOTE: A change in a1 has a greater impact on the se
6.6.1.3 corporation of Evaporation Effects 
Part of the liquid that reaches the surface of the underground opening evaporates by virtue of the 

isothermal the evaporative flux (eq[kg m-2 s-1]) applied at the wall of the 
opening is given by: 
6-8) 
where D [m2 sec1] is the vapor diffusion coefficient and C [km3] is the vapor concentration. 
The vapor concentration at the surface of the opening (oC) is related to the capillary pressure by 
Kelvins equation (after Campbell and Norman [An Introduction to Environmental Biophysics 
(1998 [DIRS 150929], Eqs. 3.11 and 3.17)]): 
..TRTR.
wuniversal gas constant, satP[Pa] is the saturated vapor pressure, T [K] is the temperature, and the 
expression )]/(exp[RTMP denotes the relative hum
The vapor concentration of bulk air of the opening () is related to the relative humidity h by 
RTC (Eq. 6-10) 
away from the opening surface, here referred to as the evaporative boundary layer, whose 
thickness (d) is inversely related to the airflow velocity (e.g., [Heat, Mass, and Momentum 
Transfer (Rohsenow and Choi 1961 [DIRS 158324], pp. 3640)]. The vapor concentration 
gradient is presumed linear within the boundary 
8o 
d=Dqe (Eq. 6-11) 
The vapor diffusion coefficient depends on temperature and pressure as given in [TOUGH 
Users Guide (Pruess 1987 [DIRS 100684], pp. 56)]: 
8.1510..
=TDDo
where oD = 2.13  10-5 m2 s-1 is the vapor diffusion coefficient at standard conditions of 
T = 273.15 K and P = 105 Pa. 
The implementation of evaporation effects into iTOUGH2 V5.0 (LBNL 2002 [DIRS 160106]) is 
described in detail in Requirements Document (RD) for iTOUGH2 V5.0-00 (BSC 2002 
[DIRS 161067], Section 1.2). 

6.6.1.4 Estimation of Evaporative Boundary-Layer Thickness 
The thickness of the evaporative boundary layer (d, see Eq. 6-11) can be estimated by 
calibration using evaporation data measured under known vapor concentration and temperature 
conditions. The d values employed in this Model Report were obtained by calibration using 
free-water evaporation data collected inside and outside of Niche 5 (DTN: 
LB0207NICH5LIQ.001 [DIRS 160408]) (see also Section 6.5.4). The vapor concentration at the 
surface of free water is given by Eq. 6-9 with 0=cP Pa. 
The available evaporation data are grouped into three classes based on ai
(1) outside of Niche 5 with active ventilation, (2) outside of Niche 5 without active ventilation 
(the regime usually encountered during nights and weekends), and (3) inside the closed-off 
Niche 5, with little or no air flow. In Figure 6-7 and Figure 6-8, the relative humidity, 
temperature and measured and fitted evaporation rates are plotted for outside and inside of 
Niche 5, respectively (Wang 2003 [DIRS 161456], SN-LBNL-SCI-223-V1, pp. 2225). 
active ventilation. Fluctuations in airflow v
boundary-layer thickness, and (together with fluctuations in relative humidity) determine the 
time-varying evaporation potential. While relative humidity was measured as a function of timcorresponding airflow velocity data are not available. For the purposes of this evalirflow velocity is adequately accoun
ventilation condition, which was categorized into (a) periods of active ventilation (during 
working days), and (b) periods without active ventilation (nights and weekends). Short-term 
fluctuations in airflow velocity are not accounted for; their effect on the average evaporation 
potential prevailing during the length of a liquid-release test is considered small. 
Evaporation-rate data from the beginning of Day 139 to the end of Day 141 were excluded, 
because they appear to have been affected by higher than average ventilation conditions. The 
boundary-layer thicknesses were increased and reduced by 1 mm (green lines in Figure 6-7) to 
show the impact of this parameter on the calculated evaporation rates; this provides an indication 
of the uncertainty in the estimated value. 

Temperature(C)
peratureU
Source: DTN: LB0207NICH5LIQ.001 [DIRS 160408]. 
tive humidity is higher during nights and week
Figure 6-7. Relative Humidity, Temperature, and Measured and Fitted Evaporation Rates from 
Experiments Conducted outside Niche 5 
Niche 5 is to estimate the boundary-layer thickness that represents the closed-oferefore, evaporation data that appear to have been affected 
rapid rise in evaporation rate) 
not used for calibration are gr
ea
e calibration exercise. 
ha(m
n 0.0075 mm/day 
green lines in Figur
e 6-8
pact of this param
aporation rates, and thus gives an indicati
ated value. 

1RelativeHumidity
elativeHumidityperature(C)
0.50.7524Temperature
14514714915123
aporationRate(m/day)
0.00050.0010.00150.002MTWTFSSMTWTFSSMTWTFMeasuredModelUncertaintyBand
E1331351371391411431451471491510 
Source: DT
Figure 6-8. Relative Humidity, Temperature, and Mesur and Fitted Evaporation Rates from 
Experiments Conducted inside Niche 5 
The boundary-lay
evaporation data (Wated 
v
Table 6-6. Boundary-Layer Thickness Estimated Using Eq. 6-1nd Relative Humidity, 
Temperature, and the Evaporation Data from a Free Water Surface 
Location of EBoundary-layer Used For Simulation of 
m) Liquid-Release Tests in 
.0 Niche 5 (Niche 1620) 
Outside Niche 5, Weekdays (ventilation on) 5.0 ECRB (no end curtains) 
Outside Niche 5 Weekends (ventilation off) 7.5 ECRB (with end curtains) 
Output DTNs: LB0302SCMREV02.002, LB0302SCMREV02.001. 
NTE: See Figure 6-7 and Figure 6-8 for rECRB=enhanced characterization of repositor
An evaporative boundary-layer thickness d = 0.02 m was used for the simulation of 
liquid-release tests in Niche 5. Some of the liquid-release tests performed in the open drift of the 
ECRB Cross-Drift were exposed to ventilation; a boundary-layer thickness d = 0.005 m was used 
for these simulations. In more recent tests, additional curtains were installed at the two ends of 
the V-shaped seepage capture curtains to reduce air circulation and thus partly protect the 
seepage section of the drift from ventilation effects; a boundary-layer thickness d = 0.0075 m 
was used for these simulations. 

6.6.1.5 Summary Description of Inverse Modeling Methodology 
[Estimation of Aquifer Parameters Un
based on the classical weighted least-squares method, which consists of minimizing the objective 
function 
rCr1-=zzTS (Eq. 6-13) 
The residual vector r contains the differences between the measured seepage rate, *z, and the 
corresponding model prediction, )(pz, which is a function of the unknown, n-dimensional 
parameter vector p, i.e., mizzriiiK1),)(*(=-=p, where m is the number of calibration 
points. The inverse of the covariance matrix zzC, which holds the expected variances of the 
final residuals on its diagonal, is used as a weighting matrix. The objective function is a measure 
of the misfit between the model output and the measured data. The objective function is 
automatically minimized using the Levenberg-Marquardt algorithm implemented in iTOUGH2 
V5.0 (Finsterle 1999 [DIRS 104367], Section 2.7.4). 
The covariance matrix of the estimated parameters is asymptotically given by: 
()1120--=JCJCzzTpps (Eq. 6-14) 
where J is an nm Jacobian matrix holding the partial derivatives of the predicted seepage 
with respect to the unknown parameters, jiijpzJ..=, and 20s is the estimated error variance, 
which represents the variance of the mean weighted residual; it is an aggregate measure of 
goodness-of-fit: 
szzT=
-rCr120
The impact of parameter uncertainty (expressed through matrix ppC) on model predictions can 
be evaluated by means of first-order-second-moment uncertainty propagation analysis. The 
covariance matrix of the model prediction, zzC, is calculated based on a linearity and normality 
assumption using 
TppzzJJCC= (Eq. 6-16) 
The inverse modeling methodology and its numerical implementation are described in detail in 
the iTOUGH2 software documentation, specifically Finsterle (1999 [DIRS 104367], Section 2). 
6.6.2 Development of Forward Model 
The numerical model constructunderground openings is referred
created for the different test locations and liquid-release events. During the inversion, the 
seepage rates calculated by the forward model are compared to the measured seepage rates at 
discrete points in time. (If the calibration time does not coincide with a data collection time, the 
comparison occurs against a value linearly interpolated between the measurements). The 

ntal site of -10.73 
the forward model. The solution to both the forward and inverse problem is calculated by 
iTOUGH2 V4.0 (LBNL 1
Air-injetion tests performed in borehole intervals at the experimental sites were used to estim
effective permeabilities (see Section 6.5.2, Table 6-4). The spatial structure of the permeabilitydata was analyzed, and the resulting geostatistical parameters were used to generate spatially 
permeability fields (for details, see Appendix C, Appendix D, an
seepage models representing the lower lithophysal zone, multiple realizations of the permeability 
field were simulated by changing the seed number of the random-number generator of the 
software SISIM V1.203 (LBNL 1999 [DIRS 134136]) and V1.204 (LBNL 2000 [DIRS 153100]) 
(see Figure C-5). To reduce the computational burden, only one permeability field is produced 
and examined for each of the niches in the middle nonlithophysal unit, which hosts a 
significantly smaller fraction of the repository. The permeability fields were eventually mapped 
onto the numerical grid (see Section 6.6.2.2). The support scale of the air-permeability data (see 
Section 6.5.2) is appropriately consistent with the gridblock size (see Section 6.6.2.2). 
The GSLIB modules GAMV2 V1.201 (LBNL 1999 [DIRS 134139]) and GAMV3 V1.201 
(LBNL 2000 [DIRS 153099]) were used to analyze spatial correlation of, respectively, 
two-dimensional and three-dimensional, irregularly spaced, log-transformed air-permeability 
data. Sequential indicator sim
hysal zone,
Multipl realizations of the permeability field were generated. 
Niche 5, Lower Lithophysal Zone 
A total of 61 air-injection tests were conducted in three boreholes (boreholes #2, #3, and #5) 
5 (DTN: LB0110

as shown in Figure 6-9. 
151

2 . 5 
g ( k [ m 
2 ] ) 2 ] 
2 
S e m iv a r io g r a m [ l0 
0 . 5 
1 
EmpiricalsemivariogramSphericalsemivariogram
-13
G
16182022-14 
eability data from DTN: LB0110AKN5POST.001 [DIR
Output: Generated permeabilities in output DTN: LB0302SCMREV02.001. 
Figure 6-11. Post-Excavation Permeability Data of Niche 5, Borehole #3 and the Corresponding Gener-
ated Permeability Values from One Representative Realization, which Honors the 
Measured Data 

Niches 2, 3, and 4, Middle Nonlithophysal Zone 
A total of 225 air-injection tests were performed in 10 boreholes above Niche 2 
(DTN: LB0011AIRKTEST.001 [DIRS 153155]), Niche 3 (DTN: LB990601233124.001 
[DIRS 105888]), and Niche 4 (DTN: LB990601233124.001 [DIRS 105888]). The m
, given that a
ntervals in boreho
L, UM, a
nments 
(DTN
SC00
S 152
mivario
thre
e per 
sur
5] and DTN: MO0107GSC0106
0.50.60.70.80.91.0Niche4788
0.3
0.2Niche3650
0.1
00.0 
Source: Permeability data from DTN: LB990601233124.001 [DIRS 105888] and DTN: LB0011AIRKTEST.001 
[DIRS 153155]. 
Output: Semivariograms in output DTN: LB0010SCM
. Empirical Post-Excavation Air-PerSemivariogram Models for Niches 2, 3, and 4 
ricasemivariogram models (Deutsch and Journel 1992 [DIRS 100567], p. 23) with the 
cal parameters shown in Table 6-7 are used to represent the empirical log-permeability 
grams. 

eabiity field is strongly correlated. All three semivariograms shown in Figure 6-12 exhibit 
ort correlation length (i.e., on the order of 1 meter or less) or a nugget effect that is 
e sill value. It can therefore be concluded that the permeability is random without a 
or significant spatial correlation. No n
Table 6-7. Geostatistical Parameters of Spherical Semivar
Niche 
Nugget effect 
[log(k)2] 
Correlation length 
[m] 
Sill value 
[log(k)2] 
Niche 2 0.40 3.87 0.53 
Niche 3 0.01 0.61 0.49 
Niche 4 0.29 
Niche 5 0.02 
Output DTN: LB0302SCMREV02.002. 
ical semivariogram models, along with the cumulative distribution functions shown in 
3, are used as input to the software SISIM V1.203 (LBNL 1999 [DIRS 134136]) for 
andm permeability field is conditioned on the measured post-excavation air-permeability 
Fr Niches 3 and 4, coordinates are added to the permeability field using software 
V1.0 (LBNL 2000 [DIRS 152814]). The resulting permeability fields are mapped 
merical grid of the SCM for each of the niches, as described below in Section 6.6.2.2 
onepermeability field is produced for the niches in the middle nonlithophysal unit. The 
of permeability fields is documented in Scientific Notebooks Finst

.2 esh Generation 
ensional meshes of a section of the ECRB Cross-Drift and Niches 2, 3, 4, and 5 were 
several steps. The following general steps were followed for all the meshes. Detailed 
fic descriptions pertaining to the Cro
A primary 3-D mesh is generated, consisting of regular gridblocks. The Y-axis is 
aligned with the drift or niche a
2. Constants are added to the coordinates of the primary mesh using software MoveMesh 
V1.0 (LBNL 2000 [DIRS 152824]) to shift the origin of the mesh to an appropriate 
datum. 
3. A random, spatially correlated field of log-permeability modifiers is generated using 
software SISIM V1.203 and SISIM V1.204 (LBNL 1999 [DIRS 134136]; LBNL 2000 
[DIRS 153100]) as described in Section 6.6.2.1. Multiple realizat

8-9.0-9.5-10.0
-11.0

5
4z[
34
212
22-4
Output DTN: LB0302SCMREV02.001. 
NOTE: (a) Borehole #4, (b) Borehole #5. In this visualization, the meshes are split into two parts to expose the 
boreholes (indicated by thick black lines) and the injection interval (thick white lines). Multiple realizations 
of the permeability field were generated. Note the rough ceilings as well as the left slot (for X<~2.5 m) of 
Mesh (b). 
gure 6-15. Numerical Grid with one

(a)
..
...
...
...
...
...
....
...
...
	
..

..
.........
........
.
..
......
................
......
.......
.......
....... 
(b)
..
...
...
...
...
...
....
	...
...

..
...
.........
........
...
......
................
......
.......
.......
.......
..
...
...
...
....
...
.	
.
.....
......
...
.........
..
......
.....
.....
......
..
...
...
...
....
....
.....
.	
.
.......
...
.........
..
.......
.......
......
......
(c)....
.	
.
........

	
. 
(d).....
...
.	
.
...
........

	
. 
Output DTN: LB0010SCMREV01.001. 
NOTE: (a) Niche 2, centered 4.42 m from the collar of borehole UM, (b) Niche 2, centered 5.64 m from the collar 
of borehole UM, (c) Niche 3, and (d) Niche 4. Note that the meshes are shown from an angle below 
horizontal to display the ceiling roughness incorporated into Meshes (c) and (d). 
Figure 6-16. Computational Meshes and Permeability Field for 3-D Seepage Calibration Model Used for 
the Analysis of Seepage Data 
6.6.2.3 Boundary Condition
No-flow boundary conditions are specified at the left, right, front, and back sides of the model. 
A free-drainage boundary condition [iTOUGH2 Verification and Validation Report 
(Finsterle 1998 [DIRS 103783], pp. 1415)] is applied at the bottom to prevent 
capillary boundary effect. Elements representing large openings (i.e., borehole intervals, drift 
sections, or niches) are assigned a zero-capillary pressure independent of saturation. The 
evaporation elements are set at a capillary pressure equivalent to the prevailing relative humidity 
h according to Kelvins equation, 
)ln(hMRTpwc.-= (Eq. 6-17) 
as discussed in Section 6.6.1.3. Water is allowed to enter, but prevented from exiting the drift or 
niche. 

A constant flux bou
(DTN: LB990801233129.003 [DIRS 122757]). (Note that slight changes in the calculated 
percolation flux as a result of future revisions of the UZ Model will not impact the estimates and 
conclusions presented in this Model Report). Software ECRB-XYZ V.03 (CRWMS M&O 1999 
[DIRS 147402]) is used to calculate the coordinates of Cross-Drift construction station CS 1726 
(Hinds 2001 [DIRS 155955], p. 124). The gridblock closest to these coordinates and those of 
Niches 2, 3, and 4 are identified from the UZ Model mesh file (DTN: LB990701233129.001 
[DIRS 106785]), and the corresponding percolation flux for the present-day, mean infiltration 
scenario is extracted from the UZ Model output file (DTN: LB990801233129.003 
[DIRS 122757]) (Ahlers 2002 [DIRS 161045], pp. 2931, 45, 55). 
The calculated percolation flu
domains. Percolation fluxes of 
Niche 3, and 2.02 mm/year for Niche 4 are injected at the top of the respective model domains. 
Niche 5 and the tested section of the ECRB Cross-Drift are closer to the high-infiltration zone 
near the crest of Yucca Mountain, explaining the higher percolation fluxes at these locations 
compared to the fluxes for the niches in the ESF. Note that the inflow into the model is 
non-uniform as a result of the heterogeneity in the permeability field. 
As evident
background percolation flux on simulated seepage rates is thus very limited. 
For the simulation of liquid-release tests, release rates and test durations are determined for each 
event shown in Table 6-5 and applied to the gridblocks representing the injection interval 
(Ahlers 2002 [DIRS 161045], pp. 810, 3435, 47; Finsterle 2002 [DIRS 161043], p. 57; 
Wang 2003 [DIRS 161456], SN-LBNL-SCI-228-V1, pp. 1617, 1920). 
6.6.2.4 Initial Conditions 
background percolation, followed by a simulation of reduced relative humidity, which creates a 
dry-out zone around the ventilated opening (Niche 5 and ECRB Cross-Drift only). 
l sauration is not expected to have a significant impact on simulation results. The 
in the initial saturation distribution is a result of formation heterogeneities and the 
f the underground opening and a dry-out zone (if evaporation is simulated). Since the 
te flow field changes if the input parameters are updated during the inversion, a 
te run precedes ea

Inversion 
are iTOUGH2 V4.0 (LBNL 1999 [DIRS 139918]) and V5.0 (LBNL 2002 
liquid-release tests performed in the lower lithophysal zone (Tptpll; Niche 5 and systematic 
seepage testing in the ECRB Cross-Drift); these models include evaporation effects (see Sections 
6.3.3.4, and 6.6.1.3). The iTOUGH2 V4.0 (LBNL 1999 [DIRS 139918]) program was used for 
the analysis of tests conducted in closed-off Niches 3 and 4 located in the middle nonlithophysal 
zone (Tptpmn), where evaporation was significantly reduced and thus not considered 
(see Sections 6.3.3.4 and 6.7). The parameterselected for model calibration are 
described below in Sections 6.6.3.1 and 6.6.3.2, respectively. The results of the inversions are 
discussed in Section 6.6.3.3. 
6.6.3.1 Parameter Selection 
Inverse modeling requires selecting one or more parameters that are considered unknown or 
uncertain and that are to be ad
the other input parameters to the numerical model, including model-domain geometry as well as 
initial and boundary conditions, are fixed during the inversion and are thus by definition part of 
the model structure. The estimated parameters are optimal for and depend on this model 
structure. 
The selection of the parameter to be estimated can be based on (1) the physical understanding of 
the system behavior as it relates to the observed data, (2) a sensitivity analysis, or (3) a synthetic 
inversion using a derivative-based algorithm, which reveals both the sensitivity coefficients and 
parameter c
selected, based on the understanding of the physical system behavior. Subsequently, these 
parameters are subjected to a synthetic inversion to identify the most sensitive parameters and 
their correlation structure. Finally, a selection of the parameters to be estimated is made based 
on their overall sensitivity, relative independence, and the availability and reliability of prior 
knowledge. 
eepage process and impact of parameterized hydrogeologic properties on seepage rates has 
the seepage rates observed during liquid-release 
ost strongly affected by the following parameters: 
1. Parameters of the capillary pressure function are expected to affect seepage rates 
because they determine the effectiveness of the capillary barrier. The two parameters 
of interest are the van Genuchten capillary-strngt
related to thepore size distribution index. Both parameters have been discussed in 
Section 6.6.1.1. 
Effective permeability impacts the flow-diversion capability of the fractured formation 
and thus seepage rates. 

