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

MDL-NBS-HS-000004

January 2000

1. PURPOSE 
The purpose of this Analysis/Model Report (AMR) is to document the development of the 
Seepage Calibration Model (SCM) based on available seepage testing data. The SCM is a 
template fracture continuum model that is developed based on air-permeability and liquid-release 
test data from the experiments performed in Niche 3650 of the Exploratory Studies Facility (ESF) 
at Yucca Mountain, Nevada. The SCM provides a methodological and conceptual basis for the 
subsequent development of drift-scale seepage models. 
Specific objectives of this work activity are to evaluate inflow rates into drifts and seepage 
thresholds based on seepage testing data from the niche studies. The potential of an underground 
opening to act as a capillary barrier, which limits or precludes seepage of water into drifts, is also 
evaluated. Inverse modeling is used to calibrate the SCM and to estimate seepage-relevant, 
model-related parameters. The accuracy of the derived parameters is estimated based on the 
goodness-of-fit to the observed data and the sensitivity of calculated seepage with respect to the 
parameters of interest. The SCM is validated against liquid-release test data that were not used for 
the calibration of the model. Linear uncertainty propagation analyses and Monte Carlo 
simulations are performed to evaluate prediction uncertainty. 
The scope of this AMR is limited to an analysis of liquid-release test data from Niche 3650. The 
parameters estimated and seepage percentages predicted by the SCM are therefore only 
applicable to an uncollapsed excavation with the geometry of Niche 3650 under ambient 
temperature conditions. Moreover, these parameters are restricted to the middle nonlithophysal 
zone of the Topopah Spring Tuff at Yucca Mountain. 
This AMR is written in accordance with the Development Plan (CRWMS M&O 1999c) in 
support of the following project activities: 
 Performance Assessment (PA) 
 Seepage Model for PA 
 Drift-Scale Coupled Processes Models 
 Abstraction of Drift Seepage and Drift-Scale Coupled Processes 
 Process Model Report (PMR) for the 3D site-scale unsaturated zone flow and transport 
model of Yucca Mountain, Nevada (UZ Model)



2. QUALITY ASSURANCE 
This AMR was developed in accordance with AP-3.10Q, Analyses and Models. Other applicable 
Department of Energy (DOE) Office of Civilian Radioactive Waste Management (OCRWM) 
Administrative Procedures (APs) and YMP-LBNL Quality Implementing Procedures (QIPs) are 
identified in the AMR Development Plan (CRWMS M&O 1999c). 
The activities documented in this AMR were evaluated with other related activities in accordance 
with QAP-2-0, Conduct of Activities, and were determined to be subject to the requirements of the 
U.S. DOE Office of Civilian Radioactive Waste Management (OCRWM) Quality Assurance 
Requirements and Description (QARD) (DOE 1998). This evaluation is documented in CRWMS 
1999a, b; and Wemheuer 1999 (Activity Evaluation for Work Package WP 1401213UM1). 
Scientific Notebooks (SN) used for the modeling activities described in this AMR are listed in 
Table 1. 
Table 1. Scientific Notebooks 
Scientific Notebook Page numbers Owner YMP M&O SNR Accession number 
YMP-LBNL-SAF-1 1150 S. Finsterle SN-LBNL-SCI-087-V1 MOL.19990723.0301 
YMP-LBNL-SAF-2 127 S. Finsterle SN-LBNL-SCI-171-V1 MOL.19990812.0357 
YMP-LBNL-RCT-DSM-1 137 R.C. Trautz SN-LBNL-SCI-157-V1 MOL.19990923.0302

 
 14 


 
 15 
3. COMPUTER SOFTWARE AND MODEL USAGE 
The software codes and routines listed in Table 2 were used in support of this AMR. The software 
codes iTOUGH2, SISIM, GAMV2, and EXT are obtained from software configuration 
management in accordance with AP-SI.1Q, Software Management. The software is appropriate 
for the intended application, and was used only within the range of validation in accordance with 
applicable software procedures. The software routines are documented in the records listed by 
accession number in Table 2. 
All computer codes listed in Table 2 were installed on hydra, a Sun SPARC multiprocessor 
workstation running under Unix operating system Solaris. Installation is documented in 
YMP-LBNL-SAF-1, p. 5. The QA status of the software codes and software routines listed in 
Table 2 is indicated on the Document Input Reference Sheet (DIRS), Attachment I. 
The software iTOUGH2 (iTOUGH2, V4.0, 10003-4.0-00) provides forward and inverse 
modeling capabilities for unsaturated and multiphase flow in fractured-porous media, and is used 
in this AMR for calibration, validation, and prediction runs (Sections 5.3, 6.36.6). The GSLIB 
module SISIM (SISIM, V1.203, 10001-1.0MSISIMV1.203-00) generates three-dimensional, 
spatially correlated random fields based on sequential indicator simulations, and is used in this 
AMR to generate permeability fields (Sections 5.3.2, 6.3.2). The GSLIB module GAMV2 
(GAMV2, V1.201, 10087-1.0MGAMV2V1.201-00) analyzes spatial correlation of twodimensional, 
irregularly spaced datasets, and is used in this AMR for the analysis of postexcavation 
air-permeability data (Section 6.2.3). The software EXT (EXT, V1.0, 10047-1.0-00) 
is used to extract data from iTOUGH2 output files for visualization with Tecplot V7.0-8-0; 
Tecplot visualizations are shown in Sections 5.3 and 6. The routine MoveMesh (MoveMesh, 
V1.0, ACC: MOL.19990721.0552) moves coordinates of a mesh file (Section 5.3, 6.3). The 
routine AddBound (AddBound, V1.0, ACC: MOL.19990721.0553) adds boundary elements to a 
mesh file (Sections 5.3, 6.3). The routine Perm2Mesh (Perm2Mesh, V1.0, ACC: 
Table 2. Code Names, Versions, Traceability 
Software Name Version 
Software Tracking Number or 
Accession Number 
iTOUGH2 4.0 10003-4.0-00 
GSLIB Module SISIM 1.203 10001-1.0MSISIMV1.203-00 
GSLIB Module 
GAMV2 
1.201 10087-1.0MGAMV2V1.201-00 
EXT 1.0 10047-1.0-00 
Routines: 
MoveMesh 1.0 ACC: MOL.19990721.0552 
AddBound 1.0 ACC: MOL.19990721.0553 
Perm2Mesh 1.0 ACC: MOL.19990721.0554 
DelMatrix 1.0 ACC: MOL.19990721.0555 
Eos9Eos3 1.0 ACC: MOL.19990721.0556 
CutNiche 1.1 ACC: MOL.19990721.0557 
userobs 1.01 ACC: MOL.19990721.0558

 
 16 
MOL.19990721.0554) maps the permeability field generated using SISIM onto a mesh file 
(Sections 5.3, 6.3). The routine DelMatrix (DelMatrix, V1.0, ACC: MOL.19990721.0555) 
reassigns permeabilities to low-permeability matrix elements (Section 5.3). The routine Eos9Eos3 
(Eos9Eos3, V1.0, ACC: MOL.19990721.0556) reformats an unsaturated flow output file SAVE 
to a two-phase flow initial condition file INCON (Section 5.3). The routine CutNiche (CutNiche, 
V1.1, ACC: MOL.19990721.0557) cuts a niche from the mesh file (Sections 5.3, 6.3). The 
routine userobs (userobs, V1.01, ACC: MOL.19990721.0558) provides a user-specified 
observation type needed to estimate semivariogram parameters (Section 6.3.2). 
In addition to the above, commercially available standard spreadsheet and visual display graphics 
programs that are exempt from software quality assurance requirements were used. EXCEL 97- 
SR-1 was used for spreadsheet calculations and calculations of basic statistics. Tecplot V7.0-8-0 
was used for plotting and visualization of analysis results in figures shown in this report. 

 
 17 
4. INPUTS 
4.1 DATA AND PARAMETERS 
The input parameters and data needed for the development of the Seepage Calibration Model are 
obtained from the TDMS. Specific input data sets and associated Data Tracking Numbers (DTNs) 
are summarized in Table 3. The QA status of the inputs listed in Table 3 is indicated on the DIRS, 
Attachment I. 
Reports documenting past experimental and numerical work related to drift seepage at Yucca 
Mountain as well as other pertinent documents are listed in Section 8.5, Supporting Bibliography. 
This bibliography is for information only, and this AMR does not directly rely on any of the listed 
documents 
4.2 CRITERIA 
At this time, no specific criteria (e.g., System Description Documents) have been identified as 
applying to this analysis and modeling activity in project requirements documents. However, this 
AMR provides information required in specific subparts of the proposed U.S. Nuclear Regulatory 
Commission rule 10 CFR 63 (see Federal Register for February 22, 1999, 64 FR 8640). It 
supports the site characterization of Yucca Mountain (Subpart B, Section 15), the compilation of 
information regarding the hydrology of the site in support of the License Application (Subpart B, 
Section 21(c)(1) (ii)), and the definition of hydrologic parameters used in performance assessment 
(Subpart E, Section 114(1)). 
The DOE interim guidance (Dyer 1999), requiring the use of specified subparts of the proposed 
NRC high-level waste rule, 10 CFR Part 63 (64 FR 8640), was released after completion of the 
work documented in this AMR; it has no impact on this work activity. 
4.3 CODES AND STANDARDS 
No specific, formally established standards have been identified as applying to this analysis and 
modeling activity. 
Table 3. DTNs of Inputs Used for the Development of the Seepage Calibration Model 
Data Description DTN 
Air-permeability data from air-injection testing in Niche 3650 LB980001233124.002 
Liquid-release test data from Niche 3650 LB980001233124.003 
Base-case hydrologic parameter set LB997141233129.001

 
 18 


 
 19 
5. ASSUMPTIONS 
5.1 INTRODUCTION 
In this section, we discuss the basic assumptions of the Seepage Calibration Model (SCM). The 
section is structured as follows: 
Section 5.2 contains a list of assumptions. Each statement of an assumption is immediately 
followed by the rationale as to why the assumption is considered valid or reasonable. The 
justification for making the continuum assumption (Assumption No. 1) required a more extensive 
study, which is presented in Section 5.3. This synthetic modeling study confirms that the 
continuum assumption is valid. At the same time, it outlines the approach and provides 
guidelines for deriving values of key parameters affecting seepage. This approach is followed in 
Section 6 where the SCM is developed and actual liquid-release test data are analyzed. 
5.2 GENERAL MODELING ASSUMPTIONS 
The basic assumptions of the SCM must be consistent with those of the UZ Flow and Transport 
Model and the Seepage Model for PA, because the concepts and parameters derived with the 
SCM are only valid and useful for subsequent seepage calculations if they refer to similar 
conceptual models. 
The assumptions for the SCM are stated below, followed by the basis or rationale for using them. 
1. Assumption: It is assumed that the continuum approach is a valid concept to calculate 
percolation flux and drift seepage at Yucca Mountain. Rationale: The rationale for this 
assumption is presented in Section 5.3. 
2. Assumption: Adopting the continuum approach, water flow under unsaturated conditions 
is assumed to be governed by Richards equation (Richards 1931, pp. 218233). 
Rationale: This general concept is believed reasonable for unsaturated water flow 
through both porous matrix and fractures. Richards equation simply states that water 
flows under the combined effect of gravitational and capillary forces, and that flow 
resistance is a function of saturation. 
3. Assumption: Permeabilities determined from air-injection tests are assumed to be 
representative of the hydraulic conductivity of the formation. Rationale: The assumption 
is believed to hold for the purposes of the current application, where air-permeability 
estimates are only used to condition the generation of a spatially correlated, random 
permeability field. Potential inaccuracies in this assumption are compensated for through 
the estimation of the van Genuchten 1/ a parameter (Luckner et al. 1989, pp. 21912192), 
which is correlated to permeability. 
4. 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 (Luckner et al. 1989, pp. 21912192) as implemented in 
the iTOUGH2 code (Finsterle 1997, p. 224). Rationale: The van Genuchten-Mualem

 
 20 
model is the standard model used in the suite of UZ flow and transport models; it was 
chosen here for consistency. Furthermore, the applicability of relative permeability and 
capillary pressure functions is consistent with the continuum assumption and seems 
appropriate also for fractures, which are likely to be rough and/or partially filled with 
porous material. 
5. Assumption: For heterogeneous models, the van Genuchten parameter 1/ a is assumed to 
be correlated to absolute permeability according to the Leverett scaling rule (Leverett 
1941, p. 159; Finsterle 1997, p. 224). Rationale: Continuum permeability is partly 
related to fracture aperture. An increase in permeability is therefore associated with a 
decrease in capillary strength. 
6. Assumption: Water removal from the formation and the capture system by vapor 
diffusion and evaporation is assumed to be small. Rationale and discussion: Under 
isothermal conditions, potential evaporation is small compared to the amount of water 
being released and given the relatively short time period of the experiments. This 
assumption may not be valid for injection tests performed at lower rates. In these cases, 
the amount of water collected in the capture system does not accurately reflect the 
seepage percentage as a result of evaporation. No relative humidity or evaporation-rate 
measurements are available from Niche 3650, and thus no correction has been made to 
the seepage-percentage data. (Seepage percentage is defined as the mass of water that 
dripped into the capture system divided by the mass of water released into the borehole 
interval during a liquid-release test event.) It is important to realize that prescribing a 
100% relative humidity boundary condition in a seepage prediction model is a 
conservative assumption. While such a model underestimates vapor flow, it yields the 
maximum liquid-phase influx, which is defined here as drift seepage. The 
underestimation of vapor flow is irrelevant, since the assumption of 100% relative 
humidity already implies that the moisture content in the drift environment is at its 
maximum. However, the assumption is not conservative if used in an inverse model 
because parameters are adjusted such that the model reproduces the observed seepage 
percentage, which is likely to be too small. We suggest to address this issue in future 
experiments by monitoring relative humidity in the niche and directly measuring 
evaporation rates. 
7. Assumption: Background percolation flux is assumed to be constant at 3 mm/year during 
the course of the experiment. Rationale: The background percolation flux is appropriate 
and of little impact on the simulation of liquid-release tests, which involve much higher 
rates. 
8. Assumption: Matrix imbibition is assumed to be small and is thus not explicitly modeled 
in the SCM. Rationale: Matrix permeability is low, and matrix imbibition is reflected in 
the effective porosity estimated by inverse modeling. Porosity estimates are irrelevant in 
the subsequent simulations of seepage under natural flow conditions, which are near 
steady state. 
All these assumptions are used throughout this report. They do not require further confirmation.

 
 21 
5.3 FRACTURE-CONTINUUM ASSUMPTION 
5.3.1 Objective 
We performed a synthetic modeling study to examine the appropriateness of using a Fracture 
Continuum Model (FCM), i.e., a model that is based on the continuum assumption, for predictive 
seepage calculations. Seepage into underground openings is dominated by water accumulation in 
a boundary layer at the apex of the opening and subsequent dripping from discrete fractures. 
Seepage threshold is determined by the capability of individual fractures to hold water by 
capillary forces and by the permeability and connectivity of the fracture network, which allows 
water to be diverted around the drift. 
Simulation studies of unsaturated flow and transport at Yucca Mountain are usually based on the 
continuum approach, in which fractures and matrix are modeled as continua for which averaged 
properties are provided. The continuum approach has been questioned to be suitable for 
describing certain flow phenomena at Yucca Mountain (see, for example, Pruess (1999)), and 
specifically for predicting drift seepage, which apparently exhibits strong discrete effects. 
In this section, we examine the appropriateness of using a heterogeneous fracture-continuum 
model as the basis for drift-scale seepage calculations. We consider the continuum approach 
appropriate for seepage studies if it is capable of predicting seepage threshold and seepage 
percentages for a drift in a fractured formation. On account of the simplifying assumptions and 
the limited amount of characterization data typically available, there will be differences between 
the seepage rates predicted by the FCM and the actual seepage rates. Therefore, the uncertainty of 
the model prediction will be considered. The seepage rates under natural flow conditions are 
unknown and can currently not be observed in the field. They are thus replaced in this study with 
synthetically generated seepage rates calculated using a model that exhibits discrete flow and 
seepage effects. This model is referred to as the Discrete Feature Model (DFM). 
We do not attempt to compare the performance of an FCM with that of a discrete fracture network 
model, which is often cited as a valid alternative to the continuum model. Such a comparison 
would have to include a detailed discussion of data needs for each of the models, the respective 
uncertainties in these characterization data, and conceptual uncertainties and their impact on 
model predictions. Moreover, a computer code capable of accurately simulating unsaturated flow 
and seepage based on a discrete fracture formulation is currently not available. 
The approach used in this synthetic study involves the following steps: 
1. A model with a complex network of high-permeability features embedded in a lowpermeability 
matrix is developed. The only purpose of this model is to produce discrete 
flow behavior and localized seepage events. Unsaturated-flow simulations performed 
with this discrete-feature model provide synthetic data against which the simplified 
Fracture Continuum Model (FCM) will be tested. 
2. Air-injection tests, liquid-release experiments, and seepage under natural flow conditions 
are simulated using the DFM to generate synthetic characterization, calibration, and 
validation data, respectively.