3. Porosity can be interpreted as anter capturing storage effects. 
Storage effects may be significant at early times, for short-duration experiments, and if 
only a small amount of water is injec
ic inversion (using the layout and test conditions of the liquid-release test conducted in 
f borehole SYBT-ECRB-LA#2) was conducted to determine the sensitivities and 
n structure of the four parameters (1) log(a/1), where a/1 [Pa] is the van Genuchten 
strength parameter, (2) n [dimensionless], which is related t
ogeneous permeability field, and (4) log(f), where f [dimensionless] is the effective 
The residual liquid saturation (another parameter of the relative-permeability and 
pressure functions) is expected to be much less important as seepage is initiated near 
ation. A separate sensitivity study was performed to examine the impact of the 
n boundary-layer thickness d (see Section 6.6.1.4) on the estimated parameters. The 
eter log(a/1) was estimated using boundary-layer thicknesses of 0.50 cm, 0.75 cm, 
m (see Table 6-6). 
The sensitivity measure reported below is the sum of the absolute values of the sensitivity 
coefficients, which is defined as the partial derivative of the calculated seepage rate with respect 
to the param
covariance matrix given by Eq. 6-14. Because the inverse problem 
results of this synthetic analysis depend on the a priori parameter valu
ieny close to the best estimates obtained by the actual inversion, i.e., there is no need to 
t th sensitivity analysis after calibration. This analysis should be considered qualitative in 
ts from the synthetic inversion (Wang 2003 [DIRS 161456], SN-LBNL-SCI-228-V1, 
is regarding the evaporation boundary-layer 
SCI-228-V1, pp. 2425) can be summarized 
low: 
The capillary-strength parameter log(a/1) has the largest impact on calculated 
seepage rates. Provided that log(a/1) tends to vary less than log(k), both parameters 
are about equally important for seepage predictions. 
The two parameters, log(a/1) and log(k), are strongly negatively correlated, i.e., an 
increase in seepage rates by a reduction of log(a/1) can almost be completely 
compensated by an appropriate increase in log(k). 
Overall, log(k) is the parameter most strongly correlated to all other parameters, i.e., 
permeability is difficult to estim from seepage-rate data alone. Permeability should 
be derived from independent i
from the air-injection tests (see Section 6.5.2). 

The van Genuchten parameter n (and thus m, see van Genuchten 1980 
[DIRS 100610], Eq. [9]) is relatively insensitive and at the same time strongly 
correlated to all the other parameters. 
Porosity is the least-sensitive parameter. However, since its impact is restricted to 
only a portion of the data (i.e., the onset of seepage and early-time seepage rates), it is 
the least-correlated parameter. If the available
volumes), porosity could be included in the estimation process. 
Changes in the evaporation boundary-layer thickness d within the range considered 
have a limited impact on the estimated parameter log(a/1). Choosing a thin boundary 
layer of 0.75 cm (an estimate based on evaporation and relative-humidity data in an 
open drift section, see Section 6.6.1.4) leads to a relatively low log(a/1) value, 
because reducing the boundary layer thickness increases evaporation and thus leads to 
a reduction in the calculated seepage rate, which is compensated for by reducing the 
capillary strength parameter. 
tivity analysis and correlation structure inferred from the synthetic inversion provides 
ters to be subjected to estimation by automatic 
ing decisions regarding parameter selection have been made: 
As few parameters as possible should be selected to avoid overparameterization. If the 
observed seepage rates can be successfully reproduced by the calibrated model and the 
estimated model parameters can be considered reasonable, the relative parsimony of 
the model and the small number of adjustable parameters provides confidence that the 
physical processes governing seepage are ap
2. The capillary-strength parameter log(a/1) is selected as the primary target parameter 
to be estimated by calibrating the SCM against seepage-rate data from the 
liquid release tests. Capillarity is the main process behind the seepage exclusion 
phenomenon, as confirmed by the large sensitivity of the calculated seepage rate to 
changes in the key parameter of the capillary pressure-saturation relationship. T
seepage-relevant
(such as fracture trace maps and aperture measurements). The parameter is suitable 
for the inclusion of a number of small-scale features and effects (such as surface 
roughness, film flow, drop detachment), and even numerical artifacts, such as 
increased seepage induced by discretization effects. It is important to realize that all 
the effects lumped into the log(a/1) parameter are related or analogous to a capillarity 
effect, justifying the approach. 
3. Permeability as the second important parameter affecting drift seepage is not estimated 
from seepage-rate data, but is taken from and conditioned on the air-permeability data. 
Fixing permeability at values that are determined independently from seepage-rate 

data is a reasonable means to resolve the non-uniqueness issue that arises from the 
strong correlation of permeability to all the other 
determination and inclusion of heterogeneity into the model, which is an important 
feature affecting seepage. The stochastic nature and uncertainty in the heterogeneous 
permeability field is accounted for by performing multiple inversions using different 
realizations of the simulated permeability field. 
4. Porosity is used as an effective parameter to capture storage effects. Porosity is only 
estimated in those liquid-release tests that involved little water, and where evaporation 
effects (which have an impact similar to increased storage) are not explicitly 
accounted for, affecting the early-time seepage-rate data. The analysis of 
liquid-release tests performed in the middle nonlithophysal zone (Niches 3 and 4) 
include the estimation of porosity; the tests conducted in the lower lithophysal zone 
(Niche 5 and systemaseep
and include only the estimation of log(a/1). Note that the estimated poros
emplacement drifts. It is only determined
concurrently estimated log(a/1) param
5. All other parameters are fixed at the values given in Table 4-2. They become part of 
the model structure along with boundary and initial conditions (see Sections 6.6.2.3 
and 6.6.2.4, respectively), and spatial discretization (specifically the length of the 
nodal distance to the drift element, see Sections 6.3.3.3, 6.6.1.2, and Point 0 of Section 
8.4). Since the estimated parameters are likely to be correlated to these fixed 
parameters and would change if the model structure were modified, the estimated 
values are to be considered model-related. 
In summary, the logarithm of the capillary-strength parameter a/1, which enters the van 
Genuchten capillary-pressure function (see Eq. 6-5), is estimated as an effective, 
seepage relev
seepage-rate data from liquid-release tests. For tests conduzone, porosity is also determined to be an effective para
meter, accounting for storage effects. 
As described in the report In Situ Field Testing of Processes (BSC 2004 [DIRS 170004], 
Sections 6.2 and 6.11) and summarized in Section 6.5.3, an automatic data acquisition system 
was set up to monitor the cumulative amount of water seeping into the capture system installed 
within the niches and the ECRB Cross-Drift. Taking the derivative of these cumulative seepage 
data (see Appendix F) yields the seepage rates to be used for calibration. Note that measurement 
noise in the cumulative seepage data induces fluctuations in the calculated seepage rates. If no 
or little seepage occurs, these fluctuations may lead to negative seepage-rate values. These 
nonphysical data points are of no concern, because the numerical model (which always produces 
physically correct, non-negative seepage rates) does not attempt 

stochastic model of the inversion). Instead, the m
component of the seepage-rate data, which is non
Seepage rates are used instead of cumulative seepage data because an error in the prediction of 
the early-time seepage behavior leads to a shift in the cumulative seepage curve. Such a shift 
induces a bias in the estimated parameters, even if only late-time data were used in the inversion. 
In general, early-time seepage data are relatively strongly affected by storage effects. Moreover, 
they reflect the properties of only a few fractures that connect the injection interval with the point 
at the drift surface where seepage is initiated. These fractures may not be representative of the 
drift-scale properties of the fracture network engaged
larger-scale network providing con
matching early-time data potentially leads to an unwanted bias in the estimated parameters. 
Late-time, near-
representative estimation of log(a/1). 
The following paragraphs discuss the data sets used for calibration (see also Table 6-5). 
Borehole SYBT-ECRB-LA#1, Lower Lithophysal Zone 
DTN: LB0110ECRBLIQR.002 [DIRS 156879] 
Only one borehole interval (zone 2) was available for liquid-release testing in borehole 
SYBT-ECRB-LA#1 (BSC 2004 [DIRS 170004], Sections 6.11.2.5 and 6.11.2.6). Starting on 
February 28, 2001, water was released for a period of almost a month at an average rate of 
approximately 17 ml/min. No seepage was induced. In the following month, four tests with 
approximate release rates between 41 ml/min and 45 ml/min were performed, interrupted by 
phases of inactivity that lasted from a few hours to approximately 6 days. These higher-rate tests 
led to seepage. Seepage-rate data from all five tests (Events 6569 of Table 6-5) are used for 
calibration. During the two-month testing period, the re
pp. 1517). 
DTN: LB00090012213U.002 [DIRS 153154], DTN: LB0110SYST0015.001 [DIRS 160409] 
Borehole SYBT-ECRB-LA#2 was tested 
Humidity in the drift was not control
therefore not be used for calibration, but for
During the second test period starting October 23, 2000, long-term liquid-release tests were 
conducted in zones 2 and 3 (Events 6164 of Table 6-5) with approximate release rates ranging 
between 33 ml/min and 41 ml/min. The relative humidity in the testing area (partly protected 
from air circulation by curtains installed at the two ends of the V-shaped seepage capture 
curtains) varied between approximately 30 percent and 90 percent (BSC 2004 [DIRS 170004], 
Figure 6.11.2-8). Data preparation is described in Finsterle (2002 [DIRS 161043], p. 133) and 
Wang (2003 [DIRS 161456], SN-LBNL-SCI-228-V1, pp. 1821). 

Borehole SYBT-ECRB-LA#3, Lower Lithophysal Zone 
DTN: LB0203ECRBLIQR.001 [DIRS 158462] 
Test events 70 and 71 (see Table 6-5) were conducted in zone 1 of borehole SYBT-ECRB-LA#3, 
starting on May 17, 2001. Despite the higher release rate of approximately 36 ml/min during t

40
]
70FlowRate[ml/mi1901952002052102152202252302350102030ReleaseRateSeepageRate
in]
60
Time[daysince01/01/200200:00]
FlowRate[ml/
190195200205210215220225230235010203040ReleaseRateSeepageRate
Source: DTN: LB0209NICH5LIQ.001 [DIRS 160796] 
NOTE: July 15, 2002 is Day 195. 
Figure 6-17. Interference between Concurrent Liquid-Release Tests in Borehole #3 (2122 ft) and 
Borehole #5 (2829 ft) in Niche 5 
Niche 3, Middle Nonlithophysal Zone 
DTN: LB0010NICH3LIQ.001 [DIRS 153144] 
Two intervals were tested in Niche 3. However, only test events in one interval (UM 4.885.18) 
resulted in seepage (see Events 6, 812 in Table 6-5). The tests in the other interval (UL 5.49
5.80) are not useable for calibration, as no seepage was observed (see Events 13), which would 
lead to an ill-posed inverse problem. Nevertheless, these data will be used for validation 
purposes. Of the nine tests performed in interval UM 4.885.18, three are selected for model 
calibration because of their variety in injection and seepage rates. The tests starting on 
March 4, 1999, April 27, 1999, and May 6, 1999 are characteristic of all the tests performed in 
this interval. The March 4, 1999 test (Event 4) was conducted at a low injection rate of 
approximately 0.9 ml/min, resulting in no seepage. The April 27, 1999 test (Event 6) used a 
medium rate of about 2.4 ml/min and resulted in a small amount of seepage. Finally, the 
May 6, 1999 test (Event 8) was performed with a higher rate of approximately 5.4 ml/min, which 
resulted in substantial seepage. The remaining tests (Events 7, 912) in this interval are reserved 
for validation. Data preparation is described by Ahlers (2002 [DIRS 161045], pp. 41, 4748). 

Niche 4, Middle Nonlithophysal Zone 
DTN: LB0010NICH4LIQ.001 [DIRS 153145] 
Three intervals were tested in Niche 4, all tests leading to seepage. Each test performed in a 
given interval was conducted with a different release rate (except in interval UM 6.106.40, 
where three low-rate tests were performed; only the test on March 14, 2000 was a long-term 
test). In each interval, the low- and medium-rate tests that resulted in seepage were selected for 
model calibration (Events 41, 43, 45, 48, 50, and 51 in Table 6-5). The highest-rate and 
lowest-rate seepage tests are reserved for validation (Events 40, 42, 44, 47, 49) to determine 
whether seepage model predictions can be successfully extrapolated to conditions beyond the 
calibration range. Data preparation is described by Ahlers (2002 [DIRS 161045], 
pp. 26-27, 34-36). 
6.6.3.3 Calibration Results 
i
in the ECRB Cross-Drift and nichesin Figure 6-21, Figure 6-26, Figure 6-28, and Figure 6-3Figure 6-18a shows the calculated flux distribution at the end of the testing period. The flow 
field above the injection point represents the natural background percolation flux; with flow 
that flow channels are established within a short distance below the top boundary. The injection 
e 2 of borehole SYBT-EC
Local flux below the water release point is very high. Water is partly diverted around the 
opening. The low relative humidity (less than 50 percent) leads to substantial evaporation, 
extracting most of the injected water from the formation before it reaches the spring line of the 
drift. Flow diversion around the drift and the removal of water that seeped into the capture 
system leads to reduced fluxes below the drift, an effect referred to as the shadow zone. 

The calculated saturation distribution at the end of seepage testing (Event 69 of Table 6-5) shows 
that the fracture continuum is essentially dry (less than 10 percent) under natural percolation 
conditions (Figure 6-18). Injecting at rates on the order of 40 ml/min increases saturation 
without completely saturating the pore space, i.e., the flow regime remains unsaturated. As a 
result of the capillary-barrier effect, saturations are highest in the immediate vicinity of the drift, 
which induces a capillary-pressure gradient promoting flow diversion. Seepage and evaporation 
removes water from the formation as it flows around the drift, explaining why no significant 
saturation increase can be observed at the spring line. In contrast, during the liquid-release tests 
performed in the closed-off niches, where evaporation is significantly reduced, water reaches the 
spring line and drains to depth as 
Section 6.7). Additional discussions of the flow and seepage behavior will be given below for 
tests in borehole SYBT-ECRB-LA#2. 
468100000100001000100101Flux[mm/yr]
t=58days4680.50.40.30.20.10.0Saturationt=58days
0123Y[m]
-202Z[
01234X[m] 
0123Y[m]
-202Z[
01234X[m] 
(a) (b) 
ration Distribution
Figure 6-18. (a) Flux and (b) SatuBorehole SYBT-ECRB
-LA#1 
ch of the calculated seepage rates (red line) to the observed data (blue symbols) for
lines) and the relative-humidity data (green line). The model correctly replicates the initial, 

e-varying b
a steadystate simulation (to achieve initial conditions), followed by a transien 
time required for these
sequence of liquid-release tests. The central processing unit
is intensive, limiting the number of realizations that can be analyzed. The number of realizaanalyzed is different for each location (see fourth column of Table 6-8 below), because th
requirements.) 
The a/1 estimate has a mean, standard deviation, and 
and 14 Pa, respectively (see Table 6-8 below). (Note 
instead of log(a/1). Using the backtransformed values is justified by the small standard 
deviation of the estimates.) The estimation uncertainty of an individual inversion (given by Eq. 
6-14) is on the order of a few Pascals. This estimation uncertainty accounts for the residual 
misfit of the calibrated model to the data and the sensitivity of the calculated seepage rates with 
respect to a/1. This uncertainty measure is not propagated through the suite of seepage models, 
because it is significantly less than the uncertainty stemming from small-scale heterogeneities 
(which is examined by performing multiple inversions with multiple realizations of the 
permeability field, amounting to 57 Pa in this case) and spatial variability (which is examined by 
inverting data from tests conducted at differe
see Section 6.6.4). 

in]
dity[%]
304050405060SYBT-ECRB-LA#1,Zone2ReleaseRelativeHumidity
Rate[ml/
Hum2030
Rel1020
0-10SeepageDataCalculatedSeepage 
Source: DTN: LB0110ECRBLIQR.002 [DIRS 156879]. 
Output DTN: LB0302SCMREV02.001. 
NOTE: Blue symbols represent measured data; th

s), onsi
crown of the drift; however, mobetween the crown and the spring line (see Figure 6-21d). 
sons between measured and calculated
shown in Figure 6-22 and Figure 6-23, respectively. Fluctuations in both data sets can be 
the drastic changes in relative humidity, which drives evaporation
r

t=20days
Saturation
8
60.40.30.20.40.3
40.10.00.1
0.1
4
2Z[m]
Z[m]
0
012-20123X[
1m-2011m3/yr
(c)
Y[m]
]
(d)
Output DTN: LB0302SCMREV02.001. 
NOTE: (a) initial distribution, (b) after 10 days, (c) 20 days, and (d) 30 days. Triangles indicate seep locations and 
seepage amount. 
Figure 6-21. Saturation Distribution Simulated With Model Calibrated against Seepage-Rate Data from 
Liquid-Release Tests Conducted in Zone 2 of Borehole SYBT-ECRB-LA#2 

404590100SYBT-ECRB-LA#2,Zone2
n]
Release
l/mi25
Rate[mveHum15204050RelativeHumidity
Re51030SeepageDataCalculatedSeepage

The capillary-strength parameter a/1 was determined based on
(557  56 Pa for zone 2 and 535  58 Pa for zone 3; see Table 6-8 below) are consistent with 
those obtained in borehole SYBT-ECRB-LA#1. 
Borehole SYBT-ECRB-LA#3, Lower Lithophysal Zone 
The calibration of liquid-release tests from zone 1 of borehole SYBT-ECRB-LA#3 revealed 
some systematic inconsistencies between the data and the model. As shown in Figure 6-24, the 
model produces considerable seepage during the simulation of the first test event, while no 
seepage was observed in the field despite the high release rate. Furthermore, the model predicts 
a relatively uniform seepage rate for the second test event, whereas the d
452  55 Pa (based on 23 inversions; see Tabl
SYBT-ECRB-LA#3,Zone1Release
03E2SCMRE
ed lik solid 
Relative-humidity data are sh
Figure 6-24. Calibration of Seepage-Rate Data from Liquid-Release Tests Conducted in Zone 1 of 
Borehole SYBT-ECRB-LA#3 

Niche 5, Borehole #4, Interval 1011 ft, Lower Lithophysal Zone 
During Test Event 89, liquid was released at a fairly constant rate. Seepage was observed after 
two days and continued to increase for four days, reaching almost steady state on the sixth day. 
The test was simulated with a constant release rate of 9.9 ml/min and a constant relative 
humidity of 85 percent. The model produced seepage 12 hours after liquid release started, and 
reached a constant seepage rate after 36 hours (see Figure 6-25). The modeled saturation and 
flux at the end of the test (see Figure 6-26) show that there is significant diversion of injected 
liquid around the niche. Thirty inversions with 30 different realizations of the underlying 
heterogeneous permeability field were performed, rltinglibrated capillary-strength 
parameter a1 of 223671 Pa. The stan
10
Release
l/min]
Ca

456456z[m]
0.90.80.70.60.50.40.3Saturation
6
44z[
100101
3
(b)
x[m]
16172-3-2-101617y[m]
123 
Output DTN: LB0302SCMREV02.001. 
Figure 6-26. Simulated (a) Saturation And (b) Flux Distribution at the End of Liquid-Release Testing 
(After 13 Days) in Interval 1011 ft of Borehole #4 in Niche 5 
Niche 5, Borehole #5, Interval 2829 ft, Lower Lithophysal Zone 
In Test Event 86, water was released at a rate of approx
fluctuations in the release data that occurred during the first four days were reproduced in the 
seepage model. Seepage started on the second day and continued at an increasing rate, until it 
reached a constant rate on the tenth day (see Figure 6-27). The slight interference from Event 
83, which occurred during the third day of Event 86, affected only the transient stage of the 
seepage data and was considered insignificant in the calibration process (see also discussion of 
Figure 6-17). The simulated relative humidity was kept constant at 85 percent, consistent with 
the averaged observed relative humidity during the test. The saturation and flux at the end of the 
test (see Figure 6-28) show that there is significant flow diversion of injected liquid around the 
niche towards the left slot. (See Section 6.7 for a detailed discussion on the significance of 
seepage into the slot in confirming the capillary-barrier concept.) Twenty-four inversions with 

different realizations of the underlying heterogeneous permeability field were performed, 
resulting in a calibrated capillary-strength parameter a1 of 339740Pa. The standard error of 
the mean is 69 Pa (see Table 6-8 below). 
30

7
AverageFlux
56m]
5100000100001000100

systems in Niche 4 leads to a strategy of es
(intervals UL 7.627.93, UM 6.1
events performed with different injection rates were inverted simultaneously (note the differe Y-axes). The model overpredicts the seepage rates from
borehole UM on 12-10-99. In this case, the miniminimm. The a/1 estimates (based on on
permeability field) for the three boreholes UL, UM, and UR are 646 Pa, 603 Pa, and 427 Pa, 
respectively. These three estimates will be combined with that from Niche 3 to yield an average 
value for the middle nonlithophysal zone (see Section 6.6.4). Additional information on 
calibrations of the tests conducted in the middle nonlithophysal zone can be found the scientific 
notebook by Ahlers (2002 [DIRS 161045], pp. 3839, 49). 
Note that effective porosity values were also estimated from the seepage-rate data measured in 
Niches 3 and 4. They represent storage effects during the transient liquid-release tests. The 
estimated mean of approximately 0.7 percent (DTN: LB0010SCMREV01.001) is a reasonable 
value for an effective storage parameter. Because porosity is insignificant for the prediction of 
steady-state seepage rates and is thus not used in downstre

(a)
....
....
...	

..
...
..
...
...
....
...	
...

..	
..
...

..
...
....
....
..........
....
....
..

...
.... 

..
(b)
....
....
...	
..
...
...
....
...	
...

..	
..
...

..
...
....
....
....
..