 
 22 
3. A simplified heterogeneous FCM is developed based on the available synthetic 
characterization data. 
4. Effective parameters are determined by calibrating the FCM against the synthetic liquidrelease 
test data. 
5. Predictions of seepage threshold and additional liquid-release tests are made with the 
FCM and compared to results obtained with the DFM for model validation. 
It is important to notice that this is a synthetic modeling study designed for the specific purpose 
outlined above. While loosely based on expected conditions encountered during the liquid-release 
tests performed in the ESF at Yucca Mountain, the parameters used to develop the DFM should 
not be interpreted as describing actual formation characteristics at Yucca Mountain. 
The study described here is documented in detail in Scientific Notebook YMP-LBNL-SAF-1, 
pp. 3086, 120139. 
5.3.2 Development of Discrete Feature Model (DFM) 
A high-resolution, two-dimensional (2D), vertical model was developed, representing an 
excavated niche in a fractured-porous formation. The model domain of dimensions 6  7 m in 
horizontal and vertical direction, respectively, was discretized into regular, square gridblocks with 
a side length of 0.05 m (SN YMP-LBNL-SAF-1, p. 36); the depth of the model is 1 m. The model 
was selected to be 2D instead of three-dimensional (3D) because effects of discrete fractures 
intersecting the drift are expected to be stronger in 2D than in 3D, where more opportunities for 
flow diversion around the opening exist. This was confirmed by 3D fracture-network simulations, 
which yielded lower seepage rates and a more porous-medium-type flow field 
(SN YMP-LBNL-SAF-2, p. 9). It is important to recall that the purpose of this study is to create a 
system behavior that is strongly affected by the discreteness of the fracture network. 
In order to generate a complex network of discrete, heterogeneous fractures, a series of four 
permeability fields generated using sequential indicator simulation (Deutsch and Journel 1992, 
p. 151) were overlapped. The individual fracture sets of orientation 0, 45, 90, and 135 were 
created by drawing log-permeability values from discretized bi- and multi-modal cumulative 
distribution functions (cdf) that yield a high probability for very low values (10-17 m2 or smaller, 
representing the matrix), a very low probability for intermediate values, and a relatively high 
probability for very high values (representing the fractures). Each indicator cutoff representing a 
point of the cdf was assigned its own semivariogram with a very high anisotropy ratio to generate 
elongated high-permeable features, which were cross-cut with occasional low-permeability 
obstacles. The resulting permeability field, shown in Figure 1a, is very complex with 
heterogeneous fractures of variable angle, length, and permeability. The permeability field shown 
in Figure 1a cannot be described using simple geostatistical parameters. The complexity and 
relative inaccessibility of the fracture-network characteristics are intentional to mimic a realistic 
situation where complete information about the geometry and hydrologic properties of a fracture 
network is not available. The process of fracture-network generation is described in detail in SN 
YMP-LBNL-SAF-1, pp. 3135. 

 
 23 
The effective vertical permeability of the fracture network was determined by applying constantpressure 
conditions at the top and bottom boundaries of the model, running a transient flow 
simulation to steady state, and using Darcy's law to back out an effective permeability from the 
simulated steady-state flow rate (SN YMP-LBNL-SAF-1, p. 47). The effective permeability of 
approximately 410-12 m2 is consistent with fracture permeabilities observed at Yucca Mountain; 
note, however, that this information is considered unknown and will not be used in the subsequent 
analysis. 
The permeability field was mapped onto the computational grid, and an opening (niche) was cut 
out from the mesh (SN YMP-LBNL-SAF-1, pp. 3739). During this process, the fracture 
permeabilities in the vicinity of the niche were linearly increased within a 1-m-thick skin zone as 
a function of distance from the niche wall, reaching a factor of 100 at the niche wall. This 
permeability increase was introduced to mimic the presence of an excavation-disturbed zone, 
which is believed to exist around the niches in the ESF (Wang and Elsworth 1999, pp. 751757). 
It was assumed that the mechanical disturbance mainly affects fractures, i.e., matrix permeability 
was left unchanged at its original low value. After attaching boundary elements, the final mesh, 
shown in Figure 1b, consisted of 11,984 elements and 24,005 connections between them. 
NOTE: The model is used to generate synthetic seepage data. 
Figure 1. Permeability Field of the Discrete Feature Model (a) Before and (b) After Niche 
Insertion 
Capillary pressure and relative liquid permeability are described by van Genuchtens function 
(Luckner et al. 1989, pp. 21912192), with the capillary strength parameter 1/ a correlated to the 
heterogeneous permeability field shown in Figure 1 according to Leveretts scaling rule (Leverett 
1941, p.159; Finsterle 1997, p. 224). The niche is modeled as a domain of zero capillarity, 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
-9.50 
-9.75 
-10.00 
-10.25 
-10.50 
-10.75 
-11.00 
-11.25 
-11.50 
-11.75 
-12.00 
-18.00 
log(k [m2]) 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
-9.50 
-9.75 
-10.00 
-10.25 
-10.50 
-10.75 
-11.00 
-11.25 
-11.50 
-11.75 
-12.00 
-18.00 
log(k [m2]) 
(a) (b) 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 24 
representing 100% relative humidity. A free-drainage boundary condition (Finsterle 1998, 
pp. 14-15) is applied at the bottom of the model at Z=0.0 m to avoid an unphysical capillary 
pressure end effect. A constant percolation flux of 3 mm/year is applied at the top of the model. 
The reference value of 1/a (sometimes referred to as air-entry pressure, AEP) for the reference 
permeability of 10-12 m2 was varied, until no seepage occurred under natural, steady-state flow 
conditions for a percolation flux of 3 mm/year (SN YMP-LBNL-SAF-1, p. 73). The percolation 
flux was then increased to 300 mm/year, yielding a seepage percentage of 80% 
(SN YMP-LBNL-SAF-1, p. 74). From that, it can be concluded that with a reference 1/ a value of 
5,000 Pa and a reference permeability of 10-12 m2, a seepage threshold exists somewhere between 
3 and 300 mm/year. The actual seepage threshold was determined to be 15 mm/year (see Section 
5.3.7). 
Figure 2 shows the simulated steady-state flow field and saturation distribution for a percolation 
flux of 3 mm/year. A moderate saturation build-up in the skin zone is sufficient to divert all the 
water around the niche; this constitutes the so-called capillary barrier effect. Figure 3 shows the 
flux distribution across the top and bottom of the model. The constant flux, which is applied 
uniformly at the top of the model, is immediately redistributed into high-conductivity fractures. 
Flow diversion around the niche leads to a percolation shadow as well as locally increased fluxes 
below the niche. 
Figure 2. Simulated Steady-State Flow Field (a) and Saturation Distribution (b) 
(a) (b) 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
1.00 
0.95 
0.90 
0.85 
0.80 
0.75 
0.70 
0.65 
0.60 
0.55 
0.50 
0.45 
0.40 
0.35 
0.30 
0.25 
0.20 
0.15 
0.10 
0.05 
0.00 
Saturation Percolation Flux: 3 mm/year 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9
6
3 
Flux [mm/year] 
Percolation Flux: 3 mm/year 
Total Seepage: 0.0 mm/year 
AEPref = 5,000 Pa 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 25 
Figure 3. Simulated Steady-State Fluxes Across Top and Bottom of Model Domain, 
Showing Flow Redistribution as a Result of the Capillary Barrier Effect 
5.3.3 Simulation of Air-Injection Tests Using the DFM 
Air-injection tests are part of a standard characterization study performed at Yucca Mountain. We 
therefore simulated air-injection tests in three boreholes and analyzed the steady-state pressure 
data based on a commonly used analytical solution (LeCain 1995, p. 10). The derived air 
permeabilities are assumed to be the only data available for the development of a drift-scale 
seepage model, which will then be calibrated against liquid-release test data. 
Air-injection tests were simulated in three boreholes above the niche (shown, for example, in 
Figure 1b); they are designated L, M, and R, according to their location to the left, middle, and 
right of the niche crown, respectively. Air is injected at a constant rate of 0.001 kg/s for 120 
seconds, followed by a 120-s recovery period. Gas pressure in the niche as well as the top and 
bottom boundary were kept constant at 1 bar. Figure 4 shows the almost instantaneous pressure 
build-up in the injection holes as well as the response in the respective observation holes. The 
pressure at the end of each injection period is taken as the steady-state pressure for the 
determination of air permeability. A steady-state overpressure of 1430, 1350, and 1890 Pa was 
obtained for the three boreholes, leading to permeability estimates of 6.910-12, 7.410-12, and 
5.210-12 m2, respectively. No high accuracy is required for these estimates; they will only be used 
to condition the heterogeneous permeability field of the FCM. Details about the simulation of 
air-injection tests using the DFM can be found in SN YMP-LBNL-SAF-1, pp. 5154, 75, 83. 
-3 -2 -1 0 1 2 3 
X [m] 
-30 
-20 
-10
0 
10 
20 
30 
Flux [mm/year] 
Flux leaving bottom of model 
Niche 
Flux entering top of model 
Average Flux: 3 mm/year 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 26 
Figure 4. Pressure Response from Simulated Air-Injection Tests Performed in Three 
Boreholes (Borehole L: Solid Line; Borehole M: Dashed Line; Borehole R: Dash-Dotted 
Line) Above the Niche 
5.3.4 Simulation of Liquid-Release Tests Using the DFM 
Three liquid-release tests were simulated from Borehole M. In each event, 3 kg of water were 
injected at a specified rate. Sufficient time was allowed between individual test events to make 
sure that seepage into the drift ceases. Nevertheless, subsequent events are likely to exhibit a 
memory effect, i.e., seepage percentage is expected to increase due to a higher initial saturation in 
the system as a result of water storage from the previous test. The third event is performed at a 
lower rate to investigate the dependence of seepage on flux. Table 4 summarizes information 
about the simulated liquid-release tests. The simulations are described in detail in 
SN YMP-LBNL-SAF-1, pp. 5558, 7679, 8486. 
Table 4. Simulated Liquid-Release Tests 
Event 
Begin Injection 
(s) 
End Injection 
(s) 
Injection Rate 
(kg/s) 
Water Released 
(kg) 
1 0.0 600.0 0.005 3.0 
2 172800.0 173400.0 0.005 3.0 
3 345600.0 351600.0 0.0005 3.0 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
100 200 300 400 500 600 
Time [sec] 
100000 
100250 
100500 
100750 
101000 
101250 
101500 
101750 
102000 
Pressure [Pa] 
Borehole L Borehole M 
Borehole R 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 27 
The flow field and saturation distribution after the first test event are visualized in Figure 5, 
revealing both partial flow diversion around the niche and the saturation build-up at the crown, 
which eventually breaks the capillary barrier, leading to seepage. 
Figure 5. Flow Field (a) and Saturation Distribution (b) After First Liquid-Release Test 
Event Simulated with DFM 
The cumulative amount of water seeping into the niche is recorded as a function of time, 
separately for each event. The resulting curve of cumulative seepage is shown in Figure 6, which 
will be the synthetic data used for subsequent calibration of the FCM. Seepage generally stops 
within a few hours after injection begins. A relatively strong memory effect can be observed for 
Events 2 and 3, leading to increased seepage (Event 2) despite a reduced injection rate (Event 3). 
The ratio of the total amount seeped into the niche divided by the amount of water released 
(3.0 kg) is termed seepage percentage. The seepage percentages of the three simulated 
liquid-release tests are 58, 89, and 89%, respectively. 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9
6
3 
Flux [mm/year] 
Liquid Release Test, Event 1 
Seepage Percentage: 58% 
AEPref = 5,000 Pa 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
0.50 
0.45 
0.40 
0.35 
0.30 
0.25 
0.20 
0.15 
0.10 
0.05 
0.00 
Saturation 
Liquid Release Test, Event 1 
Seepage Percentage: 58% 
AEPref = 5,000 Pa 
(a) (b) 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 28 
Figure 6. Cumulative Seepage as a Function of Time During Three Simulated Liquid- 
Release Tests Using the Discrete Feature Model 
5.3.5 Development of Fracture Continuum Model (FCM) 
The model described in Sections 5.3.25.3.4 serves as a hypothetical natural system, in which 
air-injection and liquid-release tests were performed. It will also be used later to provide synthetic 
validation data. 
In this section, we describe the development of a simplified seepage model that is based on the 
fracture-continuum approach. The model will be calibrated against synthetic data from 
liquid-release tests, and predictive simulations of additional liquid-release tests will be performed 
for validation. Moreover, it will be used to determine the seepage threshold under natural flow 
conditions. 
While the characteristics of the fractured system as represented by the DFM are exactly known, 
only a limited amount of information is assumed to be available for the development of the FCM. 
The available data include: 
 Air-permeability values at the three boreholes L, M, and R (see Section 5.3.3). 
 An assumed variance and isotropic correlation structure of the permeability field. 
 Cumulative-seepage data from three sequential liquid-release tests (see Figure 6). 
0 24 48 72 96 120 144 
Time [h] 
0 
0.5
1 
1.5
2 
2.5
3 
Cumulative Seepage [kg] 
1 
0.005 
2 
0.005 
3 
0.0005 
Event 
Rate [kg/s] 
Mass injected: 
3 kg/Event 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 29 
Furthermore, the two-dimensionality of the flow field is assumed to be recognized. Finally, the 
FCM developed here shares some of the simplifying assumptions that apply to the DFM, such as 
the absence of evaporation effects. 
It is important to realize that essentially no information about the fracture network (i.e., fracture 
density, orientation, aperture distribution, and hydrologic properties) are assumed to be known, 
and that the purpose of the model is simply to predict seepage percentage and seepage threshold 
under natural flow conditions. 
The development of the FCM can be described as follows: 
The FCM is a 2D vertical model, discretized into gridblocks of size 0.1  0.1 m; a niche is cut out 
from the model. The mesh consists of 3,002 elements and 6,020 connections between them. A 
heterogeneous permeability field was created using sequential indicator simulation (Deutsch and 
Journel 1992, p. 151). An arbitrary, uni-modal cumulative distribution function was defined by 
specifying three discrete cutoff values of 30, 50, and 70% at log-permeability values of -13.0, 
-12.0, and -11.0, respectively. A spherical semivariogram with a correlation length of 1.0 m, a 
nugget effect of 0.1, and a sill value of 1.0 was used to generate values ranging over 4 orders of 
magnitude. While a correlation length of 1.0 m can be considered reasonableit is of the same 
order as a potential average fracture lengththe choice of the semivariogram function and its 
parameters was arbitrary, i.e., it was purposely not based on a geostatistical analysis of the 
permeability field shown in Figure 1b. Similarly, the chosen variance of 1.0 is arbitrary but can be 
considered reasonable. This approach reflects the assumption that no hard data about the fracture 
network or its hydraulic properties are available. The only information considered available are 
the three air-permeability values in boreholes L, M, and R (see Section 5.3.3); the permeability 
field was conditioned on these three values. The log-permeability field of the FCM is shown in 
Figure 7. Mesh generation and mapping of the spatially correlated permeability field are 
described in SN YMP-LBNL-SAF-1, pp. 6167, 75. 
Figure 7. Log-Permeability Field of Fracture Continuum Model 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
-9.50 
-9.75 
-10.00 
-10.25 
-10.50 
-10.75 
-11.00 
-11.25 
-11.50 
-11.75 
-12.00 
-12.25 
-12.50 
-12.75 
-13.00 
-13.25 
-13.50 
log(k [m2])

 
 30 
It should be noted that the structure of the FCM described here is by design significantly different 
from that of the DFM presented in Section 5.3.2. While heterogeneous in nature, the FCM lacks 
the discrete features, the high resolution, and the low-permeability matrix. Moreover, no 
excavation-disturbed zone with increased permeability was introduced in the FCM. Since the 
liquid-release tests were performed within this zone, the estimated parameters will reflect skinzone 
properties rather than undisturbed formation characteristics. Using skin-zone parameters for 
seepage calculations is appropriate because seepage is determined by the properties in the 
immediate vicinity of the drift wall. Provided that skin-zone properties are used and that the 
transition between the undisturbed and the excavation-disturbed zone is gradual, no strong impact 
of the skin zone on seepage is expected. The smooth transition zone implemented in the DFM 
does not have an visible effect on the flow pattern (see Figure 2a). If properties change abruptly at 
the skin-zone boundary, however, a partial capillary barrier is formed within the formation. The 
presence of such a second capillary barrier is likely to further reduce seepage. 
In summary, the FCM is an abstracted, highly simplified representation of the fractured-porous 
system to be studied. It is obvious that such a simplified model cannot accurately reproduce the 
detailed behavior of the discrete fracture network. It remains to be examined, however, whether 
the FCM is capable of predicting seepage rates over a large range of percolation fluxes. If so, it 
fulfills its declared purpose and can be considered acceptable for seepage studies. If not, an 
alternative conceptual model must be developed, and its appropriateness must be demonstrated in 
a study similar to that described here. Note that such an alternative model may require different or 
additional characterization data. 
5.3.6 Calibration of FCM Against Synthetic Seepage Data 
The FCM can only be successful in predicting seepage if effective parameters can be estimated 
based on data that reveal seepage-relevant processes. Liquid-release tests are expected to provide 
this information. The FCM is calibrated against the cumulative-seepage data that were generated 
with the DFM. Only late-time data are used for calibration, since the early-time transient behavior 
exhibits distinct effects from the detailed characteristics of the discrete-fracture network. Because 
the FCM is not expected to be able to match these early-time data, they were excluded from the 
inversion to reduce the potential bias in the estimates. 
In order to avoid overparameterization of the inverse problem, only two parameters were 
estimated, namely (1) the logarithm of the reference air-entry pressure 1/ a, and (2) the logarithm 
of porosity f. The van Genuchten parameter 1/ a is included because seepage is expected to 
depend on the capillary strength, i.e., the effectiveness of the capillary barrier. Porosity is included 
as a parameter to be estimated because only a finite, relatively small amount of water is released. 
Porosity is here an effective parameter that accounts for water storage in the fracture system, 
potential matrix imbibition, and other transient effects. 
The calibration was performed using iTOUGH2. Since changes in the 1/ a values during the 
course of the inversion affect the initial saturation distribution, a steady-state run with percolation 
of 3 mm/year precedes each transient liquid-release test simulation. The misfit between the 
cumulative seepage calculated by the FCM and the data provided by the DFM is evaluated using 
the least-squares objective function (Finsterle 1999, p. 33). The objective function is minimized 
using the Levenberg-Marquardt algorithm (Finsterle 1999, pp. 4445).