...
.... 
Outut DT
NOTE: The step in the displayed grid c 
6-30. Simulated Late-Time Saturation Distribution for Liquid-Release Tests in Niche 4, Intervals 
(a) UL 6.727.93 (November 3, 1999), (b) UM 6.106.40 (November 16, 1999), and (c) UR
5.185.48 (December 7, 1999) 

2.0Niche4788UL11-30-99Simulated1.0Niche4788UL6-26-00
0.6
0.5
2
18240.0
303
2880
360
504
El
edTestTim
]
edTest
hours] 
3.0Nich
788UM
99
6N
4788U
-8-00
2.5
2
ated
8
ured
4
dTestTime[hours]ElapsedTest
788U
N
788
4-00
sured
0
96144ElapsedTestTim0.0
c
ure: DTN: LB0010NICH4LIQ.001 [DIRS 153145]. 
tput DTN: LB0010SCMREV01.001. 
TE: Data from a given in
ure 6-31. Comparison betwee
Tests Conducted in Three Different Borehole Int
6.6.4Summary and Compilation of Results 
Multiple three-dimensional, heterogeneous models representing niche locations and sections of 
the ECRB Cross-Drift were developed. The permeability fields were constructed and 
conditioned on local air-permeability data (see Section 6.6.2.1). Seepage-rate data from multiple 
test events, using different liquid-release rates, were inverted simultaneously. Inversions for the 
main repository host unit (the lower lithophysal zone) were repeated for multiple realizations of 
the underlying stochastic permeability field to capture the uncertainty induced by local 
heterogeneity. A total of 22 test events were used for model calibration (13 from tests conducted 
in the lower lithophysal zone and 9 in the middle nonlithophysal zone). Seepage-relevant 
capillary-strength values (parameter a/1 of the van Genuchten capillary pressure-saturation 
relationshipsee Eq. 6-5) were estimated for each location. 
A summary is provided in Table 6-8 and visualized in F

V1, p. 31)). Since multiple inversions with different realizations of the underlying 
heterogeneous permeability field were performed for test locations in the lower lithophysal zone, 
the capillary-strength parameter a/1 is calculated as the average for all inverse modeling results 
at that location, and a standard deviation s representing the related uncertainty 
standard error of the mean is calculated as , where i is the number of inversionsperformed. To reduce the computational burden, the estimates for the minor repository host unit 
(the middle nonlithophysal zone) are based on a single inversion, i.e., no estimation uncertainty 
as a result of uncertainty in small-scale heterogeneity can be given. The estimation uncertainty 
stemming from the misfit between the calibrated model and the data, and the sensitivity of the 
calculated seepage rates with respect to a/1 (see Eq. 6-14), is on the order of a few pascals, i.e., 
significantly less than the uncertainty from small-scale heterogeneity (which is greater than 
50 Pa) or spatial variability (which is on the order of 100 Pa); this contribution to the estimation 
uncertainty is therefore ignored. 
An average capillary-strength pardi
at the fferent locations within a given unit. The related standard deviation is consideredof spatial variability in a/1. In summary, the a/1 parameter of the lower lithophysalbout 580 Pa with a variability of app
has slightly a higher a/1 value of about 600 Pa and a variability of approximately 130 Pa. 
These values can be used to derive a probability distribution for the capillary-strength parameter 
model validation (see Section 7.2). Note that such a dist
uncertainty in predicted seepage rates, on account of uncertainty in stochastic small-scale 
heterogeneity. 
bility0.0030.0040.005y0.0040.005
(a)
1/a[Pa]
Prob200400600800100000.0010.002 
(b)
1/a[Pa]
Prob200400600800100000.0010.002 
Output DTN: LB0302SCMREV02.002. 
locations. For the lower lithophysal zone, the squares represent means from multiple inversions, which are 
plotted along with the standard error of the mean. (The vertical position of the symbols is of no significance.) 
Figure 6-32. Histograms and Related Normal Distributions of Van Genuchten Capillary-Strength 
Parameter 1/a for (a) the Middle Nonlithophysal Zone, and (b) the Lower Lithophysal 
Zone 

Table 6-8. Summary Statistics of Estimated Capillary-Strength Parameter for Lower Lithophysal Zone 
and Middle Nonlithophysal Zone 
Lower Lithophysal Zone (Tptpll) 
a [Pa] 
Event aLocation Interval Inversions bMeaev. cError dMin. Max. 
6569 SYBT-ECRB-LA#1 zone 2 17 534.3 56.8 13.8 447.7 674.1 
61, 62 ECRB-LA#2 zone 2 21 557.1 56.4 12.3 45763, 64 SYBT-ECRB-LA#2 zone 3 19 534.8 57.8 13.3 443.1 645.7 
SYBT-ECRB-LA#3 zone 1 23 452.0 54.7 11.4 382.8 616.6 
89 Niche 5 BH #4 30 671.2 223.2 40.8 356.0 
86 Niche 5 BH #5 24 740.5 339.0 69.2 231.1 1840.7 
Mean e =
Std. Dev. f=
581.6 
105.0 
4, 6, 8 Niche 3 UM 1 
41, 43 Niche 4 UL 
45, Niche 4 UM 1 603     
Niche 4 UR 1 427 
Mean e =
Std. Dev. f =
604.3 
131.5 
a See Table 6-5 on Page 6-29. Data from all indicated test events were jointly inverted. 
b Each inversion is based on a different realization of the heterogeneous permeabilityc
ates for the middle nonlithophysal zone). 
dard error of mean. 
age for given hydrogeologic unit. 
le; UL=upper left; UR=upper right
CT OF 
TIVE HUMIDITY ON SE
PAGE 
in Section 6
g the seepage experiments co
a from Niches 3 and 4. If evaporation were
nalyzi these lower seepage rates with a model that assumes 100 
p

The SCM developed for the analysis of seepage data from liquid-release tests conducted in 
interval 1011 ft of borehole #4 in Niche 5 includes evaporation effects. This model is therefore 
suitable for examin
Figure 6-33 shows the calculated seepage and evaporation rates as a function of the relative-
humidity boundary in the niche. Evaporation from the entire niche wall surface is 
relatively high and comparable to the amount of seepage. Evaporation decreases almost linearly 
as relative humidity increases, as expected. Nevertheless, the impact of evaporation on the 
calculated seepage rate is small for these high relative humidity values. Neglecting evaporation 
effects for the analysis of seepage-rate data from Niches 3 and 4, where relative humidity was 
close to 100 percent, is thus acceptable. Note that evaporation in the open, ventilated ECRB 
Cross-Drift is considered substantial and is thus incorporated into th
FlowPercentage101520SeepageEvaporation
80850
RelativeHumidity[%] 
LB0302SCMREV02.001. 
NOTE: Flow percentages are plotted 13 days after the start of liquid release. See Figure 6-34 for the seepage 
andevaporation rates as a function of time with relative humidity of 85 percent. Note that flow percentage 
ated re
F

ays smaller than the p
unsaturated conditions prevail. 
The seepage-exclusion phenomenon has been extensively described in the literature (see Philip et 
al. 1989 [DIRS 105743] and references therein). The related water diversion capability is 
exploited in practical applications for the protection of landfills and hazardous waste sites (the 
corresponding engineering designs are sometimes referred to as Richards Barriers). These 
standard engineering applications consider porous materials rather than fractured rocks. 
However, since the key factors affec
Section 6.3.3), which are properties of fractured rock, the same barrier effect can also be 
expected to apply at Yucca Mountain. 
This hypothesis has been extensively tested through the in situ seepage experiments described in 
the reports, In Situ Field Testing Processes (BSC 2004 [DIRS 170004], Sections 6.2 and 6.11), 
Trautz and Wang (2002 [DIRS 160335]), and Section 6.5 above. These experiments show that 
the seepage rate is less than the injection rate. Because of storage effects and evaporation, the 
reduced seepage rate by itself does not conclusively prove that water is diverted around the 
opening, which would assess the barrier capability of the natural system at Yucca Mountain. 
Evidence that seepage exclusion and flow diversion occurs is provided by the numerical model, 
which accounts for storage and evaporation effects, and which is capable of reproducing the 
observed seepage data. The following 
MMMMM+++= (Eq. 
diversionnevaporatiostorageseepagereleaseThe cumulative amounts of water that was released (releaseM) and that seeped into the capture 
system (seepageM) are known from measurements. Cumulative evaporation (nevaporatioM) is 
calculated by the model. The rest of the water is either stored in the formation above the opening 
(M) or was diverted around the drift on account of the capillary barrier effect or bypassed 
storagethe opening through known or unknown geologic features and percolated to depth (diversionM). 
Both terms can be inferred from the calibrated model. A similar balance can be written for 
steady-state rates (where the storage term drops out) instead of cumulative amounts of water. 
The rate balance for the simulation of a typical sequence of liquid-release tests in Niche 5 
(Events 8688 of Table 6-5) is shown in Figure 6-34. 

InjectionData
FlowRate[ml/min]
-6-4-20246810SeepageDataCalc.InjectionCalc.SeepageCalc.DeepFlowCalc.EvaporationCalc.StorageExampleTime
Time[d01020304050-8
Source: DTN: LB0211NICH5LIQ.001 [DIRS 160792]. 
Output DTN: LB0302SCMREV02.001. 
NOTE: A detailed rate balance on Day 10 (denoted by Example Time) is given in Table 6-9. Deep Flow refers 
to water diverted around the opening, escaping to depth. 
Figure 6-34. Rates of Water Released, Evaporated, Diverted around the Niche, Stored i
Measured and calculated flow rates for typical liquid-release test in borehole #4 of Niche 5, on 
Day 1
Table 6-9. Mass Balance for Typical Seepage Test in Borehole #4 of Niche 5, on 
September 27, 2002 at 16:20 (Day 10). 
Measured Flow Rate (ml/min) Calculated Flow Rate (ml/min) 
Release 9.915 (100.00%) 9.967 (100.00%) 
Seepage 1.130 (11.40%) 1.132 (11.36%) 
Diversion  6.707 (67.30%) 
Evaporation  1.798 (18.04%) 
Storage  0.329 (3.30%) 
Source: DTN: LB02
Output DTN: LB0302SCMREV02.001. 
NOTE: The model release includes an ambient infiltration rate of 2.20 mm/year 
(0.067 ml/min) 

The mass balance indicates that a substantial amount of water is diverted around the opening, 
confirming the seepage exclusion phenomena and barrier potential of the unsaturated zone at 
Yucca Mountain. 
The mass-balance calculation presented above includes measured values and quantities inferred 
from the calibrated model. The key quantity of interest demonstrating the barrier capability of 
the natural system (diversionM) was obtained from the model. To obtain direct evidence that flow 
diversion occurs during liquid-release tests, a horizontal slot (also referred to as batwing; see 
schematic in Figu
above the niche and eventually collected in the slot at the spring line (Figure 6-35) must have 
been diverted around the opening, corroborating the barrier capability of the seepage-exclusion 
effect known to exist in unsaturated formations. 
In summary, partial or complete diversion of water around underground openings on account of 
the capillary barrier effect reduces seepage or even prevents water from dripping into a waste 
emplacement drift. This effective barrier at the interface between the natural and engineered 
system has the potential to significantly reduce corrosion, waste dissolution, mobilization of 
radionuclides and their transport from the invert of the waste emplacement drift to the accessible 
environment. In addition, the extent of the shadow zon
in delaying radionuclide transport is strongly related to the seepage-exclusion phenomenon. (For 
a discussion of the shadow zone, see Philip et al. (1989 [DIRS 105743])). The barrier capability 
of flow diversion around underground openings in unsaturated fractured formations has been 
established theoretically, through modeling and extensive field testing at Yucca Mountain. The 
Seepage Model for Performance Assessment (SMPA) examines the effectiveness of the seepage 
barrier for a multitude of conditions. Uncertainty and spatial variability in the seepage-relevant 
model parameters have been quantified and will be propagated through the downstream seepage 
models. 

3inleftbatwing
001inleftbatwing 
LB0209NICH5LIQ.
t DTN: LB0302SCMREV02.001. 
iquid was released in interval 28
age after Day 15 is a result of test interferenc 
6-3. Water Collected in the Slot on the Side of Niche 5 Confirming Flow Diversion around the 
Opening 
Text Box: DISCLAIMER
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third partys use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

7. VALIDATION 
LEVEL OF RELATIVE MODEL IMPORTANCE 
rding to AP-SIII.10Q, Models, Section 3.13, model validation is a process used to establish 
dence that a mathematical model and its underlying conceptual model adequately represent 
sufficient accuracy the system, process, or phenomenon in question. The model validation 
ss also includes developing a level of confidence as stated in AP.SIII.10Q, Models, Section 
onfidence required in accordance with th
5.3: Validate the model to the level of cParagraph 5.3.2c) of AP.SIII.10Q. 
While rigorous model testing is fundamentally not possible (Oreskes et al. 1994 
S 152512]), confidence can be gained that the Seepage Calibration Model (SCM) is 
ate for its intended purpose to the level of confidenc
importance to the potential performance of the repository system. The scientific basis for the 
model is considered adequate if (1) the model implements the current scientific understanding of 
unsaturated flow and capillary barrier behavior (see Sections 6.3 and 6.6.1), (2) the model is 
capable of reproducing available, seepage-relevant data during model calibration (see Section 
6.6.3.3), (3) the model is capable of predicting available seepage-relevant data within a 
predefined level of accuracy considered acceptable for the intended use of the model (see 
Sections 7.2 and 7.4), and (4) the model is not inconsistent with alternative, viable 
conceptualizations (see Section 6.4). 
The intended use of the SCM is twofold (BSC 2004 [DIRS 169654], Sect
The primary purpose of the SCM is to provide the conceptual basis and a general modeling 
framework for the development of the Seepage Model for Performance Assessment (S
(BSC 2004 [DIRS 169654], Section 2.1.1.8). These seepage rates will be further abstracted and 
used in a probabilistic performance assessment (PA) calculation. 
A secondary purpose of the SCM is to provide seepage-relevant 1/a estimates. This information 
will be used in the seepage abstraction process for the development of probabilistic parameter 
distributions. However, additional data and assumptions enter the development of these 
distributions: the PA calculations do not rely solely on the 1/a values provided by the SCM
The relative importance of the SCM to the potential performance of the repository system has 
been evaluated based on sensitivity analyses as documented in Risk Information to Support 
Prioritization of Performance Assessment Models (BSC 2003 [DIRS 168796]). These sensitivity 
analyses indicate that seepage rate does not significantly change the mean annual dose estimate 
for the nominal scenario (BSC 2003 [DIRS 168796], Section 3.3.2). For the igneous activity 
groundwater release scenario, which is dominated by the solubility-limited radionuclides 
and 240Pu, the annual dose is affected by the amount of flow contacting the waste. Nevertheless, 
it was concluded that the details of the seepage model do not play a significant role in the 
estimate of mean annual dose. Consequently, the lowest level of validation (Level I) for the 
TSPA seepage model and the process models supporting it (such as the SCM) was considered 

7.2 VALIDATION ACTIVITIES AND CONFIDENCE EVALUATION CRITERIA 
The model validation activities and acceptance criteria presented in the remainder of this section 
follow those of the TWP for this Model Report (BSC 2004 [DIRS 169654], Section 2.2.1) and in 
the related planning document Technical Work Plan for: Performance Assessment Unsaturated 
Zone (BSC 2004 [DIRS 167969], Section I-4-1); these model validation activities exceed the 
Level I validation activities outlined in Risk Information to Support Prioritization of 
Performance Assessment Models (BSC 2003 [DIRS 168796], Section 4.3) and Scientific 
Processes Guidelines Manual (BSC 2002 [DIRS 160313], p. B-1). The guidelines of the 
Scientific Processes Guidelines Manual are consistent with the Level I validation activities 
described in AP-2.27Q, Planning for Science Activities, Attachment 3, and AP-SIII.10Q, Models, 
Paragraph 5.3.2c). Confidence in the adequacy of the SCM for its intended purpose has been 
gained during the model development process as well as through pre-test predictions of seepage 
experiments and rigorous post-development model testing as outlined in the TWP for this Model 
Report (BSC 2004 [DIRS 169654], Section 2.2.1). 
7.2.1 Confidence Building during Model Development to Establish the Scientific Bases 
and Accuracy for Intended Use 
For Level I validation, the Technical Work Plan for: Unsaturated Zone Flow Analysis and model 
Report Integration (BSC 2004 [DIRS 169654], Section 2.2.1) refers to Technical Work Plan for: 
Performance Assessment Unsaturated Zone (BSC 2004 [DIRS 167969], Section I-4-1) where 
model validation activities for the SCM were planned. Consistent with this planning, the 
development of the SCM has been conducted according
1. Selection of input parameters and/or input data, and a discussion of how the selection pro
builds confidence in the model [AP-SIII.10Q 5.3.2(b) (1) and AP-2.27Q Attachment 3 Level I 
(a)] 
The types and quality of the data selected as input builds confidence in the model. The inputs to 
the SCM have all been obtained from the TDMS, a controlled source. Section 4.1 identifies the 
data and design parameters used. Discussions of parameter ranges and uncertainties are covered 
in Sections 6.3 through 6.6. Model assumptions have been described in Section 5. During 
model development, input data and parameters were carefully selected (1) to best represent the 
conditions encountered at the liquid-release test sites, (2) to be compatible with the general 
modeling approach and parameters from upstream models, and (3) to provide a useful conceptual 
basis and mathematical model for the downstream modeling acti
can be considered satisfied. 
2. Description of calibration activities, and/or initial boundary condition runs, and/or run 
convergences, simulation conditions set up to span the range of intended use and avoid 
inconsistent outputs, and a discussion of how the activity or activities build confidence in the 
model. Inclu
5.3.2(b)(2) and AP-2.27Q Attachment 3 Level I (e)] 

Calibration activities for the SCM are described in Sections 6.3 and 6.6.3.1. The model is 
calibrated using a minimum number of adjustable parame
model appropriately represents the relevant physical processes a
site. Sensitivity analyses have been performed in support of the parameter selection process and 
to test the appropriateness of certain model simplifications. Detailed discussion of the model 
domain and boundary conditions for the SCM can be found in Sections 6.6.2.2 and 6.6.2.3. All 
simulations converged; therefore, discussion about non-convergence runs is not relevant for this 
Model Report. 
3. Discussion of the impacts of uncertainties to the model results including how the model 
results represent the range of possible outcomes consistent with important uncertainties 
[(AP-SIII.10Q 5.3.2(b)(3) and AP-2.27Q Attachment 3 Level 1 (d) and (f)] 
Uncertainties in the characteristics of the natural system are explicitly considered in the SCM 
development. Uncertainty associated with initial parameterization and in the inverse modeling 
and data-analysis approach is described in Sections 6.3.3 and 6.3.4, respectively. The parameter 
ranges are technically defensible and reasonably account for system uncertainties and 
variabilities. The results are discussed in Section 6.6.4. 
6.6.1), which ensures mass conservation 
physical principles such as conservation of mass (BSC 2003 [DIRS 168796], p. 4-2) are 
appropriately represented. Model assumptions and simplifications have been described in 
Sections 5 and 6.6. Boundary and initial conditions are discussed in Sections 6.6.2.3 and 6.6.2.4, 
respectively. 
The main activity presented in this Model Report is the calibration of the SCM against seepage-
rate data from liquid-release tests conducted at Yucca Mountain. Development of the SCM is 
closely coupled with seepage testing in niches and along boreholes of the systematic hydrologic 
characterization program. The iteration between testing and modeling leads to improvements in 
test design and confidence in the modeling approach. As outlined in Section 6.3.4, the approach 
ensures that the estimated drift-scale parameters
using the conceptually compatible Seepage Model for Performance Assessment, thus avoiding 
inconsistencies in the outputs. All simulation runs converged to a solution, which are described 
in Section 6.6.3.3. A summary discussion of the adequacy of the approach can be found in 
Section 8.3 below. 
parameter distribution (see Section 6.6.4), which is provided as input to the seepage abstraction 
process. A summary discussion on uncertainties and their impact is given in Section 8.2. 
Natural analogues as those reported in Natural Analogue Synthesis Report (BSC 2004 
[DIRS 169218], Section 8) provide evidence that the concept of seepage exclusion describes a 
process that actually occurs in caves, lava 

For confidence building after model develo[DIRS 167969], Section I-4-1-1) imposes the
more of the following methods: 
Section 7.2.2.1) 
P-SIII.10Q, Section 5.3.2(c), Meth
Data (see Section 7.2.2.2) 
3. AP-SIII.10Q, Section 5.3.2(d), Technical Review through Publication in a Refereed 
Professional Journal (see Section 7.2.2.3) 
7.2.2.1 Corroboration with Data from Field Experiments 
Blind predictions of seepage rates were performed with the calibrated SCM, simulating tests that 
were not used for model calibration, and that involve a different portion of the fracture system 
and a different section of the drift or niche. The DTNs containing the data used for model 
validation are identical to those listed in Table 4-1, Table 4-2, and Table 4-3. In addition, Table 
6-5 identifies the test event used for validation purposes. Moreover, each DTN is listed below 
along with the discussion of individual validation test cases. 
If seepage rates were successfully predicted (accordin 
percolation flux;

The combination of Criteria (1) and (2) imp
regarded as one-sided, i.e., 95 pe
where 90 percent of the Monte Carlo simulations fall within that band, and 95 percent are 
seepage (the physical minimum) and the upper bound. 
Note that these acceptance criteria are stricter than the general acceptance criterion of BSC 
(2003 [DIRS 168796], p. 4-2), which only requires a demonstration that basic physical 
principles such as conservation of mass are appropriately represented. 
If any of the acceptance criteria are met, the SCM can be considered validated for its primary and 
secondary purpose, according to the definition in Section 3.16 of AP-SIII.10Q, Models. 
Several methods can be employed to assess the uncertainty of model predictions as a result of 
input parameter uncertainty. When computationally feasible, Monte Carlo simulations (Finsterle 
1999 [DIRS 104367], pp. 7679) are the method of choice because they automatically account 
for nonlinearities in the model. A 
can be approximated by a normal distribution
Both methods require that probability distributions be determined for each input parameter 
ariable or uncertain. Since the seepage experiments used for validation were 
locations different from those used 
ones estimated during calibration can be consonly parameter reduces the prediction uncert
the validation acceptance criteria. The approach followed here is therefore conservative. 
The van Genuchten parameter a/1 is both spatially variable and uncertain. A measure of spatial 
variability was obtained by analyzing data from liquid-release tests performed at multiple 
locations. The resulting distribution is discussed in Section 6.6.4 and visualized in Figure 6-32. 

. For the predicapproach is conservative because it reduces the
difficult for the SCM to meet the
In general,00 Monte Carlo simulations were performed for each validation test case. This 
is considered sufficient to obtain the output statistics of interest. Specifically, the 
te is accurately identified from

2002 [DIRS 161318]; Doughty et al. 2002 [DIRS 161320]; Finsterle a
7.3 RESULTS OF MODEL VALIDATION ACTIVITIES 
In this section, the range of predicted seepage rates is compared to seepage-rate data collected 
from liquid-release tests that were not used for model calibration. The range was evaluated by 
means of Monte Carlo simulations (all tests in lower lithophysal zone and short-term tests in 
Niche 2) and using FOSM error propagation analyses (tests in Niches 3 and 4, middle 
nonlithophysal zone). The indicated range contains 90 percent of the Monte Carlo simulation 
results, orif FOSM error propagation analysis is usedrepresents the 90 percent confidence 
band. Considering that realizations below the lower bound of the confidence limit are acceptable 
(see Criterion (2) of Section 7.2.2.1), the range between zero seepage and the upper bound 
corresponds to a one-sided uncertainty band on the 95 percent s
seepage is predicted with the mean paramet
FOSM may assign a certain pro
normality assumption inherent in FOSM is of no concern because it only affects the lower bound 
of the predicted seepage rates. 
Three liquid-release tests (Events 5254 of Table 6-5) were performed in zon
May 11, 2000, 09:30, approximately 652 liters of water were released at a very high average rate 
of approximately 464 ml/min. A second test was conducted starting May 17, 2000, 11:39, in 
which about 20 liters of water were released at an approximate rate of 35 ml/min. The final, 
long-term seepage test started on May 23, 2000, 14:22, releasing approximately 334 liters of 
water at an average rate of 26 ml/min (Finsterle 2002 [DIRS 161043], p. 74). Input files for 
simulating this test sequence are created analogous to the procedure described in Section 6.6.2 
(Finsterle 2002 [DIRS 161043], pp. 8283). The software iTOUGH2 V5.0 (LBNL 2002 
[DIRS 160106]) is used to perform 100 Monte Carlo simulations. The resulting predictions of 
seepage rates into the drift are shown i

50LiquidRel
40
Rate[ml/mi1020PredictedRangeofSeepage
0MeasuredSeepage
(b) Time[dayssince5/11/2000,09:30]
6.26.46.66.87-20 
n]
304050LiquidRelease
-10
(c) Time[dayssince5/11/2000,09:30] 
Source: DTN:B00090012213U.002 [DIRS 153154]. 
0302SCMREV02.001. 
vent 52 (the actual seepage is higher than the
Figure 7-1. Liquid-Release Rates, MeasuContaining 90 Percent of the 

A long-term liquid-release test was conducted in zone 2 of borehole SYBT-ECRB-LA#2, 
starting May 11, 2000, 05:20 (Events 55 and 56 of Table 6-5). Figure 7-2 shows the liquid-
release rate, the measured seepage rate, and t
30 ml/min for more than two weeks. (The noise seen in the release and seepage-rate data 
changes with time as the water tank is emptied or filled, respectively.) It takes about 9 days for 
the water to migrate to the drift ceiling and to build up sufficient saturation for seepage to occur. 
The fluctuations in the observed seepage rates reflect the change in ventilation conditions 
(ventilation is on during daytime, and off during nighttime and weekends). This ventilation 
regime is approximately implemented in the model by changing the water potential in the drift, 
accounting for both the reduction in relative humidity and the reduction in the evaporative 
boundary-layer thickness during active ventilation (for details, see Finsterle (2002 
[DIRS 161043], pp. 134135)). The data fall within the uncertainty band of the model 
prediction, meeting the validation acceptance criterion. 
40

ag
la
0
-20-10
Time[dayssince5/17/2001,15:33]
0204060 
Source: DTN:B0203ECRBLIQR.001 [DIRS 158462]. 
0302SCMREV02.001. 

Niche 5, Borehole #5, Interval 2829 f
Water was released from interval 2829 ft of borehole #5 in Niche 5 starting on May 6, 2002 at 
different rates for 4 days (Event 78 of Table 6-5). Testing resumed on May 16, 2002 at a rate of 
approximately 60 ml/min for 5 day
fluctuated significantly, and the test was terminated due to data-logger problems while the 
seepage rate was still increasing (before it reached near-steady-state conditions). Because the 
calibrations were performed to match the late-time response (see Section 6.6.3.2), the model 
prediction during this initial 4 days overestimated the observed seepage rates, meeting the 
validation acceptance criteria. The release rate during the second period (after May 16, 2002) 
was relatively constant and allowed enough time for the seepage rate to reach an approximately 
constant rate. The late-time seepage-rate data in this period fall within the uncertainty band of 
the model prediction, meeting the
l/min]
100120140

o 
Water was released from interval 1011
September 17, 2002 and lasting f
(see Table 6-5) (Day 0 to Day 13) resulted in observable seepage and was used for cal
this validation period, a few simulations yielded seepage and thus a non-zero upper bound of the 
egion. The validation meets the acceptance criteria (see Fig 

11
FlowRate[ml/min]
-1012345678LiquidReleasePredictedSeepageRangeMeasuredSeepage
Time[daysince09/17/200216:20] 
Source: DTN: LB0211NICH5LIQ.001 [DIRS 160792]. 
Output DTN: LB0302SCMREV02.001. 
NOTE: Data for Days 1 through 13 were used for calibration; validation period is between Day 14 and Day 31. 
Figure 7-7. Liquid-Release Rates, Measured Seepage Rates, and Range of Predicted Seepage Rates 
Containing 90 Percent of the 100 Monte Carlo Simulations of the Experiment Conducted in 
Interval 1011 ft of Borehole #4 Niche 5 




on of the model validatio
(Ahlers 2002 [DIRS 161045], p. 57). Figure 7-10 shows the results of the Monte Carlo 
simulations. The measured cumulative seepage mass falls outside the range defined by the 
Monte Carlo simulations in only three of the 27 test events. All three cases (Events 19, 20, and 
22) concern data from interval UM 4.274.57. In a fourth test event conducted in the same 
interval (Event 21), the observed data point is near the upper bound of the predicted seepage 
range. Conditions in this interval seem to be specific and significantly different from those 
encountered elsewhere in Niche 2 and the other two niches in the middle nonlithophysal zone. 
In several other intervals, the simulations show (minor) seepage where no seepage was observed. 
insight into the long-term seepage behavior, because (1) the tests involved only a small portion 
of the fracture system, which is not representative of the fracture system to be engaged in flow 
diversion around the underground opening, and (2) the seepage mass is affected by storage 
effects, which are of no significance for steady-state seepage. As a result, the comparison 
between measured and calculated cumulative seepage has limited usefulness for validating or 
invalidating the seepage model. Nevertheless, as shown in Figure 7-6, the model is overall not 
inconsistent with the observed data, further corroborating the calibrated seepage prediction 
model. 

MC Realizations
Source: DTN: LB980001233124.004 [DIRS 136583]. 
Output DTN: LB0010SCMREV01.001. 
: onte Carlo simulations were performed to estimate the prediction range. The blue symbols indicated the 
ealization with the best-estimate parameter set for the Tptpmn unit. 
. Validation of Seepage Calibration Model and Tptpmn Seepage-Relevant Parameters 
Using Data from Niche 2 
d pre-test prediction of a representative liquid-release test with a rate of 30 ml/min was 
prior to actual testing in Niche 5. The predicted range of seepage rates 
0207PRESCMN5.002 [DIRS 161192], Figure 1) is shown in Figure 7-11, along with 
d seepage-rate data from a liquid-release test in borehole #5 of Niche 5 (using a rate 
proximately 25 ml/min). 
ction cannot be directly compared to the data because of the different release rates. 
i evident that the predicted rates are consistent with the seepage amounts actually 
during the test. The model overpredicted the seepage rates by a small amount, which 
ted because of conservative assumptions made in that model. (Note that the seepage 
ethe ratio between the seepage and release ratesis not expected to be constant; it 
the non-monotonic behavior of the upper lim

ge nitiation. These sensitivity coefficients, which form the basis for estimating the 
uncertainty band, are evaluated for the base-case parameter set.) 
ry, the pre-test prediction of seepage rates p
confidence in the appropriateness of the general modeling approach. 
35
30AssumedReleaseRate
FlowRate[ml/min]
152025ActualReleaseRateRamean+2Std.Dev.
10ActualSeepageRatemean-2Std.Dev.
Time[daysince07/15/200212:45]
024681012 
Source: DTN: LB0207PRESCMN5.002 [DIRS 161192]. 
Output DTN: LB0209NICH5LIQ.001. 
NOTE: The range of predicted seepage covers the uncertainty band approximately on the 95 percent confidence 
level. 
Figure
Calibration Model and Seepage-Rate Data Observed in N
7.4 SUMMARY OF MODEL VALIDATION 
As outlined in Section 7.1, the intended use of the SCM calls for Level I validation activities that 
demonstrate that the basic physical principles are appropriately represented in the model. 
Confidence building during model development is described in Section 7.2.1. Post-development 
activities include several validation methods; they are discussed in Section 7.2.2 and summarized 
below. 
Criteria for confidence building during model development have been satisfied. Estimates of the 
seepage-relevant van Genuchten parameter a/1 for the lower lithophysal zone and the middle 
nonlithophysal zone of the Topopah Spring welded unit were obtained by calibration of a process 
model against seepage-rate data collected in the ECRB Cross-Drift and in Niches 3, 4 and 5. As 
outlined in Section 7.2.1, the develo
ficaly Section 7.2.2 and the supporting references and scientific notebooks), generating 

e in the conceptual and mathematical model used as a basis for the analysis of seepage 
calibrated parameter values obtained at different locations were compiled and 
by means of a statistical distribution to represent spatial variability within a given 
ogic unit (see Section 6.6.4). Sm
gatd through the prediction models during model post-development validation by means 
Carlo simulations, in which the van Genuchten parameter a/1 was sampled from the 
statistical distribution, and a new realization of the random pe
strem seepage modelsthe Seepage Model for Performance Assessment, which uses 
realizations of the underlying heterogeneous permeability field to account for 
y, and the sampling strategy employed during TSPA calculations, which makes use of 
ted statistical distribution representing spatial variability.) 
to AP-SIII.10Q, Section 5.3.2 (c) (1), the model was corroborated by comparing 
seepage rates with measured data not previ 
for the lower lithophysal zone, and in almost all test cases for the middle nonlithophysal 
2) were lower than the predicted seepage rates in a few cases, i.e., the model prediction 
rvative. The data were marginally higher in two longer-term tests (Events 12 and 40) 
ee short-term tests in Niche 2 (Events 19, 20, and 22), which are considered of minor 
. 
re-test prediction of a representative liquid-release test planned fors were consistent with the asso
ng to AP
Q, Sect
2 (d), the
ceptual basis and m
ordi-SIe Seepage Calibra
Model 
rese
athem
in refereed scient
e evidence prese
in Sec
libratio
d the para 
adequate f
uired b 
the models relative importance to 
ystem. 
ities
to co

8. CONCLUSIONS 
SUMMARY AND CONCLUSIONS 
into waste emplacement drifts affects the performance of the proposed high-level 
aste repository at Yucca Mountain, Nevada. Theoretical analyses, numerical modeling 
nd field experiments suggest that seepage into underground openings excavated in 
ed formations is smaller than the percolation flux at the given location. Reduced inflow 
a result of capillary pressures holding water in the formation, diverting it around the 
nd preventing it from entering the underground opening. The effectiveness of this 
barrier depends on the percolation flux, the hydrogeologic properties of the formation, 
etry of the drift, the properties of the drift surface, and the evaporation potential 
ed by in-drift conditions. 
ent, calibration, and valid
ration Model (SCM). The purpose of the SCM is to provide the conceptual basis for the 
Model for Performance Assessment (SMPA), which calculates seepage into waste 
ent drifts for a variety of hydrogeologic conditions. The SCM was calibrated against 
page-rate data to provide seepage-relevant, model-related parameters
eters will be used as base-case parameters for certain SMPA sensitivity analyses and will 
to the development of parameter probability distributions for subsequent Performance 
nt calculations. 
Three-dimensional process models were developed, representing niche loca
the ECRB Cross-Drift, which are located in the lower lithophysal zone of the Topopah Spring 
Tuff (Tptpll). Air-permeability data were geostatistically analyzed to provide the basis for 
generating heterogeneous, spatially correlated permeability fields. 
ese est events were simulated with the SCM, a numerical process model that captures 
unsaturated flow through a heterogeneous fracture continuum and seepage into the 
nd openings. In addition, evaporation effects were accounted for when deemed 
t. Measured seepage-rate data from 22 liquid-release tests were used to calibrate the 
to estimate the seepage-relevant van Genuchten capillary-strength parameter a/1 (see 
6.3.3). The remainder of the seepage-rate data was used to validate the SCM, that is, 
ne whether it is appropriate and adequate for its intended use (see Section 7.3). 
ion of the liquid-releas
dered suitable for calibrating a model that subsequently will be used for the prediction of 
seepage behavior, because (1) the SCM is a transient model that captures the test 
and thus yields parameters that are no
-time seepage-rate data used for model calibration show near-steady 
iorclosely reflecting the processes governing steady-state seepage. Consequently, the use 
e, near-steady seepage-rate data provides an appropriate basis for calibrating a 

model that subsequently will be used for the prediction of steady-state seepage into 
lacement drifts. 
apilary-strength parameter was determined by calibrating the model against multiple tests 
erent liquid-release rates. Some of these release rates induced a local percolation flux 
seepage threshold, i.e., water dripped into the opening and yielded seepage-rate data 
or calibration. Ho
conducted above and below the seepage threshold. That is, the system was probed and the 
s validated for the critical range of percolation rates about the seepage threshold. 
redictions for natural percolation fluxes that are even lower than the low fluxes (below 
e threshold) induced during the low-rate tests will yield the correct result, namely zero 
ay be high and exceed the seepage threshold. Percolation fluxes that are above the 
ge threshold are the critical scenario for performance. Obviously, the parameters estimated 
iquid-release tests would be most suitable for those critical circumstances, because they 
rmined under similar high-rate conditions. In summary, t
from relatively high-rate liquid-release tests are appropriate and provide a solid basis for seepage 
predictions under natural percolation fluxes. 
The insignificant impact of reduced relative humidity on calculated seepage into closed-off 
niches in the middle nonlithophysal zone has been confirmed (see Section 6.7 and discussion in 
Section 6.3.3.4). Significant evaporation effects in the ventilated ECRB Cross-Drift have been 
addressed through moisture control, monitoring of relative humidity, and inclusion of 
evaporation in the numerical model. 
The following general conclusions a
1. The testing and modeling approach documented in this Model Report is adequate for 
providing the conceptual basis and parameters for the TSPA seepage model. The 
approach consists of analyzing seepage by means of a numerical process model that is 
calibrated against seepage-rate data from liquid-release tests conducted within the 
repository host units. 
2. The estimation of seepage-relevant, model-related, effective parameters on the 
appropr
systematic errors, provided that the structure of the prediction model (such as the 
SMPA) is consistent with the model used for calibration (the SCM). 
3. Seepage properties are spatially variable. The variability has been examined by 
performing liquid-release tests at various sites along the ESF and the ECRB Cross-
Drift. Spatial variability in the estimated van Genuchten capillary-strength parameter 
a/1 is relatively strong when compared to the estimation uncertainty at a given 
location. The main contribution to the estimation uncertainty is small-scale 
heterogeneity that can only be described stochastically. Random fluctuat

4. The seepage-relevant van Genuchten capillary-strength parameter a/1 in the lower 
lithophysal zone is on the order of 580 Pa, with a standard deviation, representing 
spatial va
The general conclusions listed above must be viewed
the modeling and analysis approach can be found in Section 8.3 below. 
8.2 MODEL OUTPUT, UNCERTAINTIES, AND LIMITATIONS 
As outlined in Sections 1 and 6.1.1, this Model Report produces (1) a methodological and 
conceptual basis for the subsequent development of the Seepage Model for Performance 
Assessment (SMPA), and (2) seepage-relevant parameters that will be used for drift seepage 
abstraction. The parameter distribution developed as a result of the abstraction process will then 
be usedin
parameter estimation purposes. 
The two products from this Model Reportconceptual model and model-related parameters
are described below. 
Conceptual Basis for Seepage Prediction Models 
The conceptual basis to be used for the SMPA or similar modeling efforts to predict seepage has 
been described in Section 6.3; recommendations for downstream
data analysis, parameter estimation, model prediction, and abstraction. The adequacy of the 
approach is fur
Seepage-Relevant van Genuchten Capillary-Strength Parameter 
t from this Model Report consists of model-related estimates of the van 
ry-strength parameter a/1. The estimates were obtained through calibration 
t seepage-rate data from liquid-release tests; they are summarized inTable 8-1. 
the different locations are combined to obtain seepage characteristics for the 
c units Tptpll and Tptpmn. The standard deviations reported in Table 8-1 
ability. 
uncertain because they are derived from limited data, which exhibit random 
stematic measurement errors, and because the model is a simplification of the 
introduces systematic and random modeling errors. Estimating model-related 
tes the impact of some of the residual systematic errors. The following 
been evaluated: 

inversions with different realizations of the underlying heterog
field. Note, that the impact of this uncertainty on seepage predictions can be directly 
evaluated using the Seepage Model for Performance Assessment (SMPA; BSC 2004 
167652]), i.e., this uncertainty does not need to be incorporated in the 
er distribution used for sampling in TSPA calculations (BSC 2004 
69131], Section 6.6.1.3). 
2. Estimation uncertainty resulting from unexplained fluctuations in the seepage-rate data 
gated through the model and is evaluated using Eq. 6-14; its contribution to the 
arameter uncertainty is negligible (see Section 6.6.3.3). 
tial bias in the estimated parameters as a result of evaporation effects is 
ed by an appropriate test design, moisture monitoring, and inclusion of 
tion effects into the model using site-specific relative humidity and 
tion-rate data. Sensitivity analyses were performed, demonstrating that 
uncertainty resulting from incomplete knowledge about the evaporative 
y-layer thickness in a ventilated drift and uncertainty in the relative h
4. Sensitivity
uncertain parameters on the estimation of seepage-relevant parameters (see Section 
6.6.3.1). These synthetic inversions also provided the correlation structure, indicating 
potential error in one (relatively insensitive) parameter can be partly 
sated for by the estimation of the model-related parameters that are most 
e. 
and Standard Deviation of Capillary-Strength Parameter a/1 fo
Zone and Middle Nonlithophysal Zone 
cation Interval 1/a [Pa] Mean 1/a [Pa] 
Std. Dev. [Pa] 
aCRB-LA#1 zone 1 534 
zone 2 557 CRB-LA#2 
zone 3 535 
CRB-LA#3 zone 1 452 
bore 
582 105 
borehole #5 741 
UM 741 
UL 646 
UM 603 
UR 427 
604 131 
02SCMREV02.002.
atial variability; the impact of uncertainty from undetermined details of small-scale 
seepage predictions is directly evaluated in the S

The primary caveats and limitations for usage of the results from the SCM are as follows: 
1. The seepage models described in this Model Report provide estimates of the seepage 
flux averaged over a drift segment of a certain length. The seepage models are not 
to accurately predict individu
distribution along a waste emplacement drift. By definition, the derived parameters 
related to the specific model structure used, i.e., these parameters are only 
le to a conceptual and numerical model similar to the SCM. (Note that the 
d the SMPA are compatible in this sense (BSC 2004 [DIRS 167652]).) The 
rs are also process specific and scale dependent, i.e., while they can be 
ed optimal for seepage calculations on the drift scale, they are not necessarily 
e to other processes on different scales. If the downstream models cannot be 
d to be fully compatible with the SCM (see Section 8.4), appropriate 
nts to the parameter values should be made. 
ctive parameters derived in this Model Report capture many processes and 
leading to dripping of formation water into a large underground opening. 
r, this does not include water dripping as a result of condensate accumulation 
rift surface or other in-drift moisture redistribution processes. 
during the thermal period is examined separately in the TH Seepage Model 
ift-Scale Coupled Processes (DST and TH Seepage) Models (BSC 2004 
70338])]. 
Y OF INPUT DATA AND MODELING APPROACH 
nts of the overall approach described in this Model Report are (1) the use of a 
numerical process model as the basis for predicting seepage into large 
ings and (2) the calibration of t
liquid-release experiments. This approach is considered appropriate for the following reasons: 
ated flow and seepage into drifts are complex, highly nonlinear phenomena. 
gical process modeling is the preferred means for predicting seepage, because 
key process relevant to seepage (i.e., flow of water u
presumptions need to be made, because the model d
cannot be observed in the field, (4) numerical models are flexible 
accommodate the nonideal initial and boundary conditions as they occur during 
experiments. 
M is based on a process model that is consistent and compatible with the 
al and numerical models used for calculating flow and transport in the 
ted zone at Yucca Mountain. 
experiments provide calibration data that reflect the process of interest. The 
d data automatically reflect the factors and features pertinent to seepage. The 

parameters are capable of reproducing observed seepage data and are thus 
yield reasonable seepage predictions. 
4. The experiments test the capillary-barrier effect on the scale of interest, i.e., no 
upscaling is required. The water encountering the niche or drift is partly diverted 
the opening, engaging the relevant portion of the fracture network on the 
ate scale. 
s released from a localized point or line source, simulating the arrival of 
distance from the release point to the opening does not signif
behavior. 
page experiments are conducted in the excavation-disturbed zone. The 
d parameters therefore reflect the seepage-relevant properties and conditions 
pected in the vicinity of a waste emplacement drift. 
Confidence into the appropriateness of the proposed approach was obtained by meeting 
during the validation exercises (Section 7). While alternative approaches are 
6.4), they often require currently unavailable characterization 
model assumptions, or extensive computational resources. Moreover, they usually need a 
calibration step similar to that described in this Model Reportthey suffer from the same 
ns and caveats. The approach outlined in
adequate for the intended purpose and for the use of the SCM and its results. 
8.4 RECOMMENDATIONS 
data-analysis approach outlined in this Model R
for providing a solid basis and sufficient characterization data for predicting seepage into waste 
emplacement drifts in the repository host rock. The Seepage Calibration Model has been 
validated applying ac
of the models reative importance to the potential performance of the repository system. The 
provided below concern the use of the conceptual model for the seepage 
performed by the Seepage Model for Performance Assessment (BSC 2004 
predictions should make use of a physically based process model capable of 
ng unsaturated flow under viscous, capillary pressure, and gravitational forces. 
2. A three-dimensional, heterogeneous continuum model should be developed. The 
computational grid should capture the overall shape of the drift; small-scale surface 
ss should not be explicitly discretized; lithophysal cavities should not be 
y discretized; the nodal distance between the formation and the drift element 
e 0.05 m; and gravity must be acting along those vertical connections. 