 
 31 
Figure 8 shows the match. The synthetic data are represented by symbols; calibration occurs only 
against the late-time data, shown as squares. The cumulative seepage predicted with the FCM and 
an initial parameter guess of log(1/ a) = 3.0 and log( f) = -2.0 is shown as a dashed line. The 
best-fit parameter set (1.60/-2.24) is determined after nine Levenberg-Marquardt iterations. The 
corresponding prediction, which matches the data reasonably well, is shown as a solid line. The 
flow field and saturation distribution after the first liquid-release event, calculated with the FCM 
and the best-estimate parameter set, are shown in Figure 9, which can be compared to Figure 5. 
While the details of the flow pattern are different from that of the DFM, the FCM clearly shows a 
saturation build-up above the crown, leading to a qualitatively similar capillary barrier effect. 
Details about the inversion can be found in SN YMP-LBNL-SAF-1, pp. 120124. 
Figure 8. Calibration of FCM Against Late-Time Cumulative-Seepage Data From Three 
Simulated Liquid-Release Events 
0 24 48 72 96 120 144 
Time [h] 
0 
0.5
1 
1.5
2 
2.5
3 
Cumulative Seepage [kg] 
Symbols: Synthetic data from DFM 
Squares: Calibration points 
Dashed: FCM, initial guess 
Solid: FCM, best fit 
Calibration of FCM 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 32 
Figure 9. Flow Field (a) and Saturation Distribution (b) After First Liquid-Release Event 
Simulated with Calibrated FCM 
Next, we discuss the estimated parameters. Since the matrix is not explicitly represented in the 
FCM, porosity is considered an effective parameter that accounts for water storage in the fracture 
system as well as matrix imbibition. The total porosity of the excavation-disturbed zone as 
specified in the DFM is 0.02; about 64% of the pore space is assigned to matrix gridblocks, 
resulting in a fracture porosity of approximately 0.02  (1 - 0.64) = 0.007. The porosity estimated 
by calibrating the FCM is 0.006, a value very close to the geometric fracture porosity specified in 
the DFM. A value larger than the fracture porosity would only be expected if matrix imbibition 
were a significant process and if all the fractures were available for storage. Apparently, matrix 
permeability is too small to allow substantial water imbibition during the relatively short testing 
period. As a result, the porosity estimate reflects the fracture pore space encountered by the 
injected water from the release point to the drift ceiling. While the porosity estimate is of no 
importance for steady-state seepage-threshold predictions, the transient nature of the liquidrelease 
tests forced us to estimate porosity concurrently with the van Genuchten parameter 1/ a, to 
which it is (negatively) correlated. Had it been fixed at a value that is too high (too low), the van 
Genuchten parameter would have been underestimated (overestimated). 
An important result of this study is the finding that a relatively small value 1/ a is required to 
match the seepage data. While the reference value used in the DFM is 5,000 Pa, the estimate for 
the FCM is approximately two orders of magnitude smaller. This discrepancy clearly indicates 
that the value and interpretation of 1/ a is strongly related to the conceptual model and its 
implementation. Two potential reasons explaining the apparent discrepancy can be identified. 
First, in the DFM, water flows predominantly through high-permeability channels (see Figures 1 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9
6
3 
Flux [mm/year] 
Liquid-Release Test Event 1 
Seepage percentage: 61% 
AEPref = 40 Pa 
-3 -2 -1 0 1 2 3 
X 
0
1
2
3
4
5
6 
Z 
0.50 
0.45 
0.40 
0.35 
0.30 
0.25 
0.20 
0.15 
0.10 
0.05 
0.00 
Saturation 
Liquid-Release Test Event 1 
Seepage percentage: 61% 
AEPref = 40 Pa 
(a) (b) 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 33 
and 5), indicating that the observed seepage mass is in fact governed by features that exhibit low 
capillary suction. The absence of discrete features in the FCM, on the other hand, leads to a more 
diffusive flow pattern, and the zone encountered by water exhibits more averaged properties (see 
Figures 7 and 9). To match the data, which are controlled by flow through regions of low capillary 
suction, the reference capillary strength parameter for the FCM must be lowered accordingly. 
The second explanation for the discrepancy focuses on the different degree of connectivity 
implemented in the two models. Since the FCM provides connections in all directions (allowing 
water to be easily diverted around the cavity), the capillary-barrier effect is increased and the 
calculated seepage tends to be small. Consequently, the capillary suction must be reduced to 
match the data from a discrete system. The van Genuchten parameter is adjusted to compensate 
for a deficiency in the model structure (here, the lack of discrete features in the FCM). Note that 
such a compromise is made and considered acceptable whenever a model is built in which certain 
aspects are simplified and represented by lumped parameters. In our case, the length, connectivity, 
and hydrologic properties of the fractures intersecting the drift are difficult if not impossible to 
characterize. A few parameters are chosen to represent the effects of these fractures on seepage, 
and they are determined by inverse modeling based on seepage-relevant data. 
It has been stated that the FCM is devoid of discrete features. A notable exception are the 
gridblocks adjacent to the drift. Their connections to the element representing the opening have 
length .z/2 = 0.05 m. Since water is diverted horizontally only if capillary suction exceeds 
0.05 m, the discretization has an effect similar to that of the discrete fractures that cut into the 
opening, with an (average) distance to the next fracture intersection of 0.05 m. It should therefore 
be clear that the estimated 1/ a value not only depends on the conceptual model, but also on the 
discretization used in the numerical model. 
Such a discretization-dependence of model parameters is well known. For example, since the 
simulation of contaminant plume spreading is affected by numerical-dispersion effects, the 
dispersion length that best matches an observed contaminant plume depends on the chosen grid 
resolution. Similarly, a direct correlation exists between capillarity and gridblock size in 
unsaturated flow problems involving phase dispersion (Pruess 1991, pp. 272274). Stronger 
capillarity leads to increased phase dispersion, as does coarser grid discretization. Consequently, 
model predictions and thus capillary strength parameters estimated by inverse modeling depend 
on grid resolution. 
Note that the mesh-dependent effects described above counteract each other in seepage 
calculations. The coarser the grid, the more seepage is induced on account of an increased 
connection length to the niche element; at the same time, increased phase dispersion leads to a 
reduction in seepage. It is difficult to assess which of the two effects dominates. However, the 
effects cancel one another to a certain degree, reducing the overall dependence of seepage 
calculations on grid resolution.

 
 34 
5.3.7 Synthetic Validation of FCM and Prediction of Seepage Threshold 
In order to test the predictability of the FCM, we perform a synthetic validation exercise, in which 
seepage rates for a new liquid-release experiment are predicted with the calibrated fracturecontinuum 
model. The new injection rate used for the validation run is five times smaller than that 
used for calibration, and the natural percolation flux is increased to 100 mm/year. The prediction 
uncertainty is determined by means of Monte Carlo simulations, in which the two estimated 
parameters log(1/ a) and log( f) are considered uncorrelated and uncertain by a factor of about 
two, i.e., slog(1/ a)= 0.3 and slog( f)= 0.3. These standard deviations reflect expected parameter 
uncertainties, which are based on the estimation uncertainties from the calibration, appropriately 
increased to account for additional errors and the fact that predictions are made under changed 
conditions. 
Figure 10 shows the model prediction with the best-estimate parameter set (solid line) along with 
the results from 100 Monte Carlo simulations. The system behavior as calculated by the DFM is 
shown as a dashed line. The calibrated FCM slightly underpredicts cumulative seepage, mainly 
because of an error in the early-time behavior. Nevertheless, taking into account the uncertainty of 
the model prediction as expressed by the Monte Carlo simulations, the model is considered partly 
validated, i.e., the seepage rate calculated with the DFM lies within the error band predicted by 
the calibrated FCM. The validation study is described in SN YMP-LBNL-SAF-1, pp. 131134. 
The approach outlined here is also used for the actual validation of the SCM (see Section 6.5). 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
NOTE: Monte Carlo simulations show uncertainty of model predictions. 
Figure 10. Validation of Calibrated FCM Against Cumulative Seepage Data 
0 12 24 36 48 60 72 84 96 
Time [h] 
0
1
2
3
4
5
6
7
8
9 
10 
Cumulative Seepage [kg] -
-
- - - - - - - - - - - ----
-
-
- - - - 
- 
- 
- - - - - - - 
- - - - 
- - - - -
- - - -
- - - - - 
-
- - - - - - - - - --
-
--
- - - - - 
- 
- - 
-
- - - - - -
- - - - - - - 
- 
- 
- 
- 
-----
-
-
- - 
----
- - - - - - - - - - - -
- -
-
- - -
-------------- - - - - - - - 
--------------- ---
-
-
- - - -
- - - - - - - - - - 
- 
- - - - -
- - - - 
-
- - 
-
- - -
- - - 
-
- - 
- - - - - - - - - - - -
- - - - 
---
-
-
- 
-
-------------- - - - 
- - - - - -
- - - - - - - - - ----
- - 
-
-
-
-
- - - - - - - - - - - - - - - - 
- 
- 
- - - - - - - - - - - 
-
-
- - - - 
-
- - - - - - ----------
----- -
-
- 
- - - - - -
-
- - - - - - - - - 
-
- - 
--------------- - 
- -
-
- - - - - - - -
-
- - - - - - - 
-
- ---------
-
------------- - - - - - - - - - - - - - - - - - - 
- - - - - - 
---
- - -
-
- - - - - - - - - - - - - - - - - - - - 
- - - - - - - - -
-
-------------- - - - - - - - - -
-------------- - - - - - - - 
-----
-
-
-
- - - 
- - - - - - - - --------
-
-
------------- - - - - - - - - - - - - - --------------- - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - - - 
- -
-
-
- - 
- - - 
-
-
- 
-
-
-
-
------------- - - - - - - 
-
- - - - - - - - - - - 
-
-
-
-----------
----------
-
---- -
--
-
-
- - - Predicted seepage from FCM 
True seepage from DFM 
Monte Carlo 
Realizations

 
 35 
The successful prediction of a transient liquid-release test does not necessarily imply that seepage 
is accurately predicted for low percolation fluxes under natural steady-state flow conditions. We 
therefore calculated the seepage percentage using the DFM in the range of percolation fluxes 
from 3 to 300 mm/year and compared it with FCM predictions. 
In Figure 11, the seepage percentage as calculated with the DFM is shown as a dashed line. A 
seepage threshold of approximately 15 mm/year is obtained. As a result of the discreteness of the 
fracture network, the flow paths diverting water around the niche change as percolation rate 
increases, leading to an erratic, but generally increasing seepage-percentage curve. The FCM 
predicts a seepage threshold of 34 mm/year. Seepage percentage increases monotonically with 
increasing percolation flux, reaching a maximum value of about 60% for a flux of 300 mm/year 
(see Figure 11, solid line). 
To obtain a measure of uncertainty in the FCM seepage prediction, we performed 500 Monte 
Carlo simulations with the two estimated parameters assumed to be uncorrelated and uncertain 
with a standard deviation of 0.3, as above. The percolation flux is sampled uniformly in the 
interval from 3 to 300 mm/year. The resulting seepage percentages are plotted as short dashes in 
Figure 11. From the scattering of the Monte Carlo simulations it can be seen that seepage 
percentage predictions are highly uncertain. The width of the error band is approximately 20% on 
either side of the result obtained with the calibrated parameter set. The distribution is skewed for 
low percolation fluxes on account of the physical lower bound of zero seepage. The Monte Carlo 
simulations for natural percolation fluxes are documented in SN YMP-LBNL-SAF-1, 
pp. 126-130, 135.

 
 36 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
Figure 11. Prediction of Seepage Percentage With Calibrated FCM Including Results 
From Monte Carlo Simulations and Comparison to Seepage Threshold Provided by the 
DFM 
5.3.8 Summary and Conclusions 
In this study, we have demonstrated that simulating seepage into underground openings excavated 
from a highly fractured formation can be performed using a model that is based on the continuum 
assumption, provided that the model is calibrated against seepage-relevant data such as data from 
a liquid-release test. 
Synthetically generated data from the DFM, a model that exhibits discrete flow and seepage 
behavior, were used to calibrate a simplified FCM. The FCM was developed assuming that only 
minimal information is available. From this study, we conclude that: 
1. A calibrated FCM is capable of predicting seepage into an underground opening 
excavated from a fractured formation. 
2. Calibration of the FCM must be performed using data collected from a seepage 
experiment (e.g., a long-term liquid-release test). 
3. The calibration of the FCM yields effective parameters that partially account for the 
discreteness of the fracture network. 
101 102 
Percolation Flux [mm/year] 
-10
0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
Seepage Percentage 
- - 
- 
- 
- 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- - 
- 
- 
- 
- - 
- 
- - 
-- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- - 
-- - 
- - 
- 
-
- 
- 
-- - 
- 
- 
- 
- 
- 
- 
-
- 
- 
- 
-- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-
- 
- 
- 
- 
- 
- 
- 
- 
-- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- - 
- 
- 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-- - 
- 
- 
- - 
- 
- 
- 
- - 
- - 
- - 
- 
- 
- 
-- - 
- - 
- 
- 
- 
- 
- - - 
- 
- 
- 
- 
- 
- 
- 
- 
- - 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- -- - 
- 
- 
- 
- 
- 
- 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- - - 
- 
- 
- 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- - 
- - 
- 
- -- 
- 
- 
- - 
- 
- 
- 
- 
- 
- 
- 
- 
- -- - 
- 
- 
- 
- 
- True seepage from DFM 
Seepage Threshold 
Seepage 
predicted 
with FCM 
Approximate 
95%-confidence interval

 
 37 
4. For seepage calculations, the calibrated capillary strength parameter (e.g., air-entry 
pressure or 1/ a) is likely to be small compared to the values obtained for single fractures. 
5. Because only limited information is available for model calibration, the estimated 
parameters and thus the model predictions remain highly uncertain. 
6. The interpretation and numerical value of the calibrated parameters are related to the 
conceptual model used; this is true for any parameter used in any analytical or numerical 
model. 
7. The synthetic exercise presented here should be repeated for alternative conceptual 
models, e.g., using a discrete fracture network model (DFNM) that is developed based on 
fracture mapping information. These competing alternatives should be ranked according 
to their data needs, their ability to pass validation tests, the respective prediction 
uncertainties, the robustness and stability of the model results, and their computational 
efficiency. 
The study was performed in 2D because effects of discrete fractures on flow and seepage behavior 
are stronger in two than in three dimensions. A similar study using 3D models is thus expected to 
yield the same conclusions as those outlined above. 
Further investigations of numerical seepage modeling may include the development of a discrete 
fracture network model or other alternative conceptual models. The prediction uncertainty of 
these alternative models should be compared to that obtained with the fracture continuum model 
discussed here. The different data requirements should also be critically evaluated when 
comparing the performance of alternative modeling approaches.

 
 38 


 
 39 
6. MODEL ANALYSIS 
6.1 OBJECTIVES AND OUTLINE 
In this section we describe the development, calibration, and validation of the Seepage 
Calibration Model (SCM). The purpose of the SCM is to present a methodology for the 
subsequent development of process models that calculate drift seepage for a variety of geologic 
units, hydrologic property sets, and waste emplacement configurations. The SCM is a template 
fracture-continuum model that is developed based on air-permeability and liquid-release test data 
from the experiments performed in Niche 3650 of the ESF at Yucca Mountain, Nevada. It is 
suggested to use a similar approach when developing seepage models for other units and drift 
geometries; these models necessarily differ to some extent from the SCM to accommodate 
particular conditions. 
In Section 6.2, we review the data basisair permeabilities and seepage data from liquid-release 
testsused for the development, calibration, and validation of the SCM. The conceptual model as 
well as details of the SCM are described in Section 6.3, followed by a discussion of its calibration 
by inverse modeling (Section 6.4). Validation of the SCM is described in Section 6.5. 
Seepage-threshold predictions with the calibrated SCM are presented in Section 6.6. Alternative 
conceptual models and sensitivity analyses are briefly discussed in Sections 6.7.1 and 6.7.2, 
respectively. Conclusions from the analyses presented in this section are summarized in Section 7. 
6.2 REVIEW OF DATA USED FOR CALIBRATION AND MODEL VALIDATION 
6.2.1 Introduction 
The data used for model calibration/validation were collected during the ESF Drift Seepage Test 
and Niche Moisture Study, an ongoing field-testing program initiated by Lawrence Berkeley 
National Laboratory (Berkeley Lab) in 1997. The two primary objectives of the field-testing 
program are: 
 Measure in situ permeability for use in the Seepage Calibration Model (this AMR) and 
the Seepage Model for PA. 
 Provide a database containing liquid-release and seepage data that can be used to 
calibrate the drift-scale seepage models. 
The objectives of the study described above are realized through field experiments consisting of 
air-injection and seepage tests. Sections 6.2.2 through 6.2.4 describe the air-injection and liquidrelease 
test data used as input to the SCM described in Sections 6.3 through 6.5 of this AMR.

 
 40 
6.2.2 Site Location and Borehole Configuration at Niche 3650 
6.2.2.1 Site Location 
Niche 3650 consists of a short drift constructed along the west side of the ESF at the location 
shown on Figure 12. The niche is located within the middle nonlithophysal zone of the Topopah 
Spring Tuff in an area of relatively competent rock mass with low fracture density. 
Figure 12. Schematic Showing Approximate Location of Niche 3650 
6.2.2.2 Borehole Configuration 
Seven short boreholes were drilled at Niche 3650 prior to excavation to gain access to the rock for 
testing and monitoring purposes. Figures 13 and 14 show the approximate location of the seven 
borings installed prior to construction of Niche 3650 as well as the idealized shape of the final 
excavation. Three of the borings, designated UL, UM, and UR (upper left, upper middle, and 
upper right), were installed above the crown of the niche in a horizontal plane. The remaining 
boreholes (ML, MR, BL, and BR) were drilled within the limits of the proposed niche and were 
subsequently mined out during niche construction. 
ESF 
27 
28 
Niche 3650 
36 + 50 
Alcove No.6 
(North Ghost Dance 
Fault Alcove) 
35 + 00 
37 + 50 
E-W Drift 
Bedrock 
Exposed

 
 41 
Figure 13. Schematic of Niche 3650, End View 
Figure 14. Schematic of Niche 3650, Plan View 
3.25 m 
CL 
1.0 m 1.0 m 
0.5 m 0.5 m 
0.5 m 0.5 m 
MR ML 
BR BL 
UL UM UR 
1.0 m 1.15 m 
Niche 3650 
Legend 
ML Borehole designation 
B = Bottom, M = Middle, U=Uppe r 
L = Left, R = Right 
*** All measurements are approximate 
and do not represent as-built conditions 
0.65 m 
2.0 m 2.0 m 
Legend 
Upper Borehole Axis 
Bottom & Middle Borehole Axis 
UL = Upper Left 
UM = Upper Middle 
UR = Upper Right 
ML = Middle Left 
MR = Middle Right 
BL = Bottom Left 
BR = Bottom Right 
CL 
N 
UM 
UR 
UL 
ESF Main Drift 
2 Meters 0 1 
Approximate Scale: 1 inch = 2 Meters 
*** All measurements are approximate and 
do not represent as- built conditions. 
ML, BL MR, BR

 
 42 
6.2.3 Air-Injection Tests 
Numerous air-injection tests were performed at Niche 3650 to determine the air-permeability 
distribution of the rock after niche construction. Air-injection tests were conducted on short test 
intervals in boreholes UL, UM, and UR. The tests were performed by isolating a short section of 
borehole 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, as well as in 
nearby observation intervals, was monitored with time until steady-state conditions were reached, 
typically occurring within a few minutes of the start of the test. 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. 
The Niche 3650 air-permeability values were entered into the Technical Database Management 
System (DTN: LB980001233124.002). The post-excavation air-permeability data for Niche 3650 
were extracted from DTN: LB980001233124.002 and the log-transform (logarithm base 10) of 
these data were calculated and used in this AMR (for details, see SN YMP-LBNL-RCT-DSM-1, 
p. 11). 
The log-transformed values of the post-excavation air-permeability data obtained from boreholes 
UL, UM, and UR were used as input to GAMV2 (GAMV2, V1.201, 
10087-1.0MGAMV2V1.201-00), a software module that is part of the Geostatistical Software 
Library (GSLIB). GAMV2 is described in detail by Deutsch and Journel (1992, pp. 5355). 
GAMV2 calculates the semivariogram of irregularly spaced data in two dimensions. Figure 15 
shows the semivariograms calculated for two test cases using a lag distance of 0.3 m. Case 1 
includes all the air-permeability data from the post-excavation air-injection tests. The 
semivariogram shown on Figure 15 for Case 1 (diamonds) is nearly linear with a positive slope, 
implying unlimited variance and a possible systematic bias in the data set. The semivariogram for 
Case 2 (triangles) was created by excluding nine data points collected from test intervals located 
beyond the limits of the excavation (borehole UM, zones 29 through 31 and borehole UR, zones 
26 through 31). Removal of these data, which typically have lower air permeabilities than those 
intervals located immediately above the opening, results in a semivariogram with a finite sill 
variance. Analyzing only those air-injection tests that were performed on test intervals located 
immediately above the opening and whose resulting air-permeability values were thus more or 
less uniformly influenced by the excavation resulted in a reasonable semivariogram devoid of a 
systematic trend. Data that are located beyond the limits of the excavation and were removed 
from Case 2 may represent a separate population of air permeabilities performed in an area of 
relatively undisturbed, lower-permeability rock. More details can be found in 
SN YMP-LBNL-RCT-DSM-1, pp. 1925. 
The histogram and cumulative distribution function shown in Figure 16 and the descriptive 
statistics shown in Table 5 were generated using the Case 2 data set described above (for details, 
see SN YMP-LBNL-RCT-DSM-1, pp. 2526). The cumulative distribution function, sample 
statistics, and the semivariogram for the post-excavation air-permeability values will be used in 
Section 6.3.2 to generate a conditioned heterogeneous permeability field for the SCM.