-relevant input parameters should be used that are specifically determined for 
ceptual model, based on data that represent the key
seepage. 
n runs with different realizations of the underlying heterogeneous permeability 
formed and appropriately averaged. 
8.5 HOW THE ACCEPTANCE CRITERIA ARE ADDRESSED 
The following inform
Yucca Mountain Review Plan (NRC 2003 [DIRS 163274], Sections 2.2.1.3.3.3 and 
.1.3.6.3). In most cases, the applicable acceptance criteria are not addressed solely by this 
ort; rather, the acceptance criteria are fully addressed when this report is considered in 
junction with other analysis and model reports that describe flow paths in the unsaturated 
e. Only those acceptance criteria that are applicable to this report (see Section 4.2) are 
ussed. 
Section 4.2, the U.S. Nuclear Regulatory Commission acceptance criteria considered 
licable to this Model Report were identified. These include criteria in YMRP Section 
1.3.3.3, Quality and Chemistry of Water Contacting Waste Packages and Waste Forms, and 
Flow Path in the Unsaturated Zone. This section summ
YMRP Section 2.2.1.3.6.3, 
those criteria are addressed
Details about how data, parameter, and model uncertainties are propagated through the model 
abstraction are primarily discussed in the seepage abstraction model report (BSC 2004 
[DIRS 169131]). This Model Report does not address the chemistry of seepage water, which is 
evaluated by the THC Seepage Model (BSC 2004 [DIRS 168848]). 
YMRP Section 2.2.1.3.3.3: Quality and Chemistry of Water Contacting Waste Packages and 
Waste Forms 
Acceptance Criterion 1, System Description and Model Integration are Adequate: 
Subcriterion (2): The conceptual and related pro
phenomena of liquid release, unsaturated flow, and seepage are described (see 
Sections 6.3 and 6.6). These descriptions demonstrate that the model is based 
on a clear understanding of the relevant seepage processes (see Section 6.3.3), 
supported by field data from

r Model Justifica
bly account for uncertainties and varid (3) do not result in an under-repres

odel Uncertainty is Characterized and
Subcriterion (1): Alternative modeling 
considered (see Section 6.4). 
Subcriterion (2): Alternative conceptual models and their limitations an
discussed qualitatively (see Sectionare discussed: (1) discrete fracture network model; (2) seepage infponding probability; (3) seepage inferred from
erred fro
seepage threshold directly inferred from liquid-release 
discrete fracture network model is a recognized, appropriate alimited by (1) data limitations, (2) the need for many auxiliary s(3) computational difficulties. Moreover, calibrated dis
lthoughative, it 
s, and 
models are expected to give results comparable to those obbase-case continuum model (see Section 6.4.1). Pondexplicitly included in the current seepage process models, wflow through a heterogeneous medium, which leads to loca
d from thffects a
Section 6.4.2). Observations of calcite and opal in lithophysal cavbe used to estimate long-term seepage rates into small openings.
there are uncertainties associated with (1) the process leadmineral depositions at cavity floors, (2) the geo
ities cou 
Howeve
water composition, and (3) upscaling issues (see Section 
using short-term liquid-release test data yields non-conservthreshold es
results (see Section 6.6.3.3), and model validation (see Sectiothe selected modeling approach is the one most consisteand current s 
show th
Subcriterion (3): Model uncertainty is minimized by requesting consistency bcalibration and prediction model (see Sections 6.3.4 and 8.4). 
limitations and uncertainties of the chosen model are discus1, 6.3.4, and 
tween thMoreove
with available site characterization data (see Section 6.6.4). 
YMRP Section 2.2.1.3.6.3, Flow Path in the Unsaturated Zone 
Subcriterion (2): The aspects of geology, hydrology, physical phenomena, andmay affect flow paths in the unsaturated zone arthrough in si
plings th
(see Sections 6.6.1.4, 6.3.4, and 6.6.3), and process modeling 
6.6). Important physical phenomena are adequately incorpomathematical model for flow in the unsaturated zone in t(see Section 6.6.1). Flow is modeled mtheore
ee Sectited in th

6.6.1.2). Evaporation from a wetted porous surface is 
Section 6.6.1.3). Model calibration is based on established inveprocedures (see Section 6.6.1.5). 
Subcriterion (6): Adequ
rated (se
variability of boundary conditions are employed to estimateSections 6.6.2.1, 6.6.2.2, 6.6.2.3, and 6.6.4). 
Subcriterion (7): The average parameter estimates are representative o
e flux (se
6.6.3.1). 
Subcriterion (1): Hydrological values used in the seepage process model for 
unsaturated flow above the underground opening are a
simulatin
Sections 4.1 and 6.6.2). Adequate descriptions of how the datainterpreted, and appropriately synthesized into the parameters 
(see Sections 6.5.2, 6.6.1.4, 6.6.2, 6.6.3, and 8.3). 
were use 
provided 
zone in the vicinity of drifts were 
developed based on appropriate techniques, which include air-inj(see Section 6.5.2) and liquid-release tests (see Section 6.5.3). 
Subcriterion (5): Model validation and uncertainty analyses are performsufficiency (see Sections 6.6.4 and 7.2.2). 
ction tes
Subcriterion (6): Accepted and well-documented procedures are used to cothe numerical models (see Section 6.6.1). 
Subcriterion (7): Reasonably complete process-level conceptual and mathematicused in the analyses (see Sections 6.3 and 6.6). 
d calibra
Acceptance Criterion 3, Data Uncertainty is Characterized and Propagated 
Model Abstraction: 
rough the
Subcriterion (1): The parameters used in and derived by the seepage process model are based 
on and consistent with available data from Yucca Mountain (
6.5, and 6.6.3). Assumptions made are technically defe
ctions 4.
Seepage is analyzed using a combination of techniques, including direct 
interpretation of field data (see Sections 6.5, 6.6.1.4, 6.6.3.3, 
natural analogs (see Sections 6.4.3 and 7.2.1). Uncertaare evaluated
6.4.4), an
seepage abstraction to TSPA-LA (BSC 2004 [DIRS 169131]).
Subcriterion (4): Initial and boundary conditions are consistunder
evaporative boundary-layer thickness used in the concSection 6.6.1.3) determined from evaporation rate measurements (see Section
odel (se

saturation distribution (see Section 6.6.2.4) are consistent 
model of the unsaturated zone at Yucca Mountain. 
Subcriterion (6): Uncertainties in the characteristics of the natural system are consperforming multiple inv
conceptu
random permeability field, and by evaluating the estimation errorelevant parameters (see Sections 6.6.4 and 8.2). 
Acceptance Criterion 4, Model Uncertainty is Characterized and Propa
Model Abstraction: 
Subcriterion (1): Alternative modeling approaches consistent with availab
discrete fracture network model (see Section 6.4.1), seepage governedprobability model (see Section 6.4.2), using geochemical information tSection 6.4.3), and estimatin
a pondin
Section 6.4.4). Model, parameter, and data uncertainties are addressed through (1) the 
estimation of effective, model-related parameters (see Section 6.3.4), (2) 
estimation uncertainty through inverse modeling (see Sections 6.6ensuring consistency between calibration and prediction models (see Secresults and limitations of these alternate models are addressed i
ination 
8.6 OUTPUT DTNS 
The computer files needed to reproduce the model results discussed in this Mode
submitted to the Technical Data Management System (TDMS). Reprodappropriately qualified individual is possible by consulting this Model Reportscientific notebook pages
ity by an 
The analyses of seepage-rate data from liquid-release tests performed in tnonlithophysal zone were fully documented in an earlier revision (REV 01
he midd
TDMS under DTN: LB0010SCMREV01.001. 
The files supporting the analyses of data from the lower lithophysal zone are listed 
this Model Report (see Table 6-4) and the 1/a estimates (see Table 8-1) were sTDMS under DTN: LB0302SCMREV02.002. 
itted to th

INTENTIONALLY LEFT BLANK 

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17+26 in Cross Drift. Submittal date: 11/09/2000. 
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MO0003GSC00103.000. Exploratory Studies Facility (ESF) Niche 3 Profile 
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152176 
MO0008GSC00273.000. Exploratory Studies Facility (ESF) Niche 4, Profile 
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MO0407SEPFEPLA.000. LA FEP List
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9.4
LB0010SCMREV01.001. Input/O 
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U
08
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L
0
2SCMREV02. 
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2SCMREV02.002. Se 
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9. 
SOFTWARE COD
CRW
S M&O 1999. So
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Dyna
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LBNL (Lawrence Berkeley Na
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A
dC
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-1.0-00. 
LBNL (Lawrence Berkeley Na
atory) 10/17/2000. Software Routine: 1528
C
tN
che. V1.2. SUN w/
-1.2-00. 
L
N 
(Lawrence Berkeley N
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Sun. 10047-1.0-00. 

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L
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V4.0. SUN, DEC. 10003-4.0-00. 
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139918 
V
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AMV3V1.201. SU
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1.0GAMV3V1.201-00. 
1530
LBNL (Lawrence Berkeley 
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V
.0
ISIMV1.204. SUN
7-1.0SISIMV1.204-00. 
LBNL (Lawrence Berkeley 
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A
dB
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3-1.0-00. 
LBNL (Lawrence Berkeley 
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V1.0. SUN w/Unix OS. 10357-1.0-00. 
LBNL (Lawrence Berkeley 
ory) 2000. Software Routine: C
V1.0. SUN w/Unix OS. 10375-1.0-00. 
LBNL (Lawrence Berkeley 
ory) 2000. Software Routine: Cu
V
.3 
SUN w/Solaris OS. 10402-1.3-00. 
LBNL (Lawrence Berkeley 
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V1.0. SUN w/Unix OS. 10358-1.0-00. 
P
m
Mesh. V1.0. SUN 
Software Routine: 
-1.0-00. 
LBNL (Lawrence Berkeley 
ory) 2002. Software Code: iTOU
V5.0. SUN UltraSparc., DEC AL
X. 10003-5.0-00. 

PENDIX A 
LIST OF COMPUTER FILES S
SCMREV02.001 


Computer files needed to reproduce the model results discussed in this Model Report are l
6- 
a
d throughout this Mod 
file name is complemented with a short description 
of
ts
contents and/or pu
e modeling of liquid-release tests conducted in
N
h 
5; Table A-2 con
odeling of test in
us
d
to analyze data from 
ents in Niches 2, 3, and 4 (located in 
no
l
hophysal zone) were p
tted under output DTN: LB0010SCMREV
Table A-1. File Nam 
for Modeling of Liquid-Release Tests in Nich
File/Folder Name
Description/File Location 
M
sh
eneration/ 
M
shgeneration/Permeabilit 
measured_log-k_12_N5.da
easured air permeability data, input file mesh gen 
N5-airK.dat 
easured air permeability data, input file to GAMV3 
N5-airK.par 
arameter file, input to GAMV3 
N5-airK.var 
omputed variogram by GAMV3 
N5-airK.xls 
xcel file to compile variogram 
M
shgeneration/Roughness/ 
ompilation of the original survey and roughness me 
ceiling_N5_survey.dat 
e main niche 
ceiling_N5_interpolated.dat
terpolated data on a regular x-y coordinate (10 cm 
ceiling_N5_1.dat 
terpolated main niche ceiling for 15.60<y<17.60 
ceiling_N5_2.dat 
terpolated main niche ceiling for 18.60<y<20.60 
ceiling_N5_3.dat 
terpolated main niche ceiling for 20.90<y<22.90 
leftbatwing_N5_survey.dat 
lation of the original survey and roughness measthe left batwing niche 
leftbatwing_N5_interpolated
terpolated data on a regular x-y coordinate (10 cm 
leftbatwing_N5_1.dat 
terpolated left batwing niche ceiling for 15.60<y<17 
leftbatwing_N5_2.dat 
terpolated left batwing niche ceiling for 18.60<y<20 
leftbatwing_N5_3.dat 
terpolated left batwing niche ceiling for 20.90<y<22 
rightbatwing_N5_survey.da
ompilation of the original survey and roughness me 
rightbatwing_N5_interpolate
the right batwing niche 
ated data on a regular x-y coordinate (10 cm x 1 
rightbatwing_N5_1.dat 
terpolated right batwing niche ceiling for 15.60<y<1 
rightbatwing_N5_2.dat 
ated right batwing niche ceiling for 18.60<y< 
rightbatwing_N5_3.dat 
terpolated right batwing niche ceiling for 20.90<y<2 
Niche1620_ceiling.lpk 
ew of the niche 5 ceili 
N
ecplot file 

Table A-1. File Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continue
File/Folder Name 
Description/File Location 
eshgeneration/N5BH4_10- 
N5BH4_10-11ft 
TOUGH2 input file with MESHMAKER block 
onestep 
TOUGH2 input file to perform a single time step 
N5BH4_10-11ft_sisim.par 
Parameter file for generation of random permeability field 
Sequence of instructions to execute multiple steps of mesh 
sh.N5BH4_10-11ft_mesh
generations 
sh.N5BH4_10-11ft_ru
Sequence of instrucinstructions mu
field seed number 
sh.onestep 
Sequence of instructions to run one time step 
N5BH4_10-11ft.mes1 
Mesh with permeability realization #1 
N5BH4_10-11ft.mes2 
Mesh with permeability realization #2 
N5BH4_10-11ft.mes3 
Mesh with permeability realization #3 
N5BH4_10-11ft.mes4 
Mesh with permeability realization #4 
N5BH4_10-11ft.mes5 
Mesh with permeability realization #5 
N5BH4_10-11ft.mes6 
Mesh with permeability realization #6 
N5BH4_10-11ft.mes7 
Mesh with permeability realization #7 
N5BH4_10-11ft.mes8 
Mesh with permeability realization #8 
N5BH4_10-11ft.mes9 
Mesh with permeability realization #9 
N5BH4_10-11ft.mes10 
Mesh with permeability realization #10 
N5BH4_10-11ft.mes11 
Mesh with permeability realization #11 
N5BH4_10-11ft.mes12 
Mesh with permeability realization #12 
N5BH4_10-11ft.mes13 
Mesh with permeability realization #13 
N5BH4_10-11ft.mes14 
Mesh with permeability realization #14 
N5BH4_10-11ft.mes15 
Mesh with permeability realization #15 
N5BH4_10-11ft.mes16 
Mesh with permeability realization #16 
N5BH4_10-11ft.mes17 
Mesh with permeability realization #17 
N5BH4_10-11ft.mes18 
Mesh with permeability realization #18 
N5BH4_10-11ft.mes19 
Mesh with permeability realization #19 
N5BH4_10-11ft.mes20 
Mesh with permeability realization #20 
N5BH4_10-11ft.mes21 
Mesh with permeability realization #21 
N5BH4_10-11ft.mes22 
Mesh with permeability realization #22 
N5BH4_10-11ft.mes23 
Mesh with permeability realization #23 
N5BH4_10-11ft.mes24 
Mesh with permeability realization #24 
N5BH4_10-11ft.mes25 
es26 
Mesh with permeability realization #25 
N5BH4_10-11ft.m
Mesh 
N5BH
Mesh 
N5BH4_10-11ft.mes28 
N5BH4_10-11ft.mes2
Mesh with permeab 
N5BH4_10-11ft.mes3
Mesh with permeabilit

Table A-1. File Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
Description/File Location 
ceiling_N5_1.dat 
File is in folder: Mesh 
measured_log-k_12_N5.dat 
File is in folder: Meshgeneration 
M
shgeneration/N5BH5_21 
N5BH5_21-22ft 
TOUGH2 input file 
sh.N5BH5_21-22ft_mes 
Sequence 
gener 
N5BH5_21-22ft.mes 
ations 
Mesh with permeability realization # 
onestep 
File is in folder: Meshgeneration/N5BH4_10-11ft/ 
sh.onestep 
File is in folder: Meshgeneration/N5BH4_ 
c
F 
leftbatwing_N5_2.dat 
rightbatwing_N5_2.da
File is in folder: Mes 
M
shgeneration/N5BH5_28 
N5BH5_28-29ft 
TOUGH2 input file 
N5BH5_28-29ft_sisim.par 
Parameter file for generation of random permea
Sequence of instructi 
sh.N5BH5_28-29ft_mesh
generations 
Sequence of instructions to ru
instruc 
sh.N5BH5_28-29ft_run 
field seed number 
N5BH5_28-29ft.mes1
Mesh with permeability realization #1 
N5BH5_28-29ft.mes2 
Mesh with permeability realization #2 
N5
Mesh 
N5BH5_28-29ft.mes4 
N5BH5_28-29ft.mes5 
Mesh with permeab 
N5BH5_28-29ft.mes6 
Mesh with permeab 
N5BH5_28-29ft.mes7 
Mesh with permeabili 
N5BH5_28-29ft.mes8 
Mesh with permeability realizatio 
N5BH5_28-29ft.mes9 
Mesh with permeability realization #9 
N5BH5_28-29ft.mes10 
Mesh with permeabilit 
N5BH5_28-29ft.mes11 
Mesh with permeability realiz 
N5BH5_28-29ft.mes12 
Mesh with 
N5BH5_28-29ft.mes13 
Mesh with permeability realization #13 
N5BH5_28-29ft.mes14
Mesh with permeability realization #14 
N5BH5_28-29ft.mes15 
Mesh with permeability realization #15 
N5
Mesh 
N5BH5_28-29ft.mes17 
N5BH5_28-29ft.mes18 
Mesh with permeab 
N5BH5_28-29ft.mes19 
Mesh with permeab

Table A-1. File Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
Description/File Location 
N5BH5_28-29ft.mes20 
Mesh with permeabili 
N5BH5_28-29ft.mes21 
Mesh with permeability realizatio 
N5BH5_28-29ft.mes22 
Mesh with permeability realization #22 
N5BH5_28-29ft.mes23 
Mesh with permeabilit 
N5BH5_28-29ft.mes24 
Mesh with permeability realiz 
N5BH5_28-29ft.mes25 
Mesh with 
N5BH5_28-29ft.mes26 
Mesh with permeability realization #26 
N5BH5_28-29ft.mes27
Mesh with permeability realization #27 
N5BH5_28-29ft.mes28 
Mesh with permeability realization #28 
N5
Mesh 
N5BH5_2
Mesh 
measured_log-k_12_N5.dat 
File is in folder: Mes 
ceiling_N5_3.dat 
File is in folder: Meshgeneration/Roughness/ 
leftbatwing_N5_3.dat 
File is in folder: Meshgeneration/Roughness/ 
rightbatwing_N5_3.dat 
File is in folder: Meshgeneration/Roughness/ 
Onestep 
File is in folder: Me 
sh.onestep 
File is in folder: Meshgeneration/N5BH4_10-11ft/ 
L
u
ReleaseTestData/ 
pilation of Re 
LB0207NICH5LIQ5-01-
DTN: LB0207NICH5LIQ.001 [DIRS 160408] 
LB
Compi
Compi 
LB0207NICH5LIQ5-02.xls 
DTN: LB0207N 
LB0209NICH5LIQ7-15.xls 
Compilation of Liquid release test data from 
DTN: LB0209NICH5LIQ.001 [DIRS 160796] 
LB0209NICH5LIQ7-16.xls 
Compilation of Liquid release test data from 
DTN: LB0209NICH5LIQ.001 [DIRS 160796] 
Compilation of Liquid release test data from 
LB0211NICH5LIQ9-17#1.xls 
DTN: LB0211NCompilation of Liqu 
LB0211NICH5LIQ9-17#2.xls 
DTN: LB0211NICH5LIQ.001 [DIRS 160792 
N5BH4_10-11ft_rate.dat 
Seepage rate for Borehole #4 (1011ft) 
N5BH5_21-22ft_rate.dat 
Seepage rate for Borehole #5 (2122ft) 
N5BH5_28-29ft_05-06-0
Seepage rate for Borehole #5 (2829ft), for test started on 
5/6/02 
Seep 
N5BH5_ 

le A-1. File Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
escription/File Location 
N5BH4_10-1al condition without evaporation 
N5BH4_1
TOUGH2 input file 
N5BH4_10-1
iTOUGH2 input file 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_10-11ft_cal4
iTOUGH2 output file, permeability realization #4 
N5BH4_10-11ft_
iTOUGH2 output file, permeability realization #5 
N5BH4_10-11ft_
iTOUGH2 output file, permeability realization #6 
N5BH4_10-11ft_
iTOUGH2 output file, permeability realization #7 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BH4_1
iTOUGH2 output file, permeability realizati 
N5BHiTOUGH2 output file, permeability realization #13 
N5BH4_10-11ft_ 
N5
H4_10-11ft_cal15i
iTOUGH2 output file, permeability re 
N5
H4_10-11ft_cal16i
iTOUGH2 output file, permeability re 
N5
H4_10-11ft_cal17i
iTOUGH2 output file, permeability re 
N5
H4_10-11ft_cal18i
iTOUGH2 output file, permeability re 
N5
H4_10-11ft_cal19i
iTOUGH2 output file, permeability re 
N5
H4_10-11ft_cal20i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal21i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal22i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal23i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal24i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal25i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal26i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal27i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal28i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal29i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_cal30i.o
iTOUGH2 output file, permeability real 
N5
H4_10-11ft_rate.da
File is in folder: LiquidReleaseTestDa 

Text Box: DISCLAIMER
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third partys use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
N5
_28-29ft_cal2 
H
iTOUGH2 output file, permeability realization #19 
N5N5BH
H
_28-29ft_
iTOUGH2 output file, permeability realization #20 
UGH2 outpu 
N5BH5_28-29ft_cal2 
5_28-29ft_cal29i.out 
Validation
N5
H
/N5BH4_1011ft/ 
0-11ft_val 
0-11ft_vali 
Validation 
N5
H 
N5BHiTOUGH2 output file, permeability realization #22 
iTOUGH2 output file, permeability realization #23 
N5BH
N5
H
is mesh number 
e is in folder: LiquidReleaseTestData/ 
N5
H
8-29ft_rate.dat 
/ 
TOUGH2 input file 
iTOUGH2 input file 
Table A-1. ile Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
escription/File Location 
C
lib
ation/N5B5_28-29ft/ 
sh
N5
H5_28-29
Sequence of commands start iTOUGH2 on 
(Node 1 to Node 10) 
sh
N5
H5_28-29ft_calM 
Sequence of commands to start iTOUGH2 on the master node 
N5
H
_28-29ft_ininonevap 
TOUGH2 input file for initial condition without evaporation 
N5
H
_28-29ft_inievap 
TOUGH2 input file for initial condition wi 
N5
H
_28-29ft_cal 
TOUGH2 input file 
N5
H
_28-29ft_cali 
iTOUGH2 input file 
N5
H
_28-29ft_cal1i.out 
iTOUGH2 output file, permeability realization #1 
N5
H
_28-29ft_cal3i.out 
iTOUGH2 output file, permeability realization #2 
N5
H
_28-29ft_cal4i.out 
iTOUGH2 output file, permeability realization #3 
N5
H
_28-29ft_cal6i.out 
iTOUGH2 output file, permeability realization #4 
N5
H
_28-29ft_cal7i.out 
iTOUGH2 output file, permeability realization #5 
N5
H
_28-29ft_cal8i.out 
iTOUGH2 output file, permeability realization #6 
N5
H
_28-29ft_cal9i.out 
iTOUGH2 output file, permeability realization #7 
N5
H
_28-29ft_cal11i.out 
iTOUGH2 output file, permeability realization #8 
N5
H
_28-29ft_cal12i.out 
iTOUGH2 output file, permeability realization #9 
N5
H
_28-29ft_cal13i.out 
iTOUGH2 output file, permeability realization #10 
N5
H
_28-29ft_cal14i.out 
iTOUGH2 output file, permeability realization #11 
N5
H
_28-29ft_cal15i.out 
iTOUGH2 output file, permeability realization #12 
N5
H
_28-29ft_cal16i.out 
iTOUGH2 output file, permeability realization #13 
N5
H
_28-29ft_cal18i.out 
iTOUGH2 output file, permeability realization #14 
N5
H
_28-29ft_cal19i.out 
iTOUGH2 output file, permeability realization #15 
N5
H
_28-29ft_cal20i.out 
iTOUGH2 output file, permeability realization #16 
N5
H
_28-29ft_cal21i.out 
iTOUGH2 output file, permeability realization #17 
N5
H
_28-29ft_cal23i.out 
iTOUGH2 output file, permeability realization #18 

Table A-1. ile Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
escription/File Location 
H4_10-11ft_val.da 
N5BH4_10-11ft_ 
N5 
Tecplot input file 
H4_10-11ft_val.lpk
Tecplot file (packaged data) 
N5
Image file 
me
asured_log-k_12_N 
N5
H4_10-11ft.mes1 
File is in folder: Meshgeneration/N5 
N5 
N5 
N5 
H4_10-11ft_val.wm
V 
N5
H5_21-22ft_val 
TOUGH2 input file 
N5
H4_10-11ft_rate.d
File is in folder: LiquidReleaseTestD 
N5
H5_21-22ft_vali.out 
iTOUGH2 output file 
N5
H5_21-22ft_vali.tec
id
tion/N5BH5_21-22ft
H5_21-22ft_vali_mc.tec 
iTOUGH2 output file for Tecplot (Mont 
iTOUGH2 input file 
H4_10-11ft_vali_m
iTOUGH2 output file for Tecplot (Mon
File is in folder: Meshgeneration/Pe 
H5_21-22ft_vali 
iTOUGH2 output file for Tecplot 
N5
N5
H5_21-22ft_val.dat 
Input file for Tecplot 
Tecplot file (packaged data) 
N5
H5_21-22ft_val.wm
Image file 
me
asured_log-k_12_N5 
N5
H5_21-22ft.mes 
File is in folder: Meshgeneration/N5B 
H5_21-22ft_rate.da
File is in folder: LiquidReleaseTestDa 
N5 
id
V 
N5
H
N5
H 
N5
H
_28-29ft_05-06-02_val
Tecplot file (packaged data) 
N5
H 
N5
me
asur
N5
H 
V
id
/N5BH5_28-29ft_07- 
N5
H
_28-29ft_07-15-02_val 
TOUGH2 input file 
N5
H
_28-29ft_07-15-02_va
iTOUGH2 input file 
N5
H5_21-22ft_val.lpk 
File is in folder: Meshgeneration/Perm 
tion/N5BH5_28-29ft
N5
H5_28-29ft_05-06-0
TOUGH2 input file 
H5_28-29ft_05-06-02_va
iTOUGH2 input file 
N5
H5_28-29ft_05-06-02_vaUGH2 output file for Tecplot 
_28-29ft_05-06-02_va
iTOUGH2 output file for Tecplot (Monte 
_28-29ft_05-06-02_va
Input file for Tecplot 
_28-29ft_05-06-02_val.
Image file 
ed_log-k_12_N5.dat 
File is in folder: Mes 
_28-29ft.mes1 
File is in folder: Meshgeneration 

Table A-1. ile Name and Description for Modeling of Liquid-Release Tests in Niche 5 (Continued) 
File/Folder Name 
escription/File Location 
N5
H
_28-29ft_07-15-02_va
iTOUGH2 output file 
N5
H
_28-29ft_07-15-02_va
iTOUGH2 output file for Tecplot 
N5
H
_28-29ft_07-15-02_vali_mc.tec 
iTOUGH2 output file for Tecplot (Monte Carlo) 
N5
H
_28-29ft_07-15-02
Input file for Tecplot 
N5
H
_28-29ft_07-15-02_val.lpk 
Tecplot file (packaged data) 
N5
_28-29ft_07-15-02_val.w
Image file 
H 
me
asur
File is in folder: Meshgeneration/Permeability/ 
N5
H
File is in folder: Meshgeneration/N5BH5_28-29ft/ 
N5
_28-29ft_rate.dat 
File is in folder: LiquidReleaseTestData/ 
M
al
nc 
N5
_10-11ft_forwardevap 
TOUGH2 input file 
H
_10-11ft_forwardevapi 
N5
H
iTOUGH2 output file, with evaporation connections 
N5
_10-11ft_forwardevapi.te
Tecplot input file, with evaporation connections 
H
_10-11ft_forwardseep 
N5
H
iTOUGH2 input file, with seepage and deep flow con 
N5
_10-11ft_forwardseepi.ou
iTOUGH2 output file, with seepage and deep flow co 
H
_10-11ft_forwardseepi.te 
N5
H
File is in folder: Meshgeneration/N5BH4_10-11ft/ 
F
w
_28-29ft_forwarddeepi 
iTOUGH2 input file, with seepage components conne
iTOUGH2 output file for Tecplot, with seepage compo
N5
8-29ft_forwarddeepi.tec 
8-29ft_forwardseep 
connections 
TOUGH2 input file 
N5BHN5 
N5
HH
iTOUGH2 output file, with seepage connections 
N5
8-29ft_forwardseepi.tec 
iTOUGH2 output file for Tecplot, with seepage connections 
28-29ft/ 
_28-29ft.mes1 
E 
ra 
Evapor
Estimation of boundary-layer thickness (d) for e 
ation_Calibration.xls 
rsio
n/ 
N5
H
TOUGH2 input file 
N5 
H
_28-29ft_forwarddeepi.ou
N5
H 
ed_log-k_12_N5.dat 
_28-29ft.mes1 
H 
ss
/ 
H
N5
iTOUGH2 input file, with evaporation connections 
_10-11ft_forwardevapi.ou 
H
N5
TOUGH input file 
_10-11ft_forwardseepi 
H
N5
iTOUGH2 output file for Tecplot, with seepage and dconnections 
_28-29ft.mes1 
ive 
_28-29ft_forwarddeep 
H
N5
iTOUGH2 output file, with seepage components conn 
H
5_28-29ft_forwardseepi age connections 
5_2pi.
8-29ft_forwardsee
H
File is in folder: Meshgeneration/N5BH5_
vapor diffusion 

Table A-2. File Name and Description for Modeling of Liquid-Release Tests in ECRB Cross-Drift 
File/Folder Name 
Description/File Location 
S 
/Fig
ur
(files used for visualization purposes 
12
t 
For plot rate vs. time, LA#1, Zone 2, S 
S
t.dat 
21
.d
For plot rate vs. time, LA#2, Zone 1, Set 1 
22
t 
For plot rate vs. time, LA#2, Zone 2, Set 1 
.d
t 
_10Sat.dat after 10 days, LA#2 Zone 2 Set 2 
22
0Sat.dat 
For saturation distribution plot after 20 days, LA#2 Zone 2 Set 2 
_3
0Sat.dat 
23
.d 
23
t 
For plot rate vs. time, LA#2, Zone 3, Set 2 
.d
t 
32
.d
For plot rate vs. time, LA#3, Zone 2, Set 1 
For plot rate vs. time, LA#2, Zone 3, S 
ST
/LA1/Zone2 
1_zone2.mes1 
Mesh with permeability realization #1 
LA
1_zone2.mes2 
LA
Mesh with permeability realization #3 
1_zone2.mes4 
Mesh with permeability realization #4 
LA
1_zone2.mes6 
LA
Mesh with permeability realization #10 
1_zone2.mes11 
Mesh with permeability realization #11 
S
s/ 
12 
t 
.d
22
22
_2 
t 
.d
31
For plot rate vs. time, LA#3, Zone 1, Set 1 
t 
LA 
Mesh with permeability realization #2 
LA1_zone2.mes13 
LA 
LA
1_zone2.mes16 
LA
1_zone2.mes23 
Mesh with permeability realization #14 
Mesh with permeability realization #21 
LA 
1_zone2.mes14 
Mesh with permeability realization #17
LA 
1_zone2.mes24 

ST/LA1/Zone2/Set2 
Cum.xls 
Excel file with processed seepage data 
LA1_zone2_ini
DTN: LB0110ECRBLIQR.002 [DIRS 1568TOUGH2 input file to create initial cond 
LA1_zone2_ini
TOUGH2 input file to create initial cond 
LA1_zone2_set2.bc 
Relative humidity boundary condition file 
LA1_zone2_set2.dat 
Seepage-rate data file for calibration 
LA1_zone2_set2 
TOUGH2 input for simulating liquid-rele 
LA1_zone2_set2i 
LA1_zone2_set2i.out1 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out2 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out3 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out4 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out6 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out10 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out11 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.out12 
iTOUGH2 output file with permeability re 
LA1_zone2_set2i.oGH2 output file with permeability realization #14 
LA1_zone2_set2i.out1 
LA1_zone2_set2i.out1
iTOUGH2 output file with permeability realization #17 
LA1_zone2_set2i.out21
iTOUGH2 output file with permeability realization #21 
iTOUGH2 output file w 
LA1_zone2_set2i.out2
iTOUGH2 output file with permeab 
Excel file with processed relative-humidity data from 
RH.xls 
DTN: LB0110ECRBLIQR.002 [DIRS 156879] 
inievapi 
Dummy iTOUGH2 input file for generatingevaporation 
S
2/ 
ed for simulation of liquid-release tests in borehol 
ST
2/Zone1 
ST
/LA2/Zone1/Set1 
Cum.xls 
LA2_zone1_set1.dat 
DTN: LB00090012213U.002 [DIRS 153154] 
Seepage-rate data file for validation 
LA2_zone1_set1_val 
TOUGH2 input file for simulating liquid-release test 
iTOUGH2 input file for performing Monte Carlo simul 
LA2_zone1_set1_vali 
LA2_zone1_set1_vali.o
iTOUGH2 output file 
Mesh file used for validation runs 
LA2_zone1_val.mes 

ST
/LA
2/Zone2 
LA
2_zone2.mes1 
Mesh with permeability realization #1 
LA
2_zone2.mes2 
Mesh with permeability realization #2 
LA
2_zone2.mes3 
Mesh with permeability realization #3 
LA
2_zone2.mes4 
Mesh with permeability realization #4 
LA
2_zone2.mes5 
Mesh with permeability realization #5 
LA
2_zone2.mes6 
Mesh with permeability realization #6 
LA
2_zone2.mes8 
Mesh with permeability realization #8 
LA
2_zone2.mes9 
Mesh with permeability realization #9 
LA
2_zone2.mes10 
Mesh with permeability realization #10 
LA2Mesh with permeability realization #12 
LA2_zone2.m 
LA2_zone2.mes14
Mesh with permeability realization #14 
LA2_zone2.mes16
Mesh with permeability realization #16 
LA2_zone2.mes17
Mesh with permeability realization #17 
LA2_zone2.mes18
Mesh with permeability realization #18 
LA2_zone2.mes21
Mesh with permeability realization #21 
LA2_zone2.mes22 
LA2_zone2.mes24
Mesh with permeability realization #24 
Mesh with permeability realization #25 
LA2_zone2.mes26
Mesh with permeability realization #26 
LA2_zone2.mes98 
Mesh, boundary-layer thickness 0.5 cm 
LA2_zone2.mes99 
Mesh, boundary-layer thickness 2.0 cm 
LA2_zone2.mes200
Mesh, extended mesh 
ST/LA2/Zone2/Set1
(seepage data from DTN: LB00090 
LA2_zone2_set1.bc 
Relative humidity boundary condition file 
LA2_zone2_set1.dat 
Seepage-rate data file 
LA2_zone2_set1_val 
TOUGH2 input file for simulating liquid-r 
LA2_zone2_set1_vali 
iTOUGH2 input file for performing Monte 
LA2_zone2_set1_vali.
iTOUGH2 output file 
LA2_zone2_val.mes 
Mesh file used for validation runs 
ST/LA2/Zone2/Set2
(seepage data from DTN: LB0110S 
LA2_zone2_inie
TOUGH2 input file to create initial condi 
LA2_zone2_inin
TOUGH2 input file to create initial condi 
LA2_zone2_set2.bc 
Relative humidity boundary condition file

LA2_zone2_set2 
LA2_zone
Processed seepage and relative humidity data from 
DTN: LB0110SYST0015.001 [DIRS 160409] 
LA2_zone2_set2i 
iTOUGH2 input for calibrating against seepage-rate data 
LA2_zone2_set2i.out1 
iTOUGH2 output file with permeability realization #1 
LA2_zone2_set2i.out
iTOUGH2 output file with permeability rea 
LA2_zone2_set2i.out3
iTOUGH2 output file with permeability rea 
LA2_zone2_set2i.
iTOUGH2 output file with permeability realization 
LA2_zone2_set2i.o
iTOUGH2 output file with permeability realization #5 
LA2_zone2_set2i.out6 
iTOUGH2 output file with permeability realization #6 
LA2_zone2_set2i.out8 
iTOUGH2 output file with permeability realization #8 
LA2_zone2_set2i.out9 
iTOUGH2 output file with permeability realization #9 
LA2_zone2_set2i.out10
iTOUGH2 output file with permeability realization #10 
iTOUGH2 output file with permeability realization #11 
LA2_zone2_set2i.out12
iTOUGH2 output file with permeability realization #12 
LA2_zone2_set2i.out13
iTOUGH2 output file with permeability realization #13 
LA2_zone2_set2i.out14
iTOUGH2 output file with permeability realization #14 
LA2_zone2_set2i.out16
iTOUGH2 output file with permeability realization #16 
LA2_zone2_set2i.out17 
iTOUGH2 output file with permeability realization #17 
LA2_zone2_set2i.out18 
iTOUGH2 output file with permeability realization #18 
LA2_zone2_set2i.out21 
iTOUGH2 output file with permeability realization #21 
LA2_zone2_set2i.out22 
iTOUGH2 output file with permeability realization #22 
LA2_zone2_set2i.out24 
iTOUGH2 output file with permeability realization #24 
LA2_zone2_set2i.out25 
iTOUGH2 output file with permeability realization #25 
LA2_zone2_set2i.out26 
iTOUGH2 output file with permeability realization #26 
LA2_zone2_set2i.out98 
iTOUGH2 output file, boundary-layer thickness 0.5 cm 
LA2_zone2_set2i.out99 
iTOUGH2 output file, boundary-layer thickness 2.0 cm 
ST
/LA2/Zone2/Set2half 
(sensitivity to reduction in 
LA2_zone2_inievap 
TOUGH2 input file to create initial conditions with dry-o 
LA2_zone2_ininoevap 
TOUGH2 input file to create initial conditions without ve 
LA2_zone2_set2.dat 
Seepage-rate data file for calibration 
LA2_zon 
LA2_zone2_set2h 
iTOUGH2 output for calibrating against seepage-ra 
ST
(sensitivity to extent 

LA2_zone2_inievap 
TOUGH2 input file to create initial conditions with dry-out zo 
LA2_zone2_ininoevap 
TOUGH2 input file to 
LA2_zone2_set2.dat 
Seepage-rate data file for calibratio 
LA2_zone2_ease test 
LA2_zo
iTOUGH2 input for calibrating against seepag 
LA2_zone2_ 
(sensitivity to evaporative surface 
LA2_z
TOUGH2 input file 
LA2_zone2
TOUGH2 input file to create initial conditions 
LA2_zone2_se 
LA2_zone2_se
Seepage-rate data file for calibration 
tive humidity boundary condition 
LA2_zone2_set
TOUGH2 input for simulating liquid-release test 
LA2_zone2_set2less
iTOUGH2 input for calibrating against seepage-rate data 
iTOUGH2 output for calibrating against seepage-rate data 
ST/LA2/Zone2/Set2
(sensitivity to evaporation) 
LA2_zone2_inievap 
Dummy TOUGH2 input file to create initial conditions 
GH2 input file to create initial co 
LA2_zone2_set
TOUnditions without ventilation 
page-rate data file for calibration 
LA2_
Relative humidity boundary condition file 
GH2 input for simulatin 
iTOUGH2 input for calibrating against seepage-rate da 
iTOUGH2 output for calibrating against seepage-rate data 
ST/LA2/Z
(sensitivity to input param 
LA2_
TOUGH2 input file to create initial conditions with dry-out 
H

(seepage data from DTN 

Mesh with permeabilitMesh with permeability reaMesh with permeability rea


Table A-2. File Name and Description for Modeling of Liquid-Release Tests in ECRB Cross-Drift (ContinuFile/Folder Name Description/File Location 
LA3_zone2_set1_vali iTOUGH2 input file for performing Monte Carlo simulations 
LA3_zone2_set1_vali.out iTOUGH2 output file 
LA3_zone2_val.mes Mesh file used for validation runs 
ST/Meshgeneration/ (files used for mesh generation 
mesh3dblock TOUGH2 input file with MESHMAKER block 
mesh3dlargeblock TOUGH2 input file with MESHMAKER block, extended model 
onestep TOUGH2 input file to perform single time step 
perm.par SISIM input file for generating heterogeneous log-permeability 
modifier field 
primary.mes Primary mesh file 
primarylarge.mes 
sh.LA1_zone2_mesh Sequence of commands used to generate meshes for tests in zoneof borehole SYBT-ECRB-LA#1 
sh.LA2_zone 
f borehole SYBT-ECRB-LA#2 
sh.LAzone3_mesh Sequence of commands used to generate meshes for tes ineof borehole SYBT-ECRB-LA#2 
sh.LA3_zone1_mesh Sequence of commands used to generate meshes for tests in zoneof borehole SYBT-ECRB-LA#3 
sh.LA3_zone2_mesh Sequence of commands used to generate meshes for tests in zone 
rse modeling results 
sh.run_forward Sequence of commands used to perform a single forward run 
sh.run_val Sequence of commands used to perform validation runs 


APPE
DIX B 
NG 


the spherical vaioe corresponding empirical variogram value, and iw is a coefficient for weighting 
The spherical variogram with a nugget effect is given by (after Deutsch and Journel 