 
 43 
Figure 15. Semivariogram for Post-Excavation Niche 3650 Log-Air-Permeability Values 
Table 5. Statistical Summary of Air-Permeability Data 
Case 2, Post-Excavation log10(k [m2]) 
Mean -11.66 
Standard deviation 0.72 
Standard error 0.08 
Median -11.71 
Minimum -13.84 
Maximum -9.15 
Range 4.69 
Number of data points 84 
(based on data submitted with this AMR under 
DTN: LB990831012027.001) 
(nlag=20, xlag=0.3 m, xltol=0.15 m) 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1
0 1 2 3 4 5 6 7 
Lag Distance (m) 
Semivariogram Value 
Case 1 - all data 
Case 2 - end data removed 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 44 
Figure 16. Histogram and Cumulative Distribution Function for Post-Excavation Niche 
3650 Log-Air-Permeability Values 
6.2.4 Liquid-Release Tests 
A series of short-duration liquid-release tests was performed at Niche 3650 to characterize 
seepage into an underground opening. The seepage tests were conducted after niche excavation 
by pumping water into select test intervals in boreholes UL, UM, and UR located above the niche. 
The tests were performed by sealing a short section of borehole using an inflatable packer system 
and then releasing water at a constant rate into the isolated test interval. Any water that migrated 
from the borehole to the niche ceiling and dripped into the opening was captured and weighed. 
The seepage percentage, defined as the mass of water that dripped into the capture system divided 
by the mass of water released into the borehole interval, was used to quantify seepage into the 
drift from a localized source of water of known duration and flow rate. 
Forty liquid-release tests were performed on 16 test intervals positioned above Niche 3650. Water 
migrated through the rock and seeped into the niche for 10 out of the 16 zones tested. The seepage 
and liquid-release data were entered into the Technical Database Management System under 
DTN: LB980001233124.003. 
Seepage-test data collected from two of the 10 intervals (UM 4.274.57 m and UM 5.495.79 m) 
were retrieved from DTN: LB980001233124.003 and are used in this AMR to calibrate and test 
0
2
4
6
8 
10 
12 
14 
16 
18 
20 
-14.00-13.75-13.50-13.25-13.00-12.75-12.50-12.25-12.00-11.75-11.50-11.25-11.00-10.75-10.50-10.25-10.00-9.75-9.50-9.25-9.00-8.75More 
Log10(k(m^2)) 
Frequency 
.00% 
20.00% 
40.00% 
60.00% 
80.00% 
100.00% 
120.00% 
Frequency 
Cumulative % 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 45 
(validate) the SCM. Table 6 summarizes five separate liquid-release tests conducted at different 
liquid-release rates in borehole UM at a depth of 4.27 to 4.57 m from the borehole collar. These 
data are used to calibrate the SCM as described in Section 6.4. Likewise, Table 7 summarizes data 
from four seepage tests conducted at different liquid-release rates in borehole UM at a depth of 
5.49 to 5.79 m from the borehole collar. These data are used to validate the SCM as described in 
Section 6.5. 
Interval UM 4.274.57 m was selected because as many as five individual seepage tests were 
performed at this location (typically, only three or fewer individual tests were conducted per test 
interval). The liquid-release rate was lowered step-wise to identify a potential seepage threshold. 
In addition, the seepage percentage for two of the tests (Test #1 12-3-97 and Test #2 12-3-97) 
were significantly different even though the tests were conducted at nearly the same liquid-release 
rate. The large difference in seepage percentage was attributed to the effect of wetting history, i.e., 
the seepage data are believed to exhibit a strong memory effect because the tests were performed 
only two hours apart. Consequently, residual water remaining in the fractures from the first test 
(Test #1 12-3-97) influenced the amount of seepage that occurred during the second test (Test #2 
12-3-97). 
Interval UM 5.495.79 m was selected for validation of the SCM because as many as four 
seepage tests were conducted at different rates, i.e., the calibrated SCM is validated against data 
from a different location and under variable flow conditions.

 
 46 
Table 6. Summary of Liquid-Release Test Results for Interval UM 4.274.57 
Event/ Event/ When Wetting When Dripping When Liquid- Mass Seepage Seepage 
Event/ Release Start Release End Front Arrives Begins Dripping Ends Release Released Mass Percentage 
Test Name (sec) (sec) (sec) (sec) (sec) Rate (kg/s) (kg) (kg) % 
Test 5 Niche 3650 0 500 409 423 2453 2.019E-03 1.0087 0.2280 22.60 
Inactive - no release 500 1720511 0.000E+00 
Test #1 12-3-97 1720511 1722529 1721505 1721804 1724389 5.034E-04 1.0157 0.2354 23.18 
Inactive - no release 1722529 1729525 0.000E+00 
Test #2 12-3-97 - start 1729525 1731528 1730015 1730083 1732793 5.057E-04 1.0125 0.5686 56.16 
Inactive - no release 1731528 4747330 0.000E+00 
Test #1 1-7-98 - end 4747330 4768628 4755893 4763621 4770213 4.715E-05 1.0042 0.0460 4.58 
Inactive - no release 4768628 7690199 0.000E+00 
Test #2 2-10-98 - start 7690199 7760753 7703568 -- -- 1.649E-05 1.1633 0 0 
Inactive - no release 7760753 
-- Event did not occur 
Data Sources: 
1. Elapsed times calculated in Excel 97 workbook "Niche 3650 Liq-rel UM4.27-4.57 m-2.xls" in worksheet "Elapsed times" at pp. 32-35 in SN YMP-LBNL-RCT-DSM-1. 
2. Liquid-release rate, mass released, seepage mass, seepage percentage, and absolute time of event from DTN: LB980001233124.003. 
Elapsed Time

 
 47 
Event/ When Event/ When Event/ When Wetting When Dripping When Liquid- Mass Seepage Seepage 
Test Name Release Begins Release Ends Front Arrives Begins Dripping Ends Release Released Mass Percentage 
(sec) (sec) (sec) (sec) (sec) Rate (kg/s) (kg) (kg) % 
Test 4 Niche 3650 0 490 205 327 1385 2.069E-03 1.0134 0.2757 27.21 
Inactive - no release 490 1811503 0.000E+00 
Test #2 12-4-97 1811503 1813554 1811905 1812151 1814143 5.040E-04 1.0337 0.2221 21.49 
Inactive - no release 1813554 4913288 0.000E+00 
Test #1 1-9-98 4913288 4931265 4916035 4924171 4931765 5.778E-05 1.0387 0.0333 3.21 
Inactive - no release 4931265 7773087 0.000E+00 
Test #1 2-11-98 7773087 7849643 7783195 -- -- 1.361E-05 1.0417 0.0000 0.00 
Inactive - no release 7849643 0.000E+00 
-- Event did not occur 
Data Sources: 
1. Elapsed times calculated in Excel 97 workbook "Niche 3650 liq-rel UM5.49-5.79 m.xls" in worksheet "Elapsed times" at pp. 35-37 in SN YMP-LBNL-RCT-DSM-1. 
2. Liquid-release rate, mass released, seepage mass, seepage percentage and absolute time of event from DTN: LB980001233124.003. 
Elapsed Time 
Table 7. Summary of Liquid-Release Test Results for Interval UM 5.495.79

 
 48 
6.3 DEVELOPMENT OF SEEPAGE CALIBRATION MODEL (SCM) 
6.3.1 Conceptual Model 
The SCM is intended to be a 3D, heterogeneous drift-scale fracture continuum model. The 
modeling assumptions are described in Section 5.2. The appropriateness of using a 
fracture-continuum approach for seepage calculations has been demonstrated in Section 5.3. 
Furthermore, choosing the continuum approach is conceptually consistent with the unsaturated 
zone site-scale model and submodels thereof, such as the various drift-scale models. This makes it 
straightforward to embed the SCM into the current modeling framework. 
During the step-wise development of the SCM, a number of more simplified models have been 
tested, including 2D homogeneous, 2D heterogeneous, and 3D homogeneous representations of 
the SCM. 
6.3.2 Generation of Heterogeneous Permeability Field 
The geostatistical analysis described in Section 6.2.2 provides an empirical semivariogram of 
post-excavation air permeabilities at Niche 3650. Different theoretical models are fitted to the 
empirical semivariogram (SN YMP-LBNL-SAF-1, pp. 8992, 98). A spherical semivariogram 
model with a nugget effect of 0.40, a correlation length of 3.87 m, and a sill value of 0.53 best 
matches the empirical log-permeability semivariogram, as shown in Figure 17. Note that the 
correlation length is simply a fitting parameter. Its relatively large value should not be 
misinterpreted as suggesting that the permeability field is strongly correlated. In fact (as clearly 
shown in Figure 17), the permeability is essentially random without a noticeable spatial 
correlation. Random, uncorrelated permeability fields are expected to yield similar results as the 
weakly correlated field used in this study. 
The spherical semivariogram of Figure 17 along with the cumulative distribution function shown 
in Figure 16 is used as input to program SISIM (SISIM, V1.203, 10001-1.0MSISIMV1.203-00). 
SISIM generates a three-dimensional, spatially correlated permeability field using sequential 
indicator simulation (Deutsch and Journel 1992, p. 151). The random permeability field is 
conditioned on the post-excavation air-permeability data measured in various intervals of 
boreholes UL, UM, and UR. More details about the generation of a heterogeneous permeability 
field can be found in SN YMP-LBNL-SAF-1, pp. 9399. The resulting permeability field is 
mapped onto the numerical grid of the SCM, as described in Section 6.3.3. Van Genuchtens 
capillary pressure and relative permeability functions (Luckner et al. 1989, pp. 21912192) are 
used in the SCM, where the capillary strength parameter 1/ a is correlated to the permeability in 
each grid block according to Leveretts scaling rule (Leverett 1941, p. 159; Finsterle 1997, 
p. 224).

 
 49 
Figure 17. Empirical and Fitted Spherical Semivariogram of Post-Excavation 
Air-Permeabilities Used to Generate Permeability Field of Seepage Calibration Model 
6.3.3 Mesh Generation 
Since the SCM will be calibrated against liquid-release test data, it is discretized to include the 
approximate geometry of Niche 3650 and its boreholes. The mesh is created in several steps as 
follows (SN YMP-LBNL-SAF-1, pp. 100102): 
1. A basic mesh is generated with X-Y-Z dimensions of 6  1.5  5 m, respectively, 
discretized into regular, square gridblocks with a side length of 0.1 m. Given the small 
amount of water being injected, a model thickness of 1.5 m is sufficient to avoid 
unwanted boundary effects. This was confirmed by the simulations, in which the 
saturation plumes did not reach the model boundaries at Y=0.0 m and Y=1.5 m. As 
discussed in Section 5.3.6, the estimated parameters are affected by grid resolution, i.e., 
the values given in Table 10 below strictly refer to the discretization chosen here. 
2. The heterogeneous permeability field (see Section 6.3.2) is mapped onto the mesh using 
program Perm2Mesh (Perm2Mesh, V1.0, ACC: MOL.19990721.0554). 
3. A niche is cut from the mesh using program CutNiche (CutNiche, V1.1, 
ACC: MOL.19990721.0557). The niche is rectangular (4 m wide and 2.5 m high) with a 
circular ceiling (radius 3 m, crown at Z = 3.25 m, axis parallel to the Y direction). A very 
Data from Post-Excavation 
Air-k Test Interpretations 
Spherical Semi-Variogram 
Nugget: 0.40 
Correlation length: 3.87 
Sill: 0.53 
0 1 2 3 4 5 6 
Lag-Distance [m] 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
Semi-Variogram 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 50 
small nodal distance is defined from the element representing the niche to the first layer 
of formation elements, allowing us to set boundary conditions right at the niche wall. The 
length of the last vertical connection is thus 0.05 m, i.e., no horizontal flow diversion can 
occur closer than 0.05 m from the niche wall. (For more details, see 
SN YMP-LBNL-SAF-1, p. 39, 101.) 
4. Boundary elements are added to the top and bottom of the model domain using program 
AddBound (AddBound, V1.0, ACC: MOL.19990721.0553). The bottom boundary 
element is assigned to rock type DRAIN to allow specifying a free-drainage boundary 
condition (Section 6.3.4). 
5. Boreholes, injection intervals, and packers are included by modifying the material name 
of the corresponding elements (for details, see SN YMP-LBNL-SAF-1, pp. 100101). 
The final grid and log-permeability field of the 3D heterogeneous SCM is shown in Figure 18. 
Submodels of the SCM include a 2D X-Z cross section at Y = 0.75 and homogeneous versions of 
the model in two and three dimensions. 
Figure 18. 3D Seepage Calibration Model with Log-Permeability Field 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 51 
6.3.4 Initial and Boundary Conditions 
The present-day percolation flux at Niche 3650 is unknown or highly uncertain. The uncertainty 
is a result of uncertainties in the infiltration rates, and the channeling effects on account of larger 
scale flow diversions and local heterogeneities. Nevertheless, percolation flux on the scale of the 
niche is expected to be on the order of a few millimeters per year (see also Section 5.2, 
Assumption No. 7). It is considered reasonable to apply a constant rate of 3 mm/year at the top of 
the model domain. Since this flux is significantly lower than the fluxes induced by the 
liquid-release tests to be modeled with the SCM, it is not expected that the simulation results are 
strongly affected by the uncertainty in the assumed percolation flux. Moreover, this flux is 
believed to be below the seepage threshold, an assumption that is tested and confirmed after 
calibration of the SCM (see Section 6.6). A free-drainage boundary condition (Finsterle 1998, 
pp. 1415) is applied at the bottom of the model. The niche itself is at a reference pressure of 1 
bar. No capillary suction is applied in the niche, i.e., it is assumed that the air directly at the niche 
wall is of 100% relative humidity (see discussion of Assumption No. 6 in Section 5.2). Water is 
allowed to enter, but is prevented from exiting the niche. Thus, the temporal change of water in 
the niche element represents the cumulative seepage collected in the capture system installed 
during the liquid-release tests in Niche 3650. No-flow boundary conditions are specified at the 
left, right, back, and front sides of the model. 
The initial saturation distribution is given by the steady-state flow field under natural percolation. 
Since the steady-state flow field changes if some of the input parameters are updated during the 
inversion, a steady-state simulation should precede each transient simulation of the liquid-release 
test. This approach is implemented for the homogeneous cases. For the heterogeneous cases, 
however, reaching steady state is computationally very time consuming. The initial saturation in 
the fracture continuum is generally low and is likely to have only a small impact on the simulation 
results. Moreover, the need for an accurate reproduction of a steady-state saturation distribution 
cannot be justified given the fact that initial conditions are under a transient regime as a result of 
ESF and niche excavation as well as dry-out from ventilation. Consequently, steady-state 
calculations are performed only once for the heterogeneous cases, assuming initial parameter 
values (see Section 6.3.5), and each transient run is preceded by a 2-year natural-state simulation 
to allow some redistribution of water as parameters are updated. 
6.3.5 Initial Parameter Set 
Table 8 summarizes the initial parameter set for the SCM. Reference permeability is taken from 
the post-excavation air-injection tests (see Section 6.2.3). The permeability used in the UZ Flow 
and Transport Model is given for comparison and is used to test the predictability of an 
uncalibrated, unconditioned SCM (see discussion in Section 6.4). Porosity and the reference van 
Genuchten parameter 1/ a will be varied during the inversion. All the other parameters are taken 
from the calibrated property set (DTN: LB997141233129.001) for the middle nonlithophysal unit 
of the Topopah Spring Tuff (hydrogeologic model layer TSw34), under base-case infiltration 
conditions. Since the SCM is a fracture-continuum model, no matrix properties are needed. Recall 
that the heterogeneous models include a spatially correlated permeability field, i.e., each 
gridblock has its own permeability value; the capillary strength parameter 1/ a is also considered 
heterogeneous, correlated to the permeabilities according to the Leverett scaling rule (see 
Section 6.3.2).