Table B-1. EXCEL Spreadsheet Vario.xls for Fitting Spherical Variogram to Empirical Log-
Permeability Variogram 
A B C D E F G 
2 nugget0.005957 
3 correlation length0.608829 
sill0.487922 
K6=objective func. 
Lag [m] Gamma # of pairs 1/(# of p
7 3 0.3050.341931380.00725-12.21159-12.21159 0.34193 6.78058E-128 4 0.6090.514541320.00758-12.23576-12.23576 0.493878 7.430211773 
5 0.9280.484612820.00355-12.24096-12.24096 0.493878 6.815955381 
6 1.2260.436194540.0022-12.19152-12
11 7 1.5120.486583320.00301-12.20027-12.20027 0.493878 5.87880692812 8 1.8170.46843200.00313-12.201-12.201 0.493878 66.25919213 
9 2.1450.486565640.00177-12.23769-12.23769 0.493878 17.094354592.450.501273860.00259-12.23959-12.23959 0.493878 8.14535658
15 11 2.7330.557053180.00314-12.2467-12.2467 0.493878 404.751331 
12 3.0330.470563180.00314-12.2
18 14 3.6530.490272660.00376-12.20598-12.20598 0.493878 0.920845218 
15 3.9630.50312440.0041-12.22635-12.22635 0.493878 5.059074452
22 18 4.890.624851780.00562-12.18056-12.18056 0.493878 543.1046002 
19 5.1980.590261680.00595-12.14107-12.14107 0.493878 262.3957768 
20 5.5070.554591360.00735-12.15426-12.15426 0.493878 68.22957124
26 22 6.1140.564081160.00862-12.20509-12.20509 0.493878 66.32586675 
23 6.4330.39963940.01064-12.21426-12.21426 0.493878 78.46250161 
24 6.7450.49685740.01351-12.30514-12.30514 0.493878 0.048389527 
25 7.0410.42472580.01724-12.29034-12.29034 0.493878 16.09206783 
26 7.340.55424440.02273-12.30341-12.30341 0.493878 7.052232487 
27 7.6380.66126320.03125-12.33875-12.33875 0.493878 28.68909386
06625 0.493878 4.43631024
35 31 8.7530.0004520.5-11.625-11.625 0.493878 0.973885245 
K7-K35 = weighted residuals 

IN
N
EF
K 

S
MESH GENERATION FOR SIMULATION OF SEEPAGE TEST


M
developed, e
permeability field. The following steps are performed: 
1. A mesh was genera
discretized into regular gridblockwas aligned with the drift axis.
co 
mesh is shifted by 8
2. The Z-coordinate of then
[DIRS 152824]) to tra
gr
3. F
perm.
generate a random, spat
4. The heterogeneous field of
Perm2Mesh V1.0 (
l drift of rad
5. A cylindrica
2000 [DIRS 152816]). The drift is centered at X = 0 and Z = 0 with its axis parallel to the Y-
6. Software AddBound V1.0 (LBNL 2000 [DIRS 152823]) is used to attach boundary elem
otto
at the top and baterial dom
special m
7. 
representing a 6 ft (1.8288 m
The injection interv
X = 0.0 m, Y = 1.8288 m, and a Z coordinate that depends on the pinning an
tested (see also Table C-1). The elevations Z [m] of the beg
interval are calculated from their respective distances from the borehole collar 
follows: 
5.2)15sin(3048.0+=dZ 
8. Dc
9. Two new evaporation elem
elements represents evaporation. 
10. A single time step was performed using a generic TOUGH2 input file one
iTOUGH2 V5.0 (LBNL 2002 [DIRS 160106]); s

obtain cross-referencing information. The execution
sh
11. The final mesh is sto
designates the test 
permeability field. 
File sh.LAx_zoneY_mes (see Figure C-5) was used to execute Mesh Gen
e
file documents all the Unix commands used as well as input variables to t
codes. An excerpt from a final sample mesh file LAx_zoneY.mesZ is shown in Figure C
Table C-1. Input Z-Coordinates to Software AddBo 
Beg
Distan
Bore
SYBT-ECRB-LB0110ECRBL
LA#1 [DIRS 156
1 17 3SYBT-ECRB-LB00090012213U.002 
LA#2 [DIRS 153154] 
3 49 6.37 
1 18 3.92 SYBT-ECRB-LB0203ECRBLIQR.001 
LA#3 [DIRS 158462] 2 34 5.18 40 
TOUGH2 input file for generating 3D block 
MESHMZ 
XY 
NX 1 0.050000
NZ 1 0.600E-10 
NZ 110 0.1000000 
NZ 1 0.600E-10 
ENDFI ---1----*----2----*----3----*----4----*----5 
to Generate Primary Mesh 
Figure C-1. Input File mesh3dblock 

SISIM V
1.204 INPUT FILE per 
****************************
**** 
Generates weakly correlated random field of log-permeability modifiers 
for the seepage model of the systematic testing area in the ECRB Cross
-Drift. 
START OF PARAMETERS: 
dum
my.dat \data file 
1 2 3 4 \column: x,y,z,vr 
-1.0e21 1.0e21 
-2.0 2.0 \minimum and maximum data value 
1 2.5 
1 1.0 
4 2.5 \upper tail option and parameter 
dummy.d
3 0
dir
perm.dat \output file for simulation 
1 
\debugging level: 0,1,2,3 
perm.dbg \output File for Debugging 
0 
590
1 \number of simulations 
41 0.00 0.10 \nx,xmn,xsiz 
12 
0.1524 0.3048 \ny,ymn,ysiz 
110 -3.00 0.10 \nz,zmn,zsiz 
1 \0=two part search, 1=data-nodes 
0 
2.0 
0.0 0.0 0.0 1.0 1.0 
0 20 
12 
\number 
0 2.5 \0=full I
0 \0

Text Box: DISCLAIMER
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third partys use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
BOUND 0 2650. .9900 1.000E-08 100000. 
DRAIN 0 2650. .9900 1.000E-08 100000. 
SKINZ 0 2650. .2000 1.000E-12 1000. 
MATRI 0 2650. .1000 1.000E-17 1000. 
NICHE 0 2650. .9900 1.000E-08 100000. 
PARAM----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
2 1 1100000000000000400003000 
1.000E-10 
0.5 
1 
1.000E-11 
INCON----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
ENDCY----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
# 
# Unix shell script file sh.onestep 
# usage: sh.onestep MeshFileName 
# 
# 
# 
echo 
echo Start shell script sh.onestep $1 
echo ============================= 
# 
echo 
echo ----------------- 
tough2 -v 5.0 -mesh -m $1 onestep 9 
mv onestep.mes $1 
Figure C-4. File sh.onestep Used to Execute a Forward Run with a Single T
PACKE 0 2650. .1000 1.000E-20 1000. 
TIMES----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
START----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
#! /bin/sh 
# S. Finsterle, August 2002 
echo Run one time step 
Generic TOUGH2 input file; performs a single small time step 
ROCKS----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
FRACT 0 2650. .1000 1.000E-12 1000. 
LITHO 0 2650. .9900 1.000E-06 100000. 
DRIFT 0 2650. .9900 1.000E-08 100000. 
EVAPO 0 2650. .9900 1.000E-08 100000. 
BOREH 0 2650. .9900 1.000E-08 100000. 

#! /bin/sh 
# 
# 
# sh.LAx_zoneY_mesh 
# 
# S. Finsterle, 8/27/2002, V1.0 
# 
echo 
echo ==================================== 
echo 
# 
j=0 
# Start loop 
while test $j -lt $runs 
do 
j=`expr $j + 1` 
i=`expr $j + $j` 
i=`expr $i + 59067` 
o " " 
ech 
echo "========================================================================= 
echo "========================================================================= 
echo "Create permeability modifier field, seed number: $i" 
cat perm.par | sed "s/^.*seed/$i \\\seed/" \ 
> perm.par$j 
xSisim << eof 
perm.par$j 
# Unix shell script file to generate TOUGH2 mesh 
echo Start shell script sh.LAx_zoneY_mesh 
echo Date : `date` 
i=0 
# generate new seed number 
echo "Run $j of $runs" 

# nodal distance to b
1-

erated: LAx_zoneY.mes$j 
========================= 
pleted: `date` 


B 3AT5 1 10.1000E-090.5000E-010.3086E-010.9659E+00 
B 4AT6 1 10.1000E-090.5000E-010.1549E-010.9659E+00 
B 5AS6 1 10.1000E-090.5000E-010.2228E-010.9659E+00 
B 6AS7 1 10.1000E-090.5000E-010.2394E-010.9659E+00 
B 7AR7 1 10.1000E-090.5000E-010.1383E-010.9659E+00 
B 8AR8 1 10.1000E-090.5000E-010.3239E-010.9659E+00 
B 9AQ8 1 10.1000E-090.5000E-010.5377E-020.9659E+00 
B 10AQ9 1 10.1000E-090.5000E-010.3004E-010.9659E+00 
B 1B 2 20.1578E+000.2888E-010.1140E-02-.2588E+00 
B 3B 4 20.1289E+000.6470E-010.1140E-02-.2588E+00 
4B 5 20.6470E-010.9307E-010.1140E-02-.2588E+00 
5B 6 20.9307E-010.1000E+000.1140E-02-.2588E+00 
6B 7 20.1000E+000.5777E-010.1140E-02-.2588E+00 
B 9B 10 20.2246E-010.1255E+000.1140E-02-.2588E+00 
BOR 0B 1 30.1000E-090.1905E-010.3777E-010.1000E+01 
0B 2 30.1000E-090.1905E-010.6913E-020.1000E+01 
0B 3 30.1000E-090.1905E-010.3086E-010.1000E+01 
0B 4 30.1000E-090.1905E-010.1549E-010.1000E+01 
BOR 0B 6 30.1000E-090.1905E-010.2394E-010.1000E+01 
BOR 0B 7 30.1000E-090.1905E-010.1383E-010.1000E+01 
BOR 0B 9 30.1000E-090.1905E-010.5377E-020.1000E+01 
BOR 0B 10 30.1000E-090.1905E-010.3004E-010.1000E+01 
42113 55 42113 56 42113 115 42113 116 42113 175 
42113 176 42113 235 42113 236 42113 295 42113 296 
42113 355 42113 356 42113 415 42113 416 42113 475 
..... ..... ..... ..... ..... ..... ..... ..... ..... ..... 
42109 42110 42111 42101 42111 42102 42111 42103 42111 42104 
42111 42105 42111 42106 42111 42107 42111 42108 42111 42109 
42111 42110 
Figure C-6. Excerpt from Sample Mesh File LAx_zoneY.mesZ 

INTENTIONALLY LEFT BLANK 

MESH GENERATION FOR SIMULATION OF SEEPAGE TESTS 


Four 3-D meshes for the simulation of liquid-release tests in niches located in the middle 
nonlithophysal zone were generated: two 1.5 m long sections of Niche 2 centered (a) 4.42 m and 
(b) 5.64 m from the collar of borehole UM, (c) a 1.5 m long section of Niche 3 centered at
0.25 m, and (d) a 2.0 m long section of Niche 4 centered at N 
The meshes were created in several steps as follows (where
information for each mesh is preceded by the letter referring to a specific panel of Figure 6-16): 
1. Primary meshes were generate 
 5.0 m, and (d) 6.0 m  2.0 m  5.0 m, respectively,
iche 
2. The X-, Y-, an 
Z-coo
dinates of each primary m
(d), and Z = 0 is (a
MoveMesh V1.0 
Y = 0 coincides w
LBNL 
ith Nich
000 [DIRS 152824]) so thtion 00 + 0.0 m for Mesh
the bottom of the
esh an 
(c & d) coincides with the local 
3. The heterogeneous perroutine Perm2Mesh V1.0 (LBNL 2000 [DIRS 152826]). 
m
ields were mapped onto their r
espective meshes using
4. (a & b) A niche with vertical walls at X = -2 m and X =
with the crown at (a) Z = 3.13 m and (b) Z = 3.33 m was cut from the mesh using software 
CutNiche V1.3 (LBNL 2000 [DIRS 152828]). (c & d) Niches with vertical walls at 
(c) X = -2.15 m and X = 2.35 m and (d) X = -2.00 m and X = 1.90 m and ceilings defined by 
survey data are cut from their respective meshes using software CutNiche V1.2 (LBNL 2000 
[DIRS 152815]). A very small nodal distance
e drift surace and the gridblocks denoting the drift, wh
locks rep
formatio
terface de
Software AddBound V1.0 (LBNL 2000 [Delements at the top and bottom of the model dom
assigned to a special material domain to allow specifying a free-drainage boundary condition. 
6. Gridblocks along the alignment of the injection boreholes were modified to represent 1 ft 
(0.3048 m) long injection intervals with a diameter of 3 inches (0.076 m). 
The final meshes (see Figure 6-16) conta
340 gridblocks and 108,000 
c in various SNs (Finsterle 
1], pp. 2729, 4244, 54; 
H999 [DIRS 153449], pp. 108123; and 
Trautz 2001 [DIRS 156903], pp. 3545). 

FT BLANK 

AP
IX E 
ES
ERATI
OR SEE 
TEST
ULA
NS I


sur
oughness 
reproduc 
interp
n fro
y da
Table E-1
s for the
ng of N 
5 
DIRS 
ption 
Coo
te Sys
003 
Niche 5 p
rvey data
155370] 
010 
Niche 5 sl
y data 
ada S
lane 
155369] 
ILN
161733] 
Niche 5 suted bottom
ta for colntervals 
LNG
161733] 
e 5 detai
e surve
e from referenc
ent 
DTN=Data
g Numbe
follo
n preparing th
e interpo
Niche 5 ceiling profiles are described below. 
poi
the mesh
s selecte
e the in
tion o
enter
ble E-2. 
m of Niche
e Nevad
rdina
stem 
orthi
Elev
uth 
D [m 
[m] 
ELD
A 
rees]
62.51 
110
DTN:
9GSC0033
370 
survey d
DTN: M
00 [D
o a regul
ZY-
inate
em us
the fo
tion of the computational meshes for simulating liquid-release tests performed in Nimajor steps: ration of niche ceiling coord
E 
PREPARA
ION OF NICHE CEILING COORDINATES 
()()()().-+.-=sincosDDNNESESX (Eq. E-1) 
()()()().-+.-=sincosDESESNNY (Eq. E-2 

A-=.360 
Dat
d Thei
dina 
Nor
Elev
X [m
23
11
-4.8
23
11
-2.8
23
11
23
11
0.00
23
11
0.00
23
11
0.00
11
0.00
23
11
0.00
23
11
0.00
23
11
11
3.03
23
11
3.95
23
11
-2.6
23
11
23
11
0.00
23
11
0.00
23
11
2.59
11
-2.7
23
11
2.66
23
11
0.00
23
11
0.00
11
0.00
233262.77 
1108.62 
2.51 13.59 
0.00 13.98 
233262.42 
1110.09 
233262.39 
1106.57 
0.00 14.01 
233262.21 
1108.48 
-2.26 
2323
11
2.04
11
0.00
23
11
0.00
23
11
2.03
23
11
23
11
0.00
23
11
0.00
23
11
2.08
11
-1.8
23
11
-0.0
23
11
-0.0

rofile Survey Data and Slot Survey Data and TZ Coordinate System (Conti
e 5 Pro
Nor
Elev
23
11
2.13
23
11
-1.8
23
11
1.97
23
11
23
11
0.00
23
11
0.00
23
11
-2.4
11
-2.0
23
11
-0.0
23
11
-0.0
23
11
23
11
-2.0
233247.45 
1110.10 
-0.01 
233247.45 
1106.88 
-0.01 
233247.21 
1108.73 
0.00 29.19 
e 5 Slo
Nor
Elev
X [m
1
-2.0
23
1
-2.4
23
1
-2.8
23
1
-3.1
23
1
-3.3
23
-3.5
23
1
-3.5
23
1
23
1
-3.0
23
-2.7
23
1
-2.3
1
-2.0
23
1
-1.9
23
1
-1.9
23
1
-2.0
23
1
-2.5
23
-2.7
23
1
-3.0
23
1
-3.4
23
1
-3.7
23
-3.8
23
1
-3.8
23
1
-3.4
23
1
-3.2
23
1
-3.0
23
1
-2.42 

Nor
X [m
23
1
-2.2
23
1
-1.9
23
1
-2.2
23
-2.6
23
1
-3.2
23
1
-3.4
23
1
-3.7
23
1
-3.6
23
1
-3.5
23
1
-3.3
23
-3.0
23
11
-2.4
233254.48 
1107.45 
1109.11 
-2.05 
233253.96 
-2.04 
232
1
-2.2
1
-2.5
23
1
-2.9
23
1
-3.1
23
1
-3.6
23
1
-3.9
23
-4.0
23
1
-3.6
23
1
23
1
-3.2
23
-2.9
23
1
-2.5
23
1
-2.3
23
1
-2.2
23
1
-2.1
23
1
-2.1
23
1
-2.1
23
1
-2.2
23
1
-2.1
23
1
-2.2
23
1
-2.0
23
1
23
1
-2.0
23
1
-2.0
23
1
-2.1
23
1
-2.1
23
1
-2.3
1
-2.2
223
-2.5
23
11
-3.0

Nor
X [m
23
1
-3.5
23
-2.0
23
-2.1
23
1
-2.5
23
1
-3.0
23
1
-3.2
23
1
-3.1
23
1
-2.7
23
1
-2.1
23
1
-2.0
23
1
-2.0
23
-2.1
233257.32 
1
1108.38 
-2.55 
233257.16 
1108.42 
-2.69 
23
1
-3.1
23
1
2.0
23
1
2.3
23
1
2.5
23
1
2.8
23
1
2.8
23
1
2.4
1
23
2.1
23
1
2.2
23
1
2.3
23
1
2.6
1
2.7
23
1
2.8
23
1
2.7
23
1
2.4
23
1
2.4
23
1
2.3
32.00
5370], 
0106
.98 
a

Table E-4. Niche 5 Ceiling Roughness Data 
X [m] Y [m] Z [m] 
-1.40 27.86 
-1.09 27.86 
-0.17 27.8
0.14 27.80.45 27.8
0.76 27
1.06 271.37 27
1.68 27
-1.40 26
-1.09 26
-0.78 26.56 4.36 
1.37 26.56 4.20 

Table E4. Niche 5 Ceiling Roughness Data 
(Continued) 
X [m] Y [m] Z [m] 
-0.78 22.66 4.32 
-0.48 22.66 4.42 
-0.17 
0.45 22.66 4.71 
1.3
.66 
4.26 
-1.40 
-1 
36 
4.13 
-0
36 
-0 
36 
4.35 
4.33 
0 
36 
0.76 
1 
36 
4.19 
1 
36 
-1 
06 
3.90 
4.11 
-1 
-0 
06 
0 
06 
0 
06 
4.31 
11.37 
06 
-1 
76 
3.96 
3.89 
-1 
76 
-0.48 18.76 4.10 
-0.17 18.76 4.09 
0.14 18.76 4.07 
0.45 18.76 4.08 
0.76 18.76 4.08 
1.06 18.76 4.13 
1.37 18.76 4.20 
1.68 18.76 3.79 
-1.40 17.46 4.00 
-1.09 17.46 3.91 
-0.78 17.46 3.93 

7 
14.87 
8 
14.87 
0 
15.19 
0 
15.51 
0 
16.49 
0 
16.81 
17.14 
17.79 
18.11 
18.44 
19.09 
19.41 
0 
19.74 
0 
20.39 
20.71 
0 
0 
0 
21.69 
0 
21.04 
Table E4. Niche 5 Ceiling Roughness Data 
(Continued) 
-0.78 16.16 4.06 
-0.416.16 4.14 
-0.17 16.16 4.12 
0.14 16.16 4.09 
0.45 16.16 4.18 
0.76 16.16 4.09 
3.92 
3.77 
1.40 
9 
14.87 
14.87 
14.87 
14.87 
14.87 
14.87 
14.87 
14.87 

26.24 
(Continued) 
Z [m] 
1.40 
1.4
0 
22.34 
22.99 
23.31 
0 
23.64 
0 
24.29 
24.61 
24.94 
26.89 
27.21 
27.54 
8 
15.19 
8 
15.51 
8 
15.84 
8 
8 
16.81 
8 
17.14 
14.87 
1 
16.16 
5 
18.76 
7 
20.06 
8 
21.36 
5 
7 
23.96 
5 
25.26 
0 
25.91 
16.49 
9 
22.66 
1 
26.56 
3 
14.87 
4 
16.16 
9 
3 
18.76 
4 
20.06 
8 
23.96 
25.26 
2 
26.56 
9 
27.86 
0 
0.14 
1 
27.86 
17.46 
1 
27.86 

0.000000E+
Excerpts of the resulting Niche 5 ceiling and slot profiles are shown in Figure E-1, and 
Figure E-2 shows the plan view of the ceiling roughness. 
Niche 1620 ceiling 
00E+0010.000000E+000 
00E+001 4.222630E+000 
E+001 4.225305E+000 
2223
21
Y[m]
181920Z[m]
4.54.2
16173.4
-4-3-2-1012314 
put DTN: LB0302SCMREV02.002. 
ble E-3 and Table E-4 were interpolated
152.82.5