 
 52 
6.4 MODEL CALIBRATION 
Calibrating the SCM against seepage data from liquid-release tests provides model-related, 
effective parameters relevant to the processes involved in drift seepage. The fact that 
seepage-relevant parameters are determined using flow data from a seepage experiment is a key 
aspect of our modeling approach. Determining parameters from flow experiments rather than 
from geometric information such as fracture density and aperture has significant advantages. 
Fracture mappings are strongly biased because of the selection of an arbitrary cut-off length, 
which filters out the smaller fractures and microfractures. The latter are believed to be of great 
importance for seepage. Small fractures and microfractures, if interconnected, are critical for 
seepage because they have sufficient capillary strength to hold the water, preventing it from 
seeping into the opening. At the same time they haveunlike the matrixsufficient permeability 
to facilitate flow diversion around the drift. Furthermore, deriving flow properties from geometric 
fracture information must rely on simplified models, such as the parallel plate assumption, 
leading to highly uncertain and systematically biased estimates. In general, we believe that there 
is only a weak correlation between fracture mapping information and seepage-relevant 
hydrogeologic parameters, i.e., flow and seepage behavior cannot be reliably predicted from 
geometric fracture information alone. 
If parameters are estimated by inverse modeling using flow data from seepage experiments, 
seepage-relevant processes are automatically reflected in the estimated parameters. Moreover, the 
estimates are able to partly compensate for processes and features not explicitly accounted for in 
the model. For example, an estimate of matrix permeability determined by inverse modeling is 
automatically increased if microfractures are present. Ormore relevant for the current study 
the tendency for increased seepage from poorly connected, discrete fractures intersecting the drift 
can be accounted for by an appropriately reduced van Genuchten 1/ a value (see discussion in 
Section 5.3). The simplification made by the model (discrete fractures are not explicitly included) 
can be partly compensated by estimating effective parameters. It should be acknowledged that the 
estimated parameters are not hydrologic properties per se, but are related to the conceptual model. 
Determining model-related parameters from data that reveal seepage-relevant processes results in 
a model suitable for seepage predictions. 
Table 8. Initial Parameter Set 
Parameter Value Units Reference 
fracture permeability, kf 2.18E-12 m2 from post-excavation air-injection tests, see Section 
6.2.3, Table 5. 
fracture permeability, kf 1.70E-11 m2 DTN: LB997141233129.001 
porosity, f 0.01 - DTN: LB997141233129.001; will be estimated. 
van Genuchten a 5.16E-4 Pa-1 DTN: LB997141233129.001; will estimate 1/ a. 
van Genuchten m 0.608 - DTN: LB997141233129.001 
van Genuchten n 2.551 - n=1/(1-m) 
residual saturation, Slrf 0.01 - DTN: LB997141233129.001 
satiated saturation, Slsf 1.0 - DTN: LB997141233129.001

 
 53 
We employed inverse modeling techniques implemented into the code iTOUGH2 for automatic 
calibration of the SCM. As described in Section 6.2.4, a sequence of five liquid-release tests was 
selected for calibration. The five tests from interval UM 4.274.57 m were conducted at various 
injection rates with different lengths of inactivity between individual test events. The seepage data 
are thus expected to reveal a number of seepage-relevant processes, including storage, memory 
effects and the approach of a seepage threshold. 
In a transient seepage experiment with only a small volume released, the amount of water seeping 
into the niche mainly depends on three factors: 
1. The potential of the formation to hold the water by capillary forces, here expressed 
through an effective van Genuchten parameter 1/ a. 
2. The potential of the formation to store the finite amount of water released, here expressed 
through an effective porosity f, which may include effects of matrix imbibition. 
3. The potential of the formation to divert water around the opening, here expressed through 
an effective permeability k, which may include contributions from microfractures and the 
matrix. 
The simulated seepage percentage can be increased by decreasing either capillary strength 1/ a, 
porosity f, or permeability k. Consequently, all parameter pairs are negatively correlated when 
inversely determined from seepage data. If only seepage-percentage data are available for 
calibration, the parameter correlations are expected to be strong, i.e., it is unlikely that they can be 
determined independently from one another and with a reasonably low estimation uncertainty. 
Unfortunately, each liquid-release test provides only one data point for calibration, which is the 
total amount of water collected in the capture system at the end of each test event. No timedependent 
seepage rates are available from Niche 3650. As a result of this limited data basis and 
the expected strong correlations, the number of parameters to be estimated must be kept as small 
as possible. 
We choose to determine the porosity f and the van Genuchten parameter 1/ a while fixing 
permeability k at the value estimated from the post-excavation air-injection tests (see 
Section 6.2.3). The rationale for this selection is as follows: 
1. The capillary strength 1/ a is both sensitive and uncertain, i.e., the reliability of 
subsequent seepage predictions depends to a large extent on the value of 1/ a, which 
cannot be deduced from independent information (such as fracture mapping). The 
estimated value will be an effective parameter describing the capillary barrier in a 
drift-scale fracture continuum model. 
2. Since only a relatively small amount of water (approximately 1 kg, see Table 6) is 
released in each test event, fracture and matrix storage effects are expected to be 
significant. It should be noted that porosity is not important for seepage predictions under 
near-steady-state conditions. The fact that porosity must be estimated here is a result of 
the particular liquid-release test design. Liquid-release test were restricted to the use of a

 
 54 
small amount of water, which made the tests highly transient and increased the 
importance of storage effects. 
3. Permeability is excluded from the estimation process because there is independent 
information available from the air-injection tests (for further discussion, see Section 5.2, 
Assumption No. 3). Furthermore, flow diversion is expected to play a minor role during 
these tests as a result of the small amount of water released. Nevertheless, permeability 
should be included and considered uncertain in any steady-state seepage prediction 
exercise. 
The need to estimate porosity is unfortunate because porosity will not affect steady-state seepage 
predictions. Moreover, estimating porosity increases the uncertainty in the estimate of 1/ a to 
which it is strongly correlated. Future liquid-release tests should be designed as long-term 
experiments, with a sufficient amount of water being released so that porosity becomes 
insensitive, and conversely flow diversion around the niche becomes important, enabling the 
estimation of seepage-relevant permeability values on the drift scale. 
Transient information is needed to better determine porosity. The obvious memory effect seen 
between Test #1 12-3-97 and Test #2 12-3-97 helps identify the storage capacity in a joint 
inversion of data from both tests. Moreover, the time when seepage starts and ends has been 
recorded (see Table 6). This informationeven though highly dependent on the specific features 
of the fractures encountered by the water on its path from the injection point to the niche ceiling 
is included in the inversion in an approximate way. Two dummy data points are added to each test 
event. The first dummy data point of zero seepage is specified at the time when seepage starts, 
and the total seepage amount is given as a second dummy data point at the time when seepage 
stopped, along with the actual point which is assigned at the end of the test event. For example, 
for Test #1 12-3-97 (see Table 6), a calibration point with a seepage mass of zero is specified at 
time 1,721,804 s and assigned a standard deviation of 0.03 kg; the second dummy calibration 
point has a value of 0.2354 kg at time 1,724,389 s, also with a standard deviation of 0.03 kg; 
finally, the third calibration point with the measured seepage mass of 0.2354 kg is set at time 
1,729,520 s (i.e., shortly before the next test event) and is more strongly weighted by assigning a 
lower standard deviation of 0.01 kg. The first two calibration points penalize both early water 
breakthrough and long seepage tails, thus introducing some transient information for a better 
constraint of porosity. More details can be found in YMP-LBNL-SAF-1, pp. 103109, 146. 
Using the initial parameter set of Table 8 would lead to zero seepage in all simulated 
liquid-release tests as a result of the relatively low a value. Recall that initial a was determined 
based on air-permeability data, fracture density information, and a parallel-plate assumption, and 
by calibration against matrix saturation and water-potential measurements, i.e., they are not 
expected to be representative of seepage-related processes. 
A joint inversion of all five test events was performed using homogeneous and heterogeneous, 2D 
and 3D representations of the SCM. The resulting matches are summarized in Table 9 and 
visualized in Figure 19. 

 
 55 
NOTE: 95%-error bands indicate prediction uncertainty of calibrated SCM. 
Figure 19. Comparison Between Measured (Circles) and Calculated (Squares) Seepage 
Mass 
All models show the following general behavior: 
1. Even though the total amount of water released during each test event is approximately 
the same, less seepage is observed if the water is released at a lower rate, suggesting the 
existence of a seepage threshold, which is defined as a rate below which no seepage 
occurs. This fundamental behavior is seen in both the data and the corresponding 
simulation results. 
2. If a liquid-release test is followed by another test at a similar rate but with little inactive 
time between them (e.g., Tests #1 and #2 conducted on 12-3-97) the second test leads to 
significantly more seepage. This is a result of the fact that a certain amount of water is 
stored in the formation before seepage is initiated; this storage volume is reduced or not 
available for the second test event. The resulting memory effect can be seen in both the 
data and the simulation. 
3. The two effects described above overlap, making it sometimes difficult to clearly 
separate and identify them. For example, Test #1 12-3-97 yielded a seepage mass 
comparable to that measured in Test 5 Niche 3650 despite its lower rate and despite a 
long inactive time between them. A potential explanation for this behavior is that storage 
1 2 3 4 5 
Test 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
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Seepage Mass [kg] 
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Test 5 Niche 3650 
Test #1 12-3-97 
Test #2 12-3-97 
Test #1 1-7-98 
Test #2 2-10-98 
2D hom 
2D het 
3D hom 
3D het 
Data Point 
(based on DTN: LB980001233124.003 and on data submitted with this AMR 
under DTN: LB990831012027.001)

 
 56 
effects are much stronger and last longer for the first in a sequence of liquid-release tests. 
Water from the first test is used mainly to fill up dead-end fractures and to saturate the 
matrix; the seepage rate is correspondingly low. For all subsequent tests, storage is 
restricted to saturation increases in the flowing fractures, i.e., the memory effect is 
expected to be much weaker. A second explanation could be thatconsidering the rate 
and measured air permeabilitythe test is performed at a rate close to or exceeding the 
saturated hydraulic conductivity in the vicinity of the tested interval, conditions for which 
seepage is almost constant regardless of injection rate. 
The matches obtained with the SCM can be considered acceptable given the uncertainty in the 
data and the parsimony of the models. The 3D models perform slightly better than the 2D 
alternatives; the differences in the matches obtained with homogeneous and heterogeneous 
models are insignificant. A notable exception is Test #1 1-7-98, in which a higher seepage mass 
is predicted with the heterogeneous models compared to the homogeneous models. The 
heterogeneous models are in better agreement with the measured data point. 
The 2D models fail to reproduce the relatively high seepage mass in Test #1 12-3-97, which 
indicates that the strong memory effect from the initial injection (Test 5 Niche 3650) is not 
appropriately represented in the model because features such as dead-end fractures and processes 
such as matrix imbibition are not explicitly represented. As a result, seepage from the first test is 
overpredicted, whereas the calculated seepage mass for the second testperformed at a lower 
rateis underpredicted. This inability of the 2D model to reproduce the initial memory effect 
may slightly bias the estimates. Since initial memory effects are insignificant if the flow field is 
near steady state, they are of little importance for seepage predictions under natural percolation 
conditions and should be eliminated by matching data of long-term liquid-release tests. 
The 3D models simulate the temporal and spatial spreading of the liquid plume more accurately 
than the 2D models. The initial storage of water and its distribution is better represented, and as a 
result, the 3D models are capable of matching all test events very accurately. Note that the 3D 
heterogeneous model is the only one that leads to seepage for the last test, which was conducted at 
a very low rate. It is therefore expected that the 3D heterogeneous model will predict a lower 
seepage threshold than the other models. 
Table 9. Comparison Between Measured and Calculated Seepage Mass in kg 
Model 
Test 5 Niche 
3650 
Test #1 
12-3-97 
Test #2 
12-3-97 
Test #1 
1-7-98 
Test #2 
2-10-98 
Meas. Calc. Meas. Calc. Meas. Calc. Meas. Calc. Meas. Calc. 
2D-homogeneous 0.228 0.290 0.235 0.155 0.569 0.577 0.046 0.000 0.000 0.000 
2D-heterogeneous 0.228 0.289 0.235 0.153 0.569 0.571 0.046 0.055 0.000 0.000 
3D-homogeneous 0.228 0.228 0.235 0.238 0.569 0.574 0.046 0.004 0.000 0.000 
3D-heterogeneous 0.228 0.229 0.235 0.239 0.569 0.564 0.046 0.046 0.000 0.001 
(based on DTN: LB980001233124.003 and on data submitted with this AMR under DTN: LB990831012027.001)

 
 57 
The parameter estimates and their uncertainties are summarized in Table 10. The standard 
deviation is the square root of the diagonal element of the estimation covariance matrix (Carrera 
and Neuman 1986, p. 205); the correlation coefficient is calculated from the variances and the 
covariance between the two estimated parameters (Finsterle 1999, p. 63). 
The following comments can be made: 
1. The estimated seepage-related capillary strength parameter log(1/ a) is much lower than 
the initial value of 3.29 (see Table 8). The difference can be explained as a result of the 
fact that the initial guess refers to the large-scale behavior of intact rock, whereas the 
estimate from the liquid-release tests characterizes the excavation-distrurbed zone and 
includes the seepage-specific effects from discrete fractures near the niche wall. 
2. The capillary strength parameter 1/ a is lower for the homogenous than for the 
heterogeneous model. This is expected because the explicitly modeled heterogeneity 
leads to some flow channeling, resulting in higher seepage. The absence of flow 
channeling in the homogeneous model is compensated for by reducing the capillary 
barrier effect, i.e., by making 1/ a smaller. 
3. The capillary-strength parameter 1/ a is lower for the 3D than for the 2D models. The 3D 
models provide additional possibilities for flow diversion, reducing seepage. 
Consequently, the capillary strength must be reduced to induce seepage comparable to 
that obtained with the 2D models and the observed data, leading to relatively low 1/ a 
estimates. 
4. The porosity estimates for the 2D homogeneous and heterogeneous models are consistent 
with one another. They are higher than the estimates from the 3D models, indicating that 
the liquid plume was somewhat confined by the two-dimensionality of the model. 
5. The correlation coefficient between capillary strength and porosity is negative because 
reducing 1/ a leads to an increase in seepage, which can be partly compensated by an 
increase in the pore volume available for storage. The correlation coefficient is 
consistently estimated for all four models considered. 
Table 10. Parameter Estimates, Estimation Uncertainty, and Correlation Coefficient 
Model log(1/ a [Pa] )* log( f) Corr. Coeff. 
Estimate Std. Dev. Estimate Std. Dev. 
2d-homogeneous 2.25 0.09 -2.46 0.06 -0.75 
2d-heterogeneous 2.95 0.06 -2.49 0.05 -0.63 
3d-homogeneous 1.50 0.03 -2.87 0.02 -0.74 
3d-heterogeneous 1.82 0.01 -2.89 0.01 -0.77 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
* In the heterogeneous models, 1/ a is the reference capillary strength, which is related to the 
reference permeability k = 10-12 m2 through Leveretts scaling rule (Leverett 1941, p. 159).

 
 58 
As demonstrated here, the differences and similarities in the estimated parameter sets can be 
explained and related to the structure of the respective conceptual models, illustrating the need to 
determine and to use process-specific and model-related parameters. As a result of effective 
parameter estimation, all models are able to reproduce the same effective behavior. 
6.5 MODEL VALIDATION 
According to AP-3.10Q/Rev. 1/ICN 0, p. 5, model validation is the process that demonstrates 
that a model is an acceptable representation of the process or system for which it is intended. 
This usually involves blind predictions of the calibrated model and comparison with independent 
data. The ultimate purpose of drift-scale seepage models is to predict seepage under natural flow 
conditions, which involve generally lower percolation fluxes over longer time periods than those 
encountered during a liquid-release test. Since no seepage data under natural percolation are 
currently available, the SCM cannot be validated. However, the SCM can be partly validated 
using seepage data from liquid-release tests different from those used in the calibration of the 
model. The prediction of transient seepage data from a local liquid-release test may be considered 
more difficult than predictions of average seepage under steady-state conditions because it 
includes additional uncertainty from the highly sensitive porosity estimate. On the other hand, 
predictions of natural seepage must be based on an extrapolation to significantly lower 
percolation rates. 
In this section, we predict seepage from four liquid-release tests and compare the results to the 
corresponding data, taking into account the prediction uncertainty. The four tests selected for 
validation are believed to be the most difficult ones to be simultaneously reproduced by the 
calibrated SCM. They were performed using a wide range of injection rates, and they show a 
seepage threshold and potentially storage effects. The majority of liquid-release tests performed 
showed zero seepage, a result that is believed to be reproducible with the calibrated SCM. 
The four tests were conducted in Interval UM 5.495.79 m as described in Section 6.2.4; the data 
are summarized in Table 7. We consider the validation successful if the data lie within the 95% 
error band calculated by the calibrated SCM. This means that the seepage calculated with the 
best-estimate parameter set is not required to match the data precisely, but with reasonable 
accuracy, where reasonable is defined by the prediction uncertainty. The question whether this 
prediction uncertainty is acceptable for future model applications does not need to be answered to 
validate the SCM. The uncertainty will simply be taken as input to the Seepage Models for PA 
and propagated through applicable PA models. A probabilistic acceptance criterion is adopted to 
ensure that prediction uncertainty is included in the validation process as well as in future model 
predictions. This should reduce misinterpretations and the misuse of the SCM. 
Several methods can be employed to assess the uncertainty of model predictions as a result of 
parameter uncertainty. When computationally feasible, Monte Carlo simulations (Finsterle 1999, 
pp. 7678) are the method of choice because they automatically account for nonlinearities in the 
model. A simplified linear uncertainty-propagation analysis (Finsterle 1999, pp. 7476) can be 
chosen in cases where running many simulations is prohibitive. Monte Carlo simulations are 
performed for the 2D models, whereas linear uncertainty-propagation analysis is applied for the 
3D models.

 
 59 
For the Monte Carlo simulations using the 2D models, a standard deviation of 0.1 is assigned to 
log(1/ a), log( f), and log(k). This value represents a 25% uncertainty in each of the parameters, 
which are assumed to be uncorrelated. The parameter uncertainty is slightly increased over the 
standard deviation given in Table 10 to account for the fact that the prediction is performed at a 
different location. Moreover, permeability is included as an additional uncertain parameter. 
Note that these small standard deviations lead to relatively small prediction uncertainties, making 
it difficult to meet the validation acceptance criteria. The standard deviations should be 
appropriately increased for probabilistic seepage predictions that involve a change in conditions 
such as a different location or unit with potentially changed formation characteristics, changes in 
spatial or temporal scale, or changes in governing processes (e.g., unmodeled thermal or 
geochemical effects). Heterogeneities that are not explicitly represented in the model or other 
conceptual uncertainties should also be accounted for by increasing parameter standard deviations 
in probabilistic seepage predictions. 
The histogram of the seepage predictions from four liquid-release tests for the 2D homogeneous 
and heterogeneous models are shown in Figures 20 and 21, respectively. The prediction with the 
best-estimate parameter set (see Table 10) is shown as a thin red line, whereas the long shaded bar 
indicates the measured value (see Table 7). For the 2D homogeneous model, the measured value 
is always within the range predicted by the Monte Carlo simulations, i.e., a correct statement 
regarding seepage was made with the calibrated SCM. 
The 2D heterogeneous model overpredicts seepage for the first two tests of the series. Predictions 
made with a heterogeneous model should include multiple realizations of the heterogeneous 
permeability field. Probabilistic simulations with multiple stochastic realizations are beyond the 
scope of this study.

 
 60 
NOTE: The seepage mass predicted with the best-estimate parameter set is shown as a thin red line; the 
measured value is represented by the long shaded bar. 
Figure 20. Histograms of Model Predictions with 2D Homogeneous Seepage Calibration 
Model 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test 4 Niche 3650 
two-dimensional 
homogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test #2 12-4-97 
two-dimensional 
homogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test #1 1-9-98 
two-dimensional 
homogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test #1 2-11-98 
two-dimensional 
homogeneous 
(based on DTN: LB980001233124.003 and on data submitted with this AMR under DTN: LB990831012027.001)

 
 61 
NOTE: The seepage mass predicted with the best-estimate parameter set is shown as a thin red line; the 
measured value is represented by the long shaded bar. 
Figure 21. Histograms of Model Predictions with 2D Heterogeneous Seepage Calibration 
Model 
The seepage predictions with the 3D homogeneous model are very accurate, i.e., the measured 
value is very close to the simulation result and always within the error band (see Figure 22). The 
uncertainties of the model predictions were calculated on the basis of a linearity and normality 
assumption with a very small parameter standard deviation of 0.05 for log(1/ a) and log( f), which 
corresponds to as little as 12% variation in these estimates. The standard deviations are chosen 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test 4 Niche 3650 
two-dimensional 
heterogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test #2 12-4-97 
two-dimensional 
heterogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
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30 
40 
50 
Frequency 
Test #1 1-9-98 
two-dimensional 
heterogeneous 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Seepage Mass 
0 
10 
20 
30 
40 
50 
Frequency 
Test #1 2-11-98 
two-dimensional 
heterogeneous 
(based on DTN: LB980001233124.003 and on data submitted with this AMR under DTN: LB990831012027.001)

 
 62 
smaller than those used for the 2D models because the estimation uncertainties were also smaller 
(see Table 10). 
The 3D heterogeneous model also meets the validation criteria with the exception of the first 
event, Test 4 Niche 3650, where seepage was underpredicted (see Figure 23). As in the 2D 
heterogeneous case, the additional uncertainty resulting from multiple realizations of random 
permeability fields is not included here. 
NOTE: The seepage mass predicted with the best-estimate parameter set is shown as a cross; the 
measured value is represented by a square; 95% error bars are determined by linear 
uncertainty-propagation analysis. 
Figure 22. Validation of 3D Homogeneous Seepage Calibration Model 
0 1 2 3 4 
Test 
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Test 4 Niche 3650 
Test #2 12-4-97 
Test #1 1-9-98 
Test #1 2-11-98 
Validation Tests Interval UM 5.49-5.79 m 
three-dimensional 
homogeneous 
(based on DTN: LB980001233124.003 and on data submitted with this AMR 
under DTN: LB990831012027.001)

 
 63 
NOTE: The seepage mass predicted with the best-estimate parameter set is shown as a cross; the 
measured value is represented by a square; 95% error bars are determined by linear uncertainty 
propagation analysis 
Figure 23. Validation of 3D Heterogeneous Seepage Calibration Model 
In summary, the model predictions are consistent with the measured seepage mass in most cases, 
despite rigorous assumptions regarding the uncertainty of the input parameters. The 
homogeneous models unconditionally meet the stringent validation criteria. Since only a single 
realization of the random permeability field was used to test the heterogeneous models, the 
predictions are outside the 95% error band in certain test events. Multiple realizations should be 
generated, which will provide a larger, more realistic spread of seepage predictions. Furthermore, 
blind predictions of future long-term seepage tests should be made and compared to the data to 
gain additional confidence into the SCM prediction capability. Instructions for reproducing the 
simulations discussed in this section can be found in SN YMP-LBNL-SAF-2, pp. 27. 
6.6 SEEPAGE THRESHOLD PREDICTIONS 
Steady-state simulations are performed to calculate seepage for a large range of percolation 
fluxes. The percolation flux below which no seepage occurs is termed seepage threshold. Note 
that the percolation fluxes examined here are significantly higher than the average infiltration rate 
considered reasonable at Yucca Mountain. These fluxes can be interpreted as being local and 
relevant to drift-scale studies. They are assumed to be significantly larger than average infiltration 
or percolation fluxes as a result of flow channeling effects (Birkholzer and Tsang 1997). The 
0 1 2 3 4 
Test 
0 
0.1 
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0.3 
0.4 
0.5 
0.6 
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0.9
1 
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X X 
X 
X 
Test 4 Niche 3650 
Test #2 12-4-97 
Test #1 1-9-98 
Test #1 2-11-98 
Validation Tests Interval UM 5.49-5.79 m 
three-dimensional 
heterogeneous 
(based on DTN: LB980001233124.003 and on data submitted with this AMR 
under DTN: LB990831012027.001)

 
 64 
development and characteristics of such weeps are not investigated here; we simply extend our 
seepage predictions to very high percolation fluxes to cover potential channeling effects. Also 
note that the seepage threshold and seepage percentages determined here are for a niche with a 
circular, smooth ceiling (see Figure 18), located in the middle nonlithophysal zone of the Topopah 
Spring welded tuffs, in an area of relatively competent rock mass and low fracture density. 
Extrapolations to other drift geometries and hydrogeologic units are not valid. 
Figures 24 and 25 show the seepage percentages as a function of percolation flux as predicted by 
the 2D homogeneous and heterogeneous models, respectively. The results with the best-estimate 
parameter set (see Table 10) are shown as a solid line, revealing a seepage threshold of 3,150 and 
1,000 mm/year, respectively. It is interesting to note that a new seepage location becomes 
available for percolation fluxes higher than 5,000 mm/year, leading to a kink in the predicted 
seepage percentage for the heterogeneous model (Figure 25). 
For the 2D models, Monte Carlo simulations were performed; the results are depicted as short 
dashes. The capillary strength parameter 1/ a and absolute permeability k are assumed 
uncorrelated and log-normally distributed with a standard deviation of 0.1. Percolation flux is 
sampled uniformly between 1 and 10,000 mm/year. The uniform distribution is not supposed to 
reflect the assumption about percolation uncertainty, but is chosen to cover a wide range of fluxes 
as part of a sensitivity analysis. The results would have to be weighted with the assumed 
percolation probability before making any probabilistic statements about drift seepage. 
The 3D homogeneous and heterogeneous model results (see Figures 26 and 27) yield a seepage 
threshold of approximately 700 and 250 mm/year, respectively. No Monte Carlo simulations 
could be performed to assess prediction uncertainty. Future seepage studies should include Monte 
Carlo simulations in which not only parameters are sampled from a distribution, but also a large 
number of random permeability fields are generated. 
The Monte Carlo simulations performed with the 2D models and the implied variability and 
uncertainty in the 3D models indicate significant uncertainty in seepage percentage predictions. 
Nevertheless, there seems to be a low seepage probability for percolation fluxes below 100 mm/ 
year. The calculation of this probability is beyond the scope of this AMR. 
Instructions for reproducing the simulations discussed in this section can be found in 
SN YMP-LBNL-SAF-2, pp. 1015.

 
 65 
NOTE: Solid line shows result obtained with best-estimate parameter set; short dashes depict Monte 
Carlo simulation results. 
Figure 24. Seepage Percentage as a Function of Percolation Flux for 2D Homogeneous 
Model 
102 103 104 
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Two-Dimensional 
Homogeneous 
Seepage Threshold 
Monte Carlo Simulations 
Best-Estimate 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 66 
NOTE: Solid line shows result obtained with best-estimate parameter set; short dashes depict Monte 
Carlo simulation results. 
Figure 25. Seepage Percentage as a Function of Percolation Flux for 2D Heterogeneous 
Model 
102 103 104 
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Two-Dimensional 
Heterogeneous 
Seepage Threshold 
Monte Carlo Simulations 
Best-Estimate 
(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 67 
Figure 26. Seepage Percentage as a Function of Percolation Flux for 3D Homogeneous 
Model 
Figure 27. Seepage Percentage as a Function of Percolation Flux for 3D Heterogeneous 
Model 
102 103 104 
Percolation Flux [mm/year] 
-10
0 
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50 
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Three-Dimensional 
Homogeneous 
(based on data submitted with this AMR under DTN: LB990831012027.001) 
102 103 104 
Percolation Flux [mm/year] 
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Three-Dimensional 
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(based on data submitted with this AMR under DTN: LB990831012027.001)

 
 68 
6.7 ALTERNATIVE CONCEPTUAL MODELS AND SENSITIVITY ANALYSES 
6.7.1 Alternative Conceptual Models 
Using a discrete fracture network model (DFNM) as an alternative to the fracture continuum 
model presented here is a valid option that should be studied in detail. The DFNM has the 
advantage of being intuitively more appropriate for seepage predictions. The reasons for not using 
a DFNM can be summarized as follows: 
1. A very large amount of geometric and unsaturated hydrologic property data would be 
required for the development of a defensible DFNM. While part of the geometric 
information can be obtained from fracture mappings, the description of the network 
remains incomplete and potentially biased. Moreover, unsaturated hydrologic parameters 
on the scale of individual fractures would be required, along with conceptual models and 
simplifying assumptions regarding unsaturated flow within fractures and across fracture 
intersections. The required data basis is not available. As a result, the cumulative effect of 
all the input uncertainties is likely to outweigh the apparent advantage of a detailed 
representation of the fracture network. 
2. Considering a DFNM more appropriate for seepage predictions is based on the premise 
that all physical processes relevant for seepage are well understood, well characterized, 
and appropriately described in the model, and that the accurate geometric representation 
of the fracture network is the only aspect that needs refinement. We believe that the 
complexity of seepage cannot be described, characterized, and simulated on the scale of 
individual water droplets. However, we maintain that the effects can be reasonably well 
accounted for in a model that acknowledges its limitations, and for which effective 
parameters are determined based on seepage-relevant data. 
3. The incorporation of a DFNM into the current framework of nested continuum models 
would be problematic. 
4. The appropriateness of using a fracture continuum model for the prediction of effective 
seepage quantities such as seepage percentage was demonstrated in Section 5.3 of this 
AMR. 
5. To our knowledge, no sophisticated fracture network code is currently available that 
would be capable of simulating unsaturated flow through complex, heterogeneous, threedimensional 
fracture networks and seepage into underground openings. 
6. The FCM can be developed based on less information than would be required for the 
development of a DFNM. 
In general, the choice of a conceptual model for seepage predictions should be based on a careful 
consideration of the study objectives, the available data basis in comparison with the data needs, 
the uncertainties in the input parameters and the corresponding prediction uncertainties, as well as 
computational aspects.

 
 69 
6.7.2 Sensitivity Analysis 
This AMR is concerned with the development and calibration of the Seepage Calibration Model 
(SCM). Extensive sensitivity analyses for seepage into drifts of varying geometry and flow 
conditions will be performed with the Seepage Model for PA. Nevertheless, many simulations 
were performed during the calibration process and to generate distributions of model output 
during validation and seepage threshold predictions. These simulations provided insight into the 
variability of the model result as parameters are changed within the range of their expected 
uncertainty. The results of these extensive sensitivity analyses were visualized and discussed in 
Sections 6.5 and 6.6.

 
 70 


 
 71 
7. CONCLUSIONS 
2D and 3D homogeneous and heterogeneous versions of the Seepage Calibration Model (SCM) 
were developed based on post-excavation air-permeability data, which showed little spatial 
correlation (see Sections 6.2 and 6.3). The SCM models were calibrated against seepage data 
from liquid-release tests performed at various injection rates and with different inactive periods 
between individual test events. Effective values for the capillary strength parameter 1/ a and 
porosity f were determined (Section 6.4). Permeability was fixed at the values estimated from the 
air-injection tests. The inverse modeling results can be summarized as follows: 
1. While zero seepage would be predicted with an uncalibrated model using initial 
parameter values (see Table 8), the seepage data were reasonably well matched by the 
calibrated models considered in this study (see Table 9). 
2. The capillary strength parameter 1/ a is reduced relative to the initial value. The smallest 
value was obtained with the 3D homogeneous model, and the highest value was 
estimated for the 2D heterogeneous model (see discussion of Table 10). 
3. It was necessary to estimate porosity as a parameter representing the storage capacity of 
the formation despite storage effects being of little concern for seepage predictions under 
natural, near-steady-state conditions. Liquid-release tests should be designed as longterm 
experiments with a sufficient amount of water being released. The transient and 
steady-state seepage rate should be measured. In such an experiment, porosity, which 
does not affect steady-state seepage predictions, becomes insensitive, i.e., it does not 
need to be determined from the liquid-release test data. This greatly reduces the 
uncertainty of the concurrently estimated parameters. On the other hand, flow diversion 
around the niche becomes important, potentially enabling the estimation of seepagerelevant 
permeability values on the scale of a drift. If possible, one should try to capture 
water near the spring line of the drift as independent evidence of flow diversion. 
4. The differences and similarities in the estimated parameter sets can be explained and 
related to the structure of the respective conceptual models, illustrating the need to 
determine and use model-related parameters. As a result of effective parameter 
estimation, all models are able to reproduce the same effective behavior. 
The SCM was tested in a validation exercise against liquid-release test data that were not used for 
the calibration of the model (Section 6.5). A probabilistic acceptance criterion was adopted to 
ensure that prediction uncertainty is included in the validation process as well as in future model 
predictions. Simulations of four liquid-release tests were performed, and the uncertainties of the 
predicted seepage values were determined. The model predictions were consistent with the 
measured seepage mass in most cases. Blind predictions of future long-term seepage tests should 
be made and compared to the data to gain additional confidence into the SCM prediction 
capability. 
Seepage threshold was determined and sensitivity studies were performed (Section 6.6). The 
calibrated SCM predicts a low seepage probability for a wide range of percolation fluxes. 
Detailed seepage predictions will be performed with the Seepage Model for PA.

 
 72 
The calibration, validation, and prediction studies reported in this AMR demonstrate that: 
1. The methodology outlined in this AMR yields drift-scale seepage models that are capable 
of reproducing and predicting seepage data from liquid-release tests (Sections 6.4 and 
6.5). 
2. The parameters estimated by calibrating the SCM against liquid-release test data are (a) 
seepage related and (b) model dependent (Sections 5.3 and 6.4). 
3. Seepage predictions performed with a homogeneous SCM are consistent with those of a 
heterogeneous fracture continuum model (Section 6.6). Predictions with a fracture 
continuum model are consistent with simulated data from a model that displays discrete 
flow and seepage behavior (Section 5.3). 
A homogeneous model is capable of determining integral measures such as seepage percentage. If 
the distribution of dripping locations is requested in addition to average seepage rates, a 
heterogeneous or discrete fracture network model must be developed. Higher model 
sophistication comes at the expense of increased data needs and often leads to the need to estimate 
a large number of strongly correlated parameters (i.e., overparameterization), which results in 
reduced prediction reliability. Extrapolations and applications of the calibrated model to different 
locations, units, time scales, and processes must be performed with great care and may require the 
development of more sophisticated models, including an extended characterization effort to 
satisfy additional or changed data needs. 
A consistently high seepage threshold on the order of 200 mm/year or more was predicted. These 
estimates remain uncertain and are only valid for seepage under ambient temperature conditions 
into an uncollapsed opening with a circular, smooth ceiling, located in the middle nonlithophysal 
zone of the Topopah Spring welded tuffs, in an area of relatively competent rock mass and low 
fracture density. Extrapolations to other drift geometries and hydrogeologic units are not valid. 
All software routines as well as input and output files pertinent to this AMR (see Attachment II) 
were submitted to the TDMS under DTN: LB990831012027.001. All input data residing in the 
TDMS were designated To Be Verified (TBV) until further verification. It is anticipated that 
the data, which were previously considered Q, will remain Q after the re-verification process is 
complete. The status of all the inputs for this AMR is available by referring to the DIRS database; 
the most recent version of the DIRS is included in Attachment I.

 
 73 
8. REFERENCES 
8.1 DOCUMENTS CITED 
Birkholzer, J. and Tsang, C.F. 1997. Solute Channeling in Unsaturated Heterogeneous Porous 
Media. Water Resources Research, 33 (10), 22212238. Washington, D.C.: American 
Geophysical Union. TIC: 235675. 
Carrera, J. and Neuman, S.P. 1986. Estimation of Aquifer Parameters Under Transient and 
Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information. 
Water Resources Research, 22 (2), 199210. Washington, D.C.: American Geophysical Union. 
TIC: 245915. 
CRWMS M&O (Civilian Radioactive Waste Management System Management & Operating 
Contractor) 1999a. M&O Site Investigations. Activity Evaluation. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.19990317.0330. 
CRWMS M&O 1999b. M&O Site Investigations. Activity Evaluation. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL. 19990928.0224. 
CRWMS M&O 1999c. Analysis & Modeling Development Plan (DP) for U0080 Seepage 
Calibration Model and Seepage Testing Data, Rev 00. TDP-NBS-HS-000005. Las Vegas, 
Nevada: CRWMS M&O. ACC: MOL.19990826.0109. 
Deutsch, C.V. and Journel, A.G. 1992. GSLIBGeostatistical Software Library and Users Guide. 
New York, New York: Oxford University Press. TIC: 224174. 
Dyer, J.R. 1999. Revised Interim Guidance Pending Issuance of New U.S. Nuclear Regulatory 
Commission (NRC) Regulations (Revision 01, July 22, 1999), for Yucca Mountain, Nevada. 
Letter from J.R. Dyer (DOE) to D.R. Wilkins (CRWMS M&O), September 9, 1999, OL&RC:SB- 
1714, with enclosure, Interim Guidance Pending Issuance of New U.S. Nuclear Regulatory 
Commission (NRC) Regulations (Revision 01). ACC: MOL.19990910.0079. 
Finsterle, S. 1997. ITOUGH2 Command Reference, Version 3.1. LBNL-40041. Berkeley, 
California: Lawrence Berkeley National Laboratory. ACC: MOL.19981008.0015. 
Finsterle, S. 1998. ITOUGH2 V3.2 Verification and Validation Report. LBNL-42002. Berkeley, 
California: Lawrence Berkeley National Laboratory. ACC: MOL.19981008.0014. 
Finsterle, S. 1999. ITOUGH2 Users Guide. LBNL-40040. Berkeley, California: Lawrence 
Berkeley National Laboratory. TIC: 243018. 
LeCain, G.D. 1995. Pneumatic Testing in 45-Degree-Inclined Boreholes in Ash-Flow Tuff Near 
Superior, Arizona. Water-Resources Investigations Report 95-4073. Denver, Colorado: U.S. 
Geological Survey. TIC: 221220.