E2. LOCATION OF BOREHOLES AND PREPARATION OF GEOSTATISTICAL 
PARAMETERS OF AIR-PERMEABILITY 
[DIRS 169532]) were transformed from the Nevada coordinate system to the regular ZYX-- 
rdinate system using Eqs. E-1 to E-4. The original borehole surveys and their transformed 
Table E-5. Original and Transformed Coordinates of Borehole Collars and Projected Bottoms 
rehole Collar/Bottom Easting (m) 
Northing 
(m) 
Elevation 
(m) Depth (m)X (m) Y (m) Z (m) 
Projected Bottom 170662.83 233246.93 1111.61 -0.83 29.48 5.70 
ECRB-NICHE1620 #3 
Collar #3 170662.25 233262.72 1111.00 0.02 13.68 5.09 
Projected Bottom 170661.98 233247.23 1111.29 
15.50 
0.02 29.17 5.38 
ECRB-NICHE1620 #4 
Collar #5 170663.27 233262.84 1111.42 -1.00 13.58 5.51 
rojected Bottom 170662.87 233247.08 1113.25 
15.88 
-0.87 29.33 
P
ECRB-NICHE1620 #6 
Collar #6 
ECRB-NICHE1620 #7 
Projected Bottom 170661.17 233248.01 1113.27 
14.81 
0.84 28.37 7.36 
easured by air-injection tests conducted in boreholes #2, #3, and #5 (see 
Collar #4 170661.26 233262.76 1111.04 1.01 13.63 5.13 
Projected Bottom 170661.16 23315.02 
Projected Bottom 

collars. These distances were first converted t
ing ZYX-- coordinates by the following elementa
()()(22zyyxx-'+''-'+''-' 
yydyY'-''
+'= 
()()()222zzyyxx''-'+''-'+''-' 
()zzdzZ'-''
+'= (Eq. 
()()()zzyyxx''-'+''-'+''-'
()'''()''''''ectively (see
of the borehole are denoted by zyx,, and zyx,,, resp
Table E-6.
DTN: LB0110AKN5POST.001 
[DIRS 156904] Calculated 
Start End 
2Midpoint Midpoint 
0 3 4 2.70E-12 3.5 1.07 -0.99 14.26 5.63
0 4 5 5.62E-12 4.5 1.37 -0
0 5 6 5.48E-09 5.5 1.68 -0.98 14.86 5.7015.16 5.73 
0 6 7 4.27E-09 6.5 1.98 -0.98 
0 7 8 4.08E-12 7.5 2.29 -0.98 15.47 5.7
0 9 10 7.77E-12 9.5 2.90 -0.97 16.07 5.8
16.38 5.87
0 10 11 3.30E-12 10.5 3.20 -0.97 
0 11 12 2.79E-11 11.5 3.51 -0.97 16.68 5.9
0 12 13 3.83E-11 12.5 3.81 -0.97 16.98 5.94
0 13 14 1.65E-10 13.5 4.11 -0.97 17.28 5.9
0 14 15 1.82E-10 14.5 4.42 -0.96 17.59 6.01
4.72 -0.96 
0 15 16 2.35E-11 15.5 17.89 6.04 
3 4 5 1.61E-11 4.5 1.37 -0.96 14.57 5.135.14 
3 5 6 3.18E-12 5.5 1.68 -0.96 14.87 
3 6 7 1.56E-11 6.5 1.98 -0.96 15.18 5.1615.48 5.17
3 7 8 1.47E-12 7.5 2.29 -0.96 
3 8 9 4.08E-10 8.5 2.59 -0.95 15.79 5.1
16.09 5.19
3 9 10 6.23E-10 9.5 2.90 -0.95 
3 10 11 6.24E-10 10.5 3.20 -0.95 16.40 5.2
3 11 12 5.52E-10 11.5 3.51 -0.94 16.70 5.21 -9.2581 
3 113 1.19E-12 12.5 3.81 -0.94 17.01 5.22 -11.9245 

[DIRS 156904] 
BH Start End k [m2] Mi
[ft] [ft] 
3 15 16 2.23E-12 
3 16 17 4.03E-09 
3 17 18 1.92E-09 
4 3 4 5.85E-09 
4 4 5 9.51E-09 
4 5 6 9.32E-12 
4 6 7 8.85E-12 
4 8 9 4.16E-13 
4 9 10 1.87E-12 
4 10 11 1.16E-13 
4 11 12 4.87E-14 
4 12 13 5.25E-13 
4 13 14 2.20E-11 
4 14 15 3.66E-11 
4 15 16 4.82E-14 
4 16 17 5.91E-13 
4 18 19 3.71E-11 
4 19 20 8.39E-13 
4 20 21 2.48E-12 
4 21 22 1.82E-12 
4 22 23 1.86E-13 
4 23 24 2.33E-13 
4 24 25 2.65E-12 
4 25 26 2.14E-12 
4 26 27 2.11E-13 
4 27 28 2.95E-13 
4 28 29 6.71E-11 
4 29 30 6.87E-11 
4 30 31 1.64E-11 
4 31 32 7.19E-12 
4 32 33 2.43E-12 
4 34 35 1.06E-12 
4 35 36 1.57E-12 
BH=borehole; DTN=Data Tracking Numb 

Multiple numerical meshes of a 2 m long section of the Niche 5 w
different stochastic realization of the underlying heterogeneous perm
three test zones, labeled Niche5a, Niche5b, and Niche5c. The locati
of these meshes are listed below in Table E-8. 
Table E-8. Primary Dimensions of Niche 5 Meshes 
Locaax
0.0 + 15.60 
0.0 + 19.60 
0.0 + 21.90 
ollowing mesh generation steps were perform
The f
mesh of borehole #5 (28-29 ft). 
1. A mesh was ge
nerated with X-Y-Z dimensions as listed in Table E-8, discre 
 0.1 m  0.1 m. The Y-axis was aligned
Niche centerline. Figure E-3 sh
gen
erate the mesh. 
2. The mesh was shifted using software Movtranslate the origin of the mesh to the datum of
3. The GSLIB module SISIM V1.204 (LBNL 2random, spatially correlated field of log-perm
seed number was inserted into the SISIM V
le measured_log-k_12_N5.dat (see Figure E-5). An excerpt of the 
meability field is shown in Figure E-6. 

8. Gridblock
(0.3 m) long injection intervals and 3 ft (0.9 m) long packers on both sides of the injection 
interval. 
9. 
Drift elements (DRI78, DRI79, DRI88, 
volume so Dirichlet boundary conditio
large 
represents seep
10. Six new evaporation eleadded and connected to the sam
distance from the formation elements to the ev
boundary-layer thickness. Flux into these elements represents eva
11. A 
single time step was performed using a generic TOUGH2 input 
iTOUGH2 V5.0 (LBNL 2002 [DIRS 160106]); s
obtain cross-referencing information. The execution
file
Steps 1-11 listed above were 
The script file sh.N5BH5_28
-29ft_run (see Figure E-10) assigns new seed numneration of permeability field and generates multiple m
ge
sh.N5B
sto
The final mesh is 
underlying random permeability field. 
TOUGH2 input file for generating 3D grid for Niche 5 
MESHMAKER ----*----2----*----3----*----4----*----5----*----6----*----7----*----8 
XYZ 
NX 
NY 20 0.1000000 
NZ 1 0.600E-10 
50 0.1000000 
NZ 
NZ 1 0.600E-10 
ENDFI ---1--
Figure E-3. Input File N5BH5_28-29ft Used to Generate Primary Mesh 

Parameters for SISIM 
******************** 
Niche 5 Borehole #5 (28-29 ft) 
TAG July 22, 2002 
START OF PARAMETERS: 
measured_log-k_12_N5.dat 
1 2 3 4 \column: 
x,y,z,
-1.0e21 1.0e21 \data trimming l
-2.0 5.0 \minimum and maximum data value 
1 2.5 \lower t
1 1.0 \middle 
4 
dum
3 0 \column for variable, weight 
direct.ik \direct input of indi
N5BH5_28-29ft_airK.dat \output file for
2 
N5BH5_
0 \0=standard order relation corr 
59069 
1 
80 -4.45 0.10 
20 
50 2.05 0.10 
1 
0 
2.0 
0.0 0.0 0
0 20 \ min, max data for simulation 
12 
\number simulated nodes t0 2.5 \0=full IK, 1=med approx(c
0 \0=SK, 1=OK 
8 \number cutoffs 
-0.725 0.066 1 0.02 \cutoff, global cdf, nst, nugget 
1 0.96 1.82 \ 
0.0 0.0 0.0 1.0 1.0 \ 
ang1,a-0.050 0.197 1 0.02 \cutoff, global 
1 0.96 1.82 \ it, aa, 
0.0 0.0 0.0 1.0 1.0 \ ang1,ang2,ang3,anis1,2 
0.625 0.443 1 0.02 \cutoff, 
0.0 0.0 0.0 1.0 1.0 \ ang1,ang2 
1.300 0.623 1 0.02 \cutoff, global cdf 
1 0.96 1.82 \ it, aa, cc 
0.0 0.0 0.0 1.0 1.0 \ ang1,ang 
1.9
7 
1 0.96 1.82 
0.0 0.0 0.0 1.0 1 
2.650 0.852 1 0.02 \cutoff, global cdf, nst, nugget 
1 0.96 1.82 
0.0 0.0 0.0 1.0 1.0 \ ang1,ang2,ang3,anis1,2 
3.
32 
1 0.96 1.82 
0.0 0.0 0.0 1.0 1 
4.000 0.999 1 0.02 
1 0.96 
0.0 
Figure E-4. Input File Pa
Generating Software SISIM 

N5-air K data 
4 
y 
z 
log-k+12 
8851 14.25626 5.63022 0.43136 
-0.
-0.
983-0.981
48 15.16481 5.73326 3.6-0.97913 15.46766 5.76761 0.6
-0.97444 16.07336 5.
-0.97210 16.37621 5.87065 0.51851 
-0.96976 16.67906 5.90500 1.44560 
-0.96741 16.98191 5.93934 1.58320 
-0.96507 17.28476 5.97369 2.21748 
-0.96272 17.58761 6.00804 2.26007 
-0.96038 17.89046 6.04239 1.37107 
-0.96330 14.56905 5.13219 1.20683 
-0.96067 14.87362 5.14378 0.50243 
-0.95804 15.17818 5.15538 1.19312 
-0.95541 15.48275 5.16698 0.16732 
-0.95278 15.78732 5.17858 2.61066 
-0.94751 16.39645 5.20177 2.79518 
-0.94488 16.70102 5.21337 2.74194 
-0.9493
-0.
-0.93435 17.91929 5.25976 0.348301
-0.93172 18.22386 5.27135 3.6053.28295 3.2833
-0.92909 18.52843 5 
0.02100 14.69933 5.11511 3.97818 
0.02100 15.00408 5.12069 0.96942 
0.02099 15.30883 5.12627 0.94694 
0.02098 15.61358 5.13185 0.98588 
0.02097 15.91833 5.13743 -0.38091 
0.02097 16.22307 5.14301 0.27184 
0.02095 16.83257 5.15417 -1.31247 
0.02095 17.13732 5.15975 -0.27984 
0.02094 17.44207 5.16533 1.34242 
0.02093 17.74682 5.17091 1.56348 
0.02092 18.05157 5.17649 -1.31695 
0.02092 18.35632 5.18207 -0.22841 
091 18.66107 5.18765 1.12710 
0.0202 
0. 
0.02089 19.27056 5.19881 -0.07624 
0.02089 19.57531 5.20439 0.39445 
97 0.26007 
0.02088 19.88006 5.20902087 20.18481 5.215 
0. 
0. 
0.
020 
0.020 
0.02084 21.40381 5.23787 -0.67572 
0.02084 21.70855 5.24345 -0.53018 
0.02083 22.01330 5.24903 1.82672 
1 1.83696 
0.02082 22.31805 5.2546 
0.02081 22.62280 5.2601 
0.02081 22.92755 5.2657 
0.02080 23.23230 5.2713 
0.02079 23.53705 5.2769 
0.02079 23.84180 5.2825 
0.02078 24.14655 5.2880 
0.02077 24.45129 5.2936
Figure E-5. Measured 
Conditioning

variables = x y z var 
zon
e 
-0.44
50000E+01 0.2095000E+02 0.2050000E+01 0.685333 
-0.4350000E+01 0.2095000E+02 0.2050000E+01 0.1218344 
-0.4250000E+01 0.2095000E+02 0.2050000E+01 0.714998
... 
0.3250000E+01 0.2285000E+02 0.6950000E+01 0.2682163E+01 
0.3350000E+01 0.2285000E+02 0.6950000E+01 0.3089783E+01 
0.3450000E+01 0.2285000E+02 0.6950000E+01 0.2990684E+01 
Figure E-6. Excerpt from the Generated Permeability Field 
Niche 1620 left batwing 
x y z 
-4.450000E+000 1.455000E+001 0.000000E+000 
-4.350000E+000 1.455000E+001 0.000000E+000 
-4.250000E+000 1.455000E+001 0.000000E+000 
.... 
-2.850000E+000 1.965000E+001 3.026709E+000 
-2.650000E+000 1.965000E+001 2.910188E+000 
-2.550000E+000 1.965000E+001 2.883803E+000 
.... 
-1.650000E+000 2.355000E+001 0.000000E+000 
-1.550000E+000 2.355000E+001 0.000000E+000 
-1.450000E+000 2.355000E+001 0.000000E+000 
Figure E-7. Excerpts from Interpolated Left Slot Profile of Niche 5 
Niche 1620 right batwing data 
x y z 
2.050000E+000 1.455000E+001 0.000000E+000 
2.150000E+000 1.455000E+001 0.000000E+000 
2.250000E+000 1.455000E+001 0.000000E+000 
2.250000E+000 2.005000E+001 2.991148E+000 
2.350000E+000 2.005000E+001 2.904599E+000 
2.450000E+000 2.005000E+001 2.835122E+000 
2.750000E+000 2.355000E+001 0.000000E+000 
2.850000E+000 2.355000E+001 0.000000E+000 
2.950000E+000 2.355000E+001 0.000000E+000 
Figure E-8. Excerpts from Interpolated Right Slot Profile of Nich 

#! /bin/sh 
# 
# Unix shell script file sh.N5BH5_28-29ft_mesh 
# 
Usage: sh.N
# 
# 
# Generates TOUGH2 mesh N5BH5_28-29ft.mes
# uses 
# airK_N5_3.dat 
ceiling_N5_3.dat 
# 
# l
# righ
# 
1
# TA Ghezzehei (Sept 10, 2002), Version
# 
# modified from S. Finsterle, August 6, 1999, Ve
# 
echo 
cript sh.N5B
echo ' Start shell scho ' mesh generator
e
echo '= 
echo
# 
echo 
echo 1. Generate 3d mesh 
echo -------------------- 
ft 9 # general 
tough2 -mesh N5BH5_28-29
# 
echo 
echo 2. Center mesh 
echo -------------- 
esh << eof 
xMoveM
N5BH5_28-29ft.mes 
temp01.mes 
-4.50 
20.90 
7.00 
eof 
# 
echo 
echo 3. Map correlated permeability fiecho ------------------------------------ 

cei
2 
.0 
100
1.0
1.0 # cosine multiplicatio
-2.00 # Xmin 
# Xmax 
2.10 
80 
20.
23.00 
0.0 
4.7 
eof 
# 
eplace all NIC98 and N
# R
# will be NIC98 and NIC99 (TA Ghezzehei June 1
# 
echo 
echo 5.
o -
ech
sed 's/NIC98/NIC78/g' temp04.mes | \ 
/NIC99/NIC79/g' > temp05.mes 
sed 's
# 
echo 
echo 6. Cut out left batwing 
--
echo ------
temp0
temp06.mes 
batwing_N5_3.dat 
left
2 
100.0 
1.0e-10 
1.0 
-4.10 
-1.90 # Xmax 
# Ymin N5_3 
20.80 
0.0 # Zmin 
3.6 # Zmax 
eof 
# 
8 and NIC99 eleme
# Replace all NIC9
# left batwing is now NIC88l be NIC98 and NIC99 
# wil
# 
o 
ech
echo 7. Replace NIC9* by NIC8* 
--- 
echo ----------------------
sed 's/NIC98/NIC88/g' temp06.mes | \ 
's/NIC99/NIC89/g' > temp07.mes 
sed
# 
o 
ech
echo 8. Cut out right batwing 
----------------------- 
echo -
xCutNiche1.2 << eof 
p07.mes 
tem
temp08.mes 
0.0 # niche volume 

emp09.mes # input mesh file 
emp10.mes # output mesh file 
# boundary material type 


sed 's/A5C44A6C44.*/A5C44A6C44 30.0000E-010.5000E-010.1000E-010.1000E+01 /g'| \ 
sed 's/A4A44A5A44.*/A4A44A5A44 30.5000E-010.0000E-010.1000E-110.1000E+01 /g'| \ 
sed 's/A4B44A5B44.*/A4B44A5B44 30.5000E-010.0000E-010.1000E-110.1000E+01 /g'| \ 
sed 's/A4C44A5C44.*/A4C44A5C44 30.5000E-010.0000E-010.1000E-110.1000E+01 /g' \ 
> temp12.mes 
# 
echo 
echo 13. Edit volume of niche elements 
echo --------------------------------- 
sed 's/NICHE........../NICHE0.5000E+52/g' temp12.mes > temp13.mes 
# 
# 
echo 
echo 14. Add evaporation elements 
echo ---------------------------- 
cat temp13.mes | sed -n '1,/BOT99/p' > eleme 
cat temp13.mes | sed -n '/TOP99A21 1/,$p' > conne 
grep NIC temp13.mes | sed 's/NIC/EVP/' | sed 's/NICHE/EVAPP/' > elemeconne 
cat << eof >> eleme 
NE 
p -v EVAPP elemeconne | \ 
ed 's/EVP\(.......\).*0.1000E-090.5000E-01\(..........\).*$/EVP\1 \ 
echo ---------------------------- 
echo 
mv temp14.mes N5BH5_28-29ft.mes 
rm temp*.* 
rm t2.msg 
rm fort* 
rm *airK.dbg 
echo 
echo ===========================

# 
# 
# Unix script to perform multiple inv
# Niche5, Borehole 5 (28-29ft) 
# 
# ceiling_N5_3.dat 
# leftbatwing_N5_3.dat 
rightbatwing_N5_3b.dat 
##
# TA Ghezzehei 09/09/2002 
# Adapated from S. Finsterle, V1.0, 8/20/02 
clear 
# 
ing
echo Copy air-K and Niche ceil
# 
echo 'Generate meshes for multi
5, 28-29ft)' 
echo " " 
i=0 
j=0 
while test $j -lt $runs 
do 
# calculate new seed number 
j=`expr $j + 1` 
i=`expr $j + $j` 
i=`expr $i + 59067` 
echo "Run $j of $runs" 
echo "============================================================================" 
echo "Create
\\\seed/" \ 
> N5BH5_28-29ft_sisim.par$j 
xSisim << eof 
N5BH5_28-29ft_sisim.par$j 
cp 5BH5_28-29ft_airK.d# 
echo `date`: Mesh generation 
sh.N5BH5_28-29ft_mesh 
cp N5BH5_28-29ft.mes N5BH5_28-29ft.mes$j 
# 
# 
echo remove unnecessary files 
echo 
rm N5BH5_28-29ft_sisim.pa
# 
ech sh.N5BH5_28-29ft_r

APPENDI
PREPARATION OF SEEPAGE RATE AND RELATIVE-HUMIDITY DATA FOR THE 


_sep_193259.txt


9. Translate date and time to seconds after February 28, 2001 13:46: 
Col. 2 = (RC[-1]-2/28/2001 13:46)*86400 
10. Save as space delimited text file RH.txt. 
Open RH.txt using text editor vim. 
13. Add first dummy data point (, ) to provide historic relative humidity. 
Add last dummy data point (1E20, 0.252) to cover potential prediction time frame. 
Duplicate all 4141 lines twice. 
Remove second column from Line 2 to Line 4142. 
Remove first column from Line 4143 to end of file. 
19. Add new line EVA98, Line 4143. 
Add new line EVA99, Line 8285. 
1200.0 
2400.0 
......... 
45380.0 
52 
52
46580.0 
5247780.0 
1.0E20 
0.190 
0.190 
0.190 
88 
0.1
..... 
0.275 
0.260 
0.252 
0.252 
99 
EVA
0.190 
90 
0.1
0.190 
0.188 
..... 
0.275 
0.260 
0.252 
2 
0.25
Figure 

INTENTIONALLY LEFT BLANK 

APPENDIX G 
EXECUTION OF MULTIPLE INVERS


Cross-Drift were performed, based on multiple realizations of the underlying permeability field. 
he following steps were performed (
T
1. Go to the appropriate 
number, Y represents the injection zone, 
ed in the same interval).
sequences were perform
2. Select the appropriate mesh c 
a steady-state sim
3. Perform
eff
input to iTO
4.
with evaporation effe
2
2.1310-5 m/s). A dry-out zone develops around the drift. 
e inv
5. Take saturation distribution from previous simulation as initial conditions for th
A representative TOUGH2 input file LAx_zoneY_setZ (as input to iTOUGH2 V
5. 
is shown in Figure G-3; an excerpt from a representative iT
2002 [DIRS 160106]))
V5.0 (LBNL 2002 [DIRS 160106]) input file L
6. Figure 
0106
16
obtained from a single inversion. 
#! /bin/sh 
# 
# sh.run LA ZONE SET [RU
# 
# LA = Boreho
# ZONE = Zone number 
Test set number 
# SET = 
S = N
# RUN
ltiple inversions of seepage data 
# 
cd $HOME/ym/Seepage/ST/LA$1/Zone$2/Set$3 
# 
if test "$4" = "" 
hen 
t 
se 

#