 
 74 
Leverett, M.C. 1941. Capillary Behavior in Porous Solids. AIME Transactions, Tulsa 
Meeting, October 1940, 142, 152169. New York, New York: American Institute of Mining, 
Metallurgical, and Petroleum Engineers. TIC: 240680. 
Luckner, L.; van Genuchten, M.T.; and Nielsen, D.R. 1989. A Consistent Set of Parametric 
Models for the Two-Phase Flow of Immiscible Fluids in the Subsurface. Water Resources 
Research, 25 (10), 21872193. Washington, D.C.: American Geophysical Union. TIC: 224845. 
Pruess, K. 1991. Grid Orientation and Capillary Pressure Effects in the Simulation of Water 
Injection into Depleted Vapor Zones. Geothermics, 20 (5-6), 257277. Elmsford, New York: 
Pergamon Press. TIC: 245139. 
Pruess, K. 1999. A Mechanistic Model for Water Seepage through Thick Unsaturated Zones in 
Fractured Rocks of Low Matrix. Water Resources Research, 35 (4), 10391051. Washington, 
D.C.: American Geophysical Union. TIC: 244913. 
Richards, L.H. 1931. Capillary Conduction of Liquids through Porous Media. Physics, 1, 218 
233. Washington, D.C.: American Physical Society. TIC: 225383. 
Wang, J.S.Y. and Elsworth, D. 1999. Permeability Changes Induced by Excavation in Fractured 
Tuff. Rock Mechanics for Industry, Proceedings of the 37th U.S. Rock Mechanics Symposium, 
Vail, Colorado, June 6-9, 1999, 2, 751757. Alexandria, Virginia: The American Rock 
Mechanics Association. TIC: 245246. 
Wemheuer, R.F. 1999. First Issue of FY00 NEPO QAP-2-0 Activity Evaluations. Interoffice 
correspondence from R.F. Wemheuer (CRWMS M&O) to R.A. Morgan (CRWMS M&O), 
October 1, 1999, LV.NEPO.RTPS.TAG.10/99-155, with attachments, Activity Evaluation for 
Work Package #1401213UM1. ACC: MOL.19991028.0162. 
Software Cited 
Software Code: iTOUGH2 V.4.0. STN: 10003-4.0-00. 
Software Code: GSLIB Module SISIM V.1.203. STN: 10001-1.0MSISIMV1.203-00. 
Software Code: GSLIB Module GAMV2 V.1.201. STN: 10087-1.0MGAMV2V1.201-00. 
Software Code: EXT V.1.0. STN: 10047-1.0-00. 
Macro/Routine: MoveMesh V.1.0. ACC: MOL.19990721.0552. 
Macro/Routine: AddBound V.1.0. ACC: MOL.19990721.0553. 
Macro/Routine: Perm2Mesh V.1.0. ACC: MOL.19990721.0554. 
Macro/Routine: DelMatrix V.1.0. ACC: MOL.19990721.0555. 
Macro/Routine: Eos9Eos3 V.1.0. ACC: MOL.19990721.0556.

 
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Macro/Routine: CutNiche V.1.1. ACC: MOL.19990721.0557. 
Macro/Routine: userobs V.1.01. ACC: MOL.19990721.0558. 
8.2 STANDARDS, REGULATIONS AND PROCEDURES CITED 
64 FR (Federal Register) 8640. Disposal of High-Level Radioactive Waste in a Proposed 
Geologic Repository at Yucca Mountain. Proposed rule 10 CFR 63. Readily available. 
AP-3.10Q, Rev. 1, ICN 0. Analysis and Models. Washington, D.C.: U.S. Department of Energy, 
Office of Civilian Radioactive Waste Management. ACC: MOL.19990702.0314. 
AP-SI.1Q, Rev. 1, ICN 0. Software Management. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: MOL.19990630.0395. 
DOE (U.S. Department of Energy) 1998. Quality Assurance Requirements and Description. 
DOE/RW-0333P, REV 8. Washington D.C.: DOE OCRWM. ACC: MOL.19980601.0022. 
QAP-2-0, Rev. 5. Conduct of Activities. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.19980826.0209. 
8.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
DTN: LB980001233124.002. Air Permeability Testing in Niches 3566 and 3650. Submittal 
date: 04/23/1998. 
DTN: LB980001233124.003. Liquid Release Tests Performed to Determine if a Capillary 
Barrier Exists in Niches 3566 and 3650. Submittal date: 04/23/1998. 
DTN: LB997141233129.001. Calibrated Basecase Infiltration 1-D Parameter Set for the UZ 
Flow and Transport Model, FY99. Submittal date: 07/21/1999. 
8.4 AMR OUTPUT DATA LISTED BY DATA TRACKING NUMBER 
DTN: LB990831012027.001. Input to Seepage Calibration Model AMR U0080. Submittal date: 
08/26/1999. 
8.5 SUPPORTING BIBLIOGRAPHY 
Birkholzer, J.; Li, G.; Tsang, C.F.; and Tsang, Y. 1999. Modeling Studies and Analysis of 
Seepage into Drifts at Yucca Mountain. Journal Of Contaminant Hydrology, 38 (1-3), 349384. 
Amsterdam, The Netherlands: Elsevier Science. TIC: 244160. 
Birkholzer, J.; Li, G.; Tsang, C.F.; and Tsang, Y. 1998. Drift Scale Modeling: Studies of Seepage 
into a Drift. Milestone Report SP4CKLM4. Berkeley, California: Lawrence Berkeley National 
Laboratory. ACC: MOL.19980825.0204. 

 
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Birkholzer, J.; Li, G.; Tsang, C.F.; Tsang, Y; Trautz, R.C.; and Wang, J. 1998. Drift Scale 
Modeling: Studies of Seepage into a Drift. Milestone Report SP3CKLM4. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19980908.0282. 
Birkholzer, J.T.; Tsang, C.F.; Tsang, Y.W.; and Wang, J.S.Y. 1996. Drift Scale Modeling, FY96 
Project Report. Chapter 4 of Multi-Scale Modeling to Evaluate Scaling Issues, Percolation Flux 
and Other Processes for PA Recommendations. Altman S.J. et al., eds. Milestone Report T6540. 
Albuquerque, New Mexico: Sandia National Laboratories. ACC: MOL.19970320.0116. 
Cook, P.J.; Trautz, R.C.; and Wang, J.S.Y. 1998. Compilation of Borehole Permeability Values 
from the Pre- and Post-Excavation Air Injection Tests. Chapter 3 of Drift Seepage and Niche 
Moisture Study: Phase 1 Report on Flux Threshold Determination, Air Permeability Distribution, 
and Water Potential Measurement, Version 1.0. Wang, J.S.Y. et al. Milestone Report 
SPC315M4. Berkeley, California: Lawrence Berkeley National Laboratory. ACC: 
MOL.19980806.0713. 
Cook, P.J.; Trautz, R.C.; and Wang, J.S.Y. 1997. Cross-hole Pneumatic Tests of Fracture Flow 
Paths. Chapter 2 of Field Testing and Observation of Flow Paths in Niches, Phase 1 Status 
Report of the Drift Seepage Test and Niche Moisture Study. Wang, J.S.Y et al. Milestone Report 
SPC314M4. Berkeley, California: Lawrence Berkeley National Laboratory. ACC: 
MOL.19980121.0078. 
Finsterle, S. and James, A.L. 1998. Sensitivity Analysis of Drift Seepage with Two-Dimensional 
and Three-Dimensional Models. Chapter 3 of Testing and Modeling of Seepage into Drift: Input 
of Exploratory Study of Facility Seepage Test Results to Unsaturated Zone Models. Wang, J.S.Y. 
et al. Milestone Report SP33PLM4. Berkeley, California: Lawrence Berkeley National 
Laboratory. ACC: MOL.19980508.0004. 
Finsterle, S.; James, A.L.; Ritcey, A.C.; and Wang, J.S.Y. 1998. Model Prediction of Cross-Drift 
Impact on Moisture. Milestone Report SP33T1M4. Berkeley, California: Lawrence Berkeley 
National Laboratory. ACC: MOL.19980507.0177. 
Finsterle, S. 1997. ITOUGH2 Sample Problems. Report LBNL-40042. Berkeley, California: 
Lawrence Berkeley National Laboratory. TIC: 244009. 
Flint, A. L. and Flint, L. E. 1997. Niche Moisture Analysis. Chapter 5 of Field Testing and 
Observation of Flow Paths in Niches, Phase 1 Status Report of the Drift Seepage Test and Niche 
Moisture Study. Wang, J.S.Y. et al. Milestone Report SPC314M4. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19980121.0078. 
Flint, L.E. 1998. Characterization of Hydrogeologic Units Using Matrix Properties, Yucca 
Mountain, Nevada. Water Resources Investigations Report 974243. Denver, Colorado: U.S. 
Geological Survey. ACC: MOL.19980429.0512. 
Geller, J.T.; Su, G.; and Pruess, K. 1996. Preliminary Studies of Water Seepage through Rough- 
Walled Fractures. Report LBNL-38810. Berkeley, California: Lawrence Berkeley National 
Laboratory. TIC: applied for.

 
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James, A.L. and Finsterle, S. 1997. Fracture Flow Models for Niche Liquid Release Tests. 
Chapter 4 of Field Testing and Observation of Flow Paths in Niches, Phase 1 Status Report of the 
Drift Seepage Test and Niche Moisture Study. Wang, J.S.Y. et al. Milestone Report SPC314M4. 
Berkeley, California: Lawrence Berkeley National Laboratory. ACC: MOL.19980121.0078. 
Hvorslev, M.J. 1951. Time Lag and Soil Permeability in Ground-Water Observations. U.S. 
Army Corps of Engineers Bulletin, 36. Vicksburg, Mississippi: U.S. Army Corps of Engineers. 
TIC: 238956. 
Li, G.; Tsang, C.F.; and Birkholzer, J. 1998. Calculation of Drift Seepage for Alternative 
Emplacement Designs. BB0000000-01717-0200-00011. Berkeley, California: Lawrence 
Berkeley National Laboratory. ACC: MOL.19990323.0138 
Philip, J.R. 1989. The Seepage Exclusion Problem for Sloping Cylindrical Cavities. Water 
Resources Research, 25 (6), 14471448. Washington, D.C.: American Geophysical Union. TIC: 
239729. 
Philip, J.R. 1989. Asymptotic Solutions for the Seepage Exclusion Problem for Elliptic- 
Cylindrical, Spheroidal, and Strip- and Disc-Shaped Cavities. Water Resources Research, 25 
(7), 15311540. Washington, D.C.: American Geophysical Union. TIC: 239858. 
Philip, J.R.; Knight, J.H. and Waechter, R.T. 1989. Unsaturated Seepage and Subterranean 
Holes: Conspectus, and Exclusion Problem for Cylindrical Cavities. Water Resources Research, 
25 (1), 1628. Washington, D.C.: American Geophysical Union. TIC: 239117. 
Philip, J.R.; Knight, J.H; and Waechter, R.T. 1989. The Seepage Exclusion Problem for 
Parabolic and Paraboloidal Cavities. Water Resources Research, 25 (4), 605618. Washington, 
D.C.: American Geophysical Union. TIC: 239116. 
Pruess, K. 1998. On Water Seepage and Fast Preferential Flow in Heterogeneous, Unsaturated 
Rock Fractures. Journal of Contaminant Hydrology, 30 (34), 333362. Amsterdam, The 
Netherlands: Elsevier Science. TIC: 238921. 
Pruess, K.; Faybishenko, B.; and Bodvarsson, G.S. 1997. Alternative Concepts and Approaches 
for Modeling Unsaturated Flow and Transport in Fractured Rocks. Chapter 5 of The Site-Scale 
Unsaturated-Zone Model of Yucca Mountain, Nevada, for the Viability Assessment. Bodvarsson, 
G.S.; Bandurraga, T.M.; and Wu, Y.S., eds. LBNL-40376. Berkeley, California: Lawrence 
Berkeley National Laboratory. ACC: MOL.19971014.0232. 
Rasmussen, T.C.; Evans, D.D.; Sheets, P.J.; and Blanford, J.H. 1993. Permeability of Apache 
Leap Tuff: Borehole and Core Measurements Using Water and Air. Water Resources Research, 
29 (7), 19972006. Washington, D.C.: American Geophysical Union. TIC: 245278. 
Salve, R. and Wang, J.S.Y. 1998. Compilation of Water Potentials Measured in the Niches. 
Chapter 4 of Drift Seepage Test and Niche Moisture Study: Phase 1 Report on Flux Threshold 
Determination, Air Permeability Distribution, and Water Potential Measurement. Wang, J.S.Y. et 

 
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al. Milestone Report SPC315M4. Berkeley, California: Lawrence Berkeley National 
Laboratory. ACC: MOL.19980806.0713. 
Trautz, R.C.; Wang, J.S.Y.; and Cook, P.J. 1998. Compilation of Seepage Results From the Pre- 
And Post-Excavation Liquid-Release Tests. Chapter 2 of Drift Seepage Test and Niche 
Moisture Study: Phase 1 Report on Flux Threshold Determination, Air Permeability Distribution, 
and Water Potential Measurement. Wang, J.S.Y. et al. Milestone Report SPC315M4. Berkeley, 
California: Lawrence Berkeley National Laboratory. ACC: MOL.19980806.0713. 
Trautz, R.C.; Cook, P.J.; and Wang, J.S.Y. 1997. Liquid Migration Along Fracture Flow Paths. 
Chapter 3 of Field Testing and Observation of Flow Paths in Niches. Milestone Report 
SPC314M4. Wang, J.S.Y. et al. Berkeley, California: Lawrence Berkeley National Laboratory. 
ACC: MOL.19980121.0078. 
Tsang, C.F.; Li, G.; Birkholzer, J.T.; and Tsang, Y.W. 1998 . Abstraction Modeling of Drift 
Seepage for TSPA/VA. Milestone Report SLX01LB4. Berkeley, California: Lawrence Berkeley 
National Laboratory. ACC: MOL.19980730.0607. 
Tsang, C.F. 1997. Drift-Scale Heterogeneous Permeability Field Conditioned to Field Data. 
Milestone Report SP331BM4. Berkeley, California: Lawrence Berkeley National Laboratory. 
ACC: MOL.19971125.0915. 
Tsang, C.F.; Birkholzer, J.T.; and Li, G. 1997. Drift Scale Modeling: Progress in Studies of 
Seepage into a Drift. Milestone Report SP331CM4. Berkeley, California: Lawrence Berkeley 
National Laboratory. ACC: MOL.19971204.0420. 
Tsang, C.F.; Birkholzer, J.T.; Li, G; and Tsang, Y.W. 1997. Drift Scale Modeling: Studies of 
Seepage into a Drift. Milestone Report SP331DM4. Berkeley, California: Lawrence Berkeley 
National Laboratory. ACC: MOL.19971208.0369. 
Tsang, C.F.; Li, G.; Birkholzer, J.T.; and Tsang, Y.S. l997. Drift Scale Abstraction: Evaluation of 
Seepage into Drifts. Milestone Report NWD981. Berkeley, California: Lawrence Berkeley 
National Laboratory. ACC: MOL.19980423.0388. 
Tsang, C.F.; Li, G.; Birkholzer, J.; and Tsang, Y. S 1997. Drift Scale Abstraction: Additional 
Results on Seepage into Drifts and Model Testing against Niche Liquid Release Test. Milestone 
Report NWD982. Berkeley, California: Lawrence Berkeley National Laboratory. ACC: 
MOL.19980506.0008. 
Wang, J.S.Y.; Trautz, R.C.; Salve, R.; Oldenburg, C.M.; Ahlers, C.F.; Finsterle, S.; Cook, P.J.; 
Doughty, C.; Fairley, J.P.; James, A. Hu, M.Q.; Guell, M.A.; and Freifeld, B. 1998. Progress 
Report on Fracture Flow, Drift Seepage, and Matrix Imbibition Tests in the Exploratory Studies 
Facility. Milestone Report SP33PBM4. Berkeley, California: Lawrence Berkeley National 
Laboratory. 
Wang, J.S.Y.; Trautz, R.C.; Cook, P.J.; Finsterle, S.; James, A.L.; Birkholzer J.; and Ahlers, C.F. 
1998. Testing and Modeling of Seepage into Drift: Input of Exploratory Study of Facility Seepage 

 
 79 
Test Results to Unsaturated Zone Model. Milestone Report SP33PLM4. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19980508.0004. 
Wang, J.S.Y.; Trautz, R.C.; Cook, P.J.; and Salve, R. 1998. Drift Seepage Test and Niche 
Moisture Study: Phase 1 Report on Flux Threshold Determination, Air Permeability Distribution, 
and Water Potential Measurement. Milestone Report SPC315M4. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19980806.0713. 
Wang, J.S.Y, Cook, P.; Trautz, R.; James, A.; Finsterle, S. Sonnenthal, E.; Salve, R.; Hesler, G.; 
and Flint, A. 1997. Drift Seepage Test and Niche Moisture Study: Phase 1 Progress Report on 
Data Interpretation and Model Prediction. Milestone Report SPC31DM4. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19980501.0484. 
Wang, J.S.Y., Cook, P.J.; Trautz, R.C.; Salve, R.; James, A.L.; Finsterle, S.; Tokunaga, T.K.; 
Solbau, R.; Clyde, J.; Flint, A.L.; and Flint, L.E. 1997. Field Testing and Observation of Flow 
Paths in Niches, Phase 1 Status Report of the Drift Seepage Test and Niche Moisture Study. 
Milestone Report SPC314M4. Berkeley, California: Lawrence Berkeley National Laboratory. 
ACC: MOL.19980121.0078.

 
 80 


 
 81 
9. ATTACHMENTS 
Attachment I  Document Input Reference Sheet 
Attachment II  Software Routines, Input and Output Files

 
 82 


 
 Attachment I-1 
ATTACHMENT IDOCUMENT INPUT REFERENCE SHEET 
DIRS as of the issue date of this AMR. Refer to the DIRS database for the current status of these 
inputs. 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
1. DTN: 
LB980001233124.002. 
Wang, J. Air permeability 
testing in Niches 3566 and 
3650. Submittal date: 
04/23/1998. Initial use. 
File 
S98131_ 
002, 
Rows 
247-277, 
309-339 
TBV-3094 6.2, 6.3 Post-excavation air 
permeability data for 
conditioning of Seepage 
Calibration Model 
1 N/A N/A X 
2. DTN: 
LB980001233124.003. 
Wang, J. Liquid Release 
Tests Performed to 
Determine if a Capillary 
Barrier Exists in Niches 
3566 and 3650. Submittal 
date: 04/23/1998. Initial 
Use. 
File 
S98132_ 
005, 
Rows 10- 
14, 20-23 
TBV-3093 6.2, 6.4 Liquid release test data for 
calibration and validation 
1 N/A N/A X 
3. DTN: 
LB997141233129.001. 
Wu, Y.S. Calibrated 
Basecase Infiltration 1-D 
Parameter Set for the UZ 
Flow and Transport Model 
FY99. Submittal date: 
07/21/1999. Initial use. 
IDbaseca 
se 
Rlwodis. 
xls. 
TBV-3095 6.3 Fracture properties for 
Seepage Calibration Model 
1 N/A N/A X

 
 Attachment I-2 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
4. Birkholzer, J. and Tsang, 
C.F. 1997. Solute 
Channeling in Unsaturated 
Heterogeneous Porous 
Media. Water Resources 
Research, 33 (10), 2221 
2238. Washington, D.C.: 
American Geophysical 
Union. TIC: 235675. 
Entire N/A - 
Reference 
only 
6.6 Channeling effect N/A N/A N/A N/A 
5. Carrera, J. and Neuman, 
S.P. 1986. Estimation of 
Aquifer Parameters Under 
Transient and Steady State 
Conditions: 1. Maximum 
Likelihood Method 
Incorporating Prior 
Information. Water 
Resources Research, 22 
(2), 199210. Washington, 
D.C.: American 
Geophysical Union. TIC: 
245915. 
205 N/A - 
Reference 
only 
6.4 Estimation covariance 
matrix 
N/A N/A N/A N/A

 
 Attachment I-3 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
6. CRWMS M&O (Civilian 
Radioactive Waste 
Management System 
Management & Operating 
Contractor) 1999a. M&O 
Site Investigations. 
Activity Evaluation. Las 
Vegas, Nevada: CRWMS 
M&O. ACC: 
MOL.19990317.0330. 
Entire N/A - 
Reference 
only 
2 Standards, Codes & 
Regulations 
N/A N/A N/A N/A 
7. CRWMS M&O 1999b. 
M&O Site Investigations. 
Activity Evaluation. Las 
Vegas, Nevada: CRWMS 
M&O. ACC: MOL. 
19990928.0224. 
Entire N/A - 
Reference 
only 
2 Standards, Codes & 
Regulations 
N/A N/A N/A N/A

 
 Attachment I-4 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
8. CRWMS M&O 1999c. 
Analysis & Modeling 
Development Plan (DP) 
for U0080 Seepage 
Calibration Model and 
Seepage Testing Data, Rev 
00. TDP-NBS-HS- 
000005. Las Vegas, 
Nevada: CRWMS M&O. 
ACC: 
MOL.19990826.0109. 
Entire N/A - 
Reference 
only 
2 Standards, Codes & 
Regulations 
N/A N/A N/A N/A 
9. Deutsch, C.V. and Journel, 
A.G. 1992. 
GSLIB.Geostatistical 
Software Library and 
Users Guide. New York, 
New York: Oxford 
University Press. TIC: 
224174. 
53-55 
151 
N/A - 
Reference 
only 
5.3, 
6.2.3 
6.3.2 
Use of modules GAMV2 
and SISIM 
N/A N/A N/A N/A

 
 Attachment I-5 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
10. Dyer, J.R. 1999. Revised 
Interim Guidance Pending 
Issuance of New U.S. Nuclear 
Regulatory Commission 
(NRC) Regulations (Revision 
01, July 22, 1999), for Yucca 
Mountain, Nevada. Letter 
from J.R. Dyer (DOE) to D.R. 
Wilkins (CRWMS M&O), 
September 9, 1999, 
OL&RC:SB-1714, with 
enclosure, Interim Guidance 
Pending Issuance of New U.S. 
Nuclear Regulatory 
Commission (NRC) 
Regulations (Revision 01). 
ACC: MOL.19990910.0079. 
Entire N/AReference 
only 
4.2 Interim Guidance N/A N/A N/A N/A 
11. Finsterle, S. 1997. 
ITOUGH2 Command 
Reference, Version 3.1. 
Report LBNL-40041. 
Berkeley, California: 
Lawrence Berkeley 
National Laboratory. 
ACC: 
MOL.19981008.0015. 
Entire N/A - 
Reference 
only 
5.2 
6.3 
Supports software use N/A N/A N/A N/A

 
 Attachment I-6 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
12. Finsterle, S. 1998. 
ITOUGH2 V3.2 
Verification and Validation 
Report. Report LBNL- 
42002. Berkeley, 
California: Lawrence 
Berkeley National 
Laboratory. ACC: 
MOL.19981008.0014. 
p. 14-15 N/A - 
Reference 
only 
5.3.2 
6.3.4 
Instructions for application 
of free-drainage boundary 
condition 
N/A N/A N/A N/A 
13. Finsterle, S. 1999. 
ITOUGH2 Users Guide. 
Report LBNL-40040. 
Berkeley, California: 
Lawrence Berkeley 
National Laboratory. TIC: 
243018. 
Entire N/A - 
Reference 
only 
5.3.4 
6.3.5 
6.5 
Objective function, 
minimization algorithm 
and error analysis 
N/A N/A N/A N/A 
14. LeCain, G. D. 1995. 
Pneumatic Testing in 45- 
Degree-Inclined Boreholes 
in Ash-Flow Tuff near 
Superior, Arizona. Water 
Resources Investigations 
Report 95-4073. Denver, 
Colorado: U.S. Geological 
Survey. ACC: 
MOL.19960416.0160. 
p. 32 N/A - 
Reference 
only 
5.3.3 Analytical solution for 
permeability from steadystate 
air-injection data. 
N/A N/A N/A N/A

 
 Attachment I-7 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
15. Leverett, M.C. 1941. 
Capillary Behavior in 
Porous Solids. AIME 
Transactions, Tulsa 
Meeting, October 1940, 
142, 152-169. New York, 
New York: American 
Institute of Mining, 
Metallurgical, and 
Petroleum Engineers. TIC: 
240680. 
Entire N/A - 
Reference 
only 
5.2 
6.3 
Leverett Scaling Rule N/A N/A N/A N/A 
16. Luckner, L.; van 
Genuchten, M.T.; and 
Nielsen, D.R. 1989. A 
Consistent Set of 
Parametric Models for the 
Two-Phase Flow of 
Immiscible Fluids in the 
Subsurface. Water 
Resources Research, 25 
(10), 21872193. 
Washington, D.C.: 
American Geophysical 
Union. TIC: 224845. 
pp. 2191- 
2192 
N/A - 
Reference 
only 
5.2 
6.3 
Relative permeability and 
capillary pressure function 
N/A N/A N/A N/A

 
 Attachment I-8 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
17. Pruess, K. 1991. Grid 
Orientation and Capillary 
Pressure Effects in the 
Simulation of Water 
Injection into Depleted 
Vapor Zones. 
Geothermics, 20 (56), 
257277. Elmsford, New 
York: Pergamon Press. 
TIC: 245139. 
pp. 272- 
274 
N/A - 
Reference 
only 
5.3.6 Description of phase 
dispersion effects 
N/A N/A N/A N/A 
18. Pruess, K. 1999. A 
Mechanistic Model for 
Water Seepage through 
Thick Unsaturated Zones 
in Fractured Rocks of Low 
Matrix. Water Resources 
Research, 35 (4), 1039 
1051. Washington, D.C.: 
American Geophysical 
Union. TIC: 244913. 
pp. 1039- 
1051 
N/A - 
Reference 
only 
5.3 Alternative conceptual 
model description 
N/A N/A N/A N/A

 
 Attachment I-9 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
19. Richards, L.H. 1931. 
Capillary Conduction of 
Liquids Through Porous 
Media. Physics, 1, 218 
233. Washington, D.C.: 
American Physical 
Society. TIC: 225383. 
pp. 218- 
233 
N/A - 
Reference 
only 
5.2 
6.3 
Concept of unsaturated 
flow through porous media 
N/A N/A N/A N/A 
20. Wang, J. S. Y., and 
Elsworth, D. 1999. 
Permeability Changes 
Induced by Excavation in 
Fractured Tuff. 
Proceedings, 37th U.S. 
Rock Mechanics 
Symposium, Rock 
Mechanics for Industry, 2, 
751757. Alexandria, 
Virginia: The American 
Rock Mechanics 
Association. TIC: 245246. 
pp. 751- 
757 
N/A - 
Reference 
only 
5.3.2 Description of excavationdisturbed 
zone around 
niches 
N/A N/A N/A N/A

 
 Attachment I-10 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
21. Wemheuer, R.F. 1999. First 
Issue of FY00 NEPO QAP-2- 
0 Activity Evaluations. 
Interoffice correspondence 
from R.F. Wemheuer 
(CRWMS M&O) to R.A. 
Morgan (CRWMS M&O), 
October 1, 1999, 
LV.NEPO.RTPS.TAG.10/99- 
155, with attachments, 
Activity Evaluation for Work 
Package #1401213UM1. 
ACC: MOL.19991028.0162. 
WP#1401 
213UM1 
N/AReference 
only 
2 Activity Evaluation N/A N/A N/A N/A 
22. Software Code: iTOUGH2 
v 4.0, STN: 10003-4.0-00 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.4, 
6.5, 6.6 
General software use N/A N/A N/A N/A 
23. Software Code: GSLIB 
Module SISIM v 1.203, 
STN: 10001- 
1.0MSISIMV1.203-00 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
24. Software Code: GSLIB 
Module GAMV2 v 1.201, 
STN: 10087- 
1.0MGAMV2V1.201-00 
Entire N/AQualified/ 
Verified/ 
Confirmed 
6.2 General software use N/A N/A N/A N/A

 
 Attachment I-11 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
25. Software Code: EXT v 1.0, 
STN: 10047-1.0-00 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6 General software use N/A N/A N/A N/A 
26. Software Code: MoveMesh 
v. 1.0. ACC: 
MOL.19990721.0552 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
27. Software Code: AddBound 
v. 1.0. ACC: 
MOL.19990721.0553. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
28. Software Code: 
Perm2Mesh v. 1.0. ACC: 
MOL.19990721.0554. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
29. Software Code: DelMatrix 
v 1.0. ACC: 
MOL.19990721.0555. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
30. Software Code: Eos9Eos3 
v 1.0. ACC: 
MOL.19990721.0556. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A

 
 Attachment I-12 
OFFICE OF CIVILIAN RADIOACTIVE WASTE MANAGEMENT 
DOCUMENT INPUT REFERENCE SHEET 
1. Document Identifier No./Rev.: 
/Rev. 00E 
Change: Title: 
Seepage Calibration Model & Seepage Testing Data 
Input Document 4. Input 
Status 
5. 
Section 
Used in 
6. Input Description 7. 
TBV/TBD 
Priority 
8. TBV Due To 
2. Technical Product Input Source 
Title and Identifier(s) with Version 
3. 
Section 
Unqual. From 
Uncontrolled 
Source 
Unconfirm 
ed 
31. Software Code: CutNiche 
v 1.1. ACC: 
MOL.19990721.0557. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
32. Software Code: userobs v 
1.01. ACC: 
MOL.19990721.0558. 
Entire N/AQualified/ 
Verified/ 
Confirmed 
5.3, 6.3 General software use N/A N/A N/A N/A 
AP-3.15Q.1 Rev. 06/30/1999

 
 Attachment II-1 
ATTACHMENT II

 
 Attachment II-2 January 2000

 
 Attachment II-3 
#! /bin/sh 
#
# file maketar ceates compressed tar file U0080.tar.Z. 
# DTN: LB990831012027.001, TDIF 308840 
#
# Contains input and output files that allow reproduction of all simulations pertinent to AMR U0080, 
# "Seepage Calibration Model and Seepage Testing Data," ANL-NBS-HS-000004, MOL.19990721.0521 
#
# To extract files from U0080_00B.tar.Z on a Unix workstation, type: 
# uncompress U0080.tar.Z; tar xvf U0080.tar 
# Requires about 150 MB of disk space. 
#
# All commands can be found in the sh.* script files; 
# consult Scientific Notebooks YMP-LBNL-SAF-1, YMP-LBNL-SAF-2, and YMP-LBNL-RCT-DSM-1. 
#
# S. Finsterle, September 1999 
#
tar -cvf - \ 
\
./maketar \ the file creating the tar file 
\ 
./Codes/AddBound.f \ FORTRAN77 source code 
./Codes/CutNiche.f \ FORTRAN77 source code 
./Codes/DelMatrix.f \ FORTRAN77 source code 
./Codes/Eos9Eos3.f \ FORTRAN77 source code 
./Codes/MoveMesh.f \ FORTRAN77 source code 
./Codes/Perm2Mesh.f \ FORTRAN77 source code 
./Codes/sisim.f \ FORTRAN77 source code 
./Codes/sisimm.f \ FORTRAN77 source code 
./Codes/sisim.inc \ include file for SISIM 
./Codes/ext.f \ FORTRAN77 source code 
./Codes/catalog.f \ FORTRAN77 source code 
./Codes/inspect.f \ FORTRAN77 source code 
./Codes/rmesh.f \ FORTRAN77 source code 
./Codes/gamv2m.f \ FORTRAN77 source code 
./Codes/gamv2.f \ FORTRAN77 source code 
./Codes/gamv2.inc \ include file for GAMV2 
./Codes/userobs.f \ link to iTOUGH2 V4.0 
./Codes/xAddBound \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xCutNiche \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xDelMatrix \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xEos9Eos3 \ Executable for Sun SPARC multiprocessor, Solaris

 
 Attachment II-4 
./Codes/xExt \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xGamv2 \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xMoveMesh \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xPerm2Mesh \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/xSisim \ Executable for Sun SPARC multiprocessor, Solaris 
./Codes/sh.compile \ Unix shell script file used to compile all codes 
\ 
./Codes/gamv2/cluster.dat \ Input data for gamv2 validation case 1 
./Codes/gamv2/gamv2.par1 \ Input parameter file for gamv2 validation case 1 
./Codes/gamv2/gamv2.par2 \ Input parameter file for gamv2 validation case 2 
./Codes/gamv2/gamv2.var1 \ Ouput variogram for gamv2 validation case 1 
./Codes/gamv2/gamv2.var2 \ Ouput variogram for gamv2 validation case 2 
./Codes/gamv2/randfield.f \ FORTRAN77 source code to generate random numbers 
./Codes/gamv2/random.dat \ Input data for gamv2 validation case 2 
\ 
./ContDisc/DFNMairk \ TOUGH2 input file air-k test with DFNM, Section 5.3.3 
./ContDisc/DFNMairki \ iTOUGH2 input file air-k test with DFNM, Section 5.3.3 
./ContDisc/DFNMairki.out \ iTOUGH2 output file air-k test with DFNM, Section 5.3.3, Figure 4 
./ContDisc/DFNMlrt \ TOUGH2 input file liquid-release test with DFNM, Section 5.3.4, Table 4 
./ContDisc/DFNMlrt.out \ TOUGH2 output file liquid-release test with DFNM, Section 5.3.4, Figure 5. 
./ContDisc/DFNMlrti \ iTOUGH2 input file liquid-release test with DFNM, Section 5.3.4, Table 4 
./ContDisc/DFNMlrti.out \ iTOUGH2 output file liquid-release test with DFNM, Section 5.3.4, Figure 6 
./ContDisc/DFNMperm \ TOUGH2 input file for saturated steady-state run with DFNM, Section 5.3.2 
./ContDisc/DFNMperm.out \ TOUGH2 output file for saturated steady-state run with DFNM, Section 5.3.2, Figure 1a 
./ContDisc/DFNMpred \ TOUGH2 input file for prediction run with DFNM, Section 5.3.7 
./ContDisc/DFNMpredi \ iTOUGH2 input file for prediction run with DFNM, Section 5.3.7 
./ContDisc/DFNMpredi.out \ iTOUGH2 output file for prediction run with DFNM, Section 5.3.7, Figure 10 
./ContDisc/DFNMseep \ TOUGH2 input file for seepage threshold determination with DFNM, Section 5.3.7 
./ContDisc/DFNMseepi \ iTOUGH2 input file for seepage threshold determination with DFNM, Section 5.3.7 
./ContDisc/DFNMseepi.out \ iTOUGH2 output file for seepage threshold determination with DFNM, Section 5.3.7, Figure 11 
./ContDisc/DFNMss \ TOUGH2 input file for steady-state run with DFNM, Section 5.3.2 
./ContDisc/DFNMss.out \ TOUGH2 output file for steady-state run with DFNM, Section 5.3.2, Figures 2, 3 
./ContDisc/DFNM.inc \ Initial condition file (output from DFNMss run), Section 5.3.2 
./ContDisc/DFNMairk.inc \ Initial condition file for DFNMairk run (converted output from DFNMss run), Section 5.3.2 
./ContDisc/FCMlrt \ TOUGH2 input file for liquid-release test with FCM, Section 5.3.6 
./ContDisc/FCMlrt.out \ TOUGH2 output file for liquid-release test with FCM, Section 5.3.6, Figures 7, 9 
./ContDisc/FCMlrti \ iTOUGH2 input file for liquid-release test with FCM, Section 5.3.6 
./ContDisc/FCMlrti.out \ iTOUGH2 output file for liquid-release test with FCM, Section 5.3.6, Figure 8 
./ContDisc/FCMpred \ TOUGH2 input file for prediction run with FCM, Section 5.3.7 
./ContDisc/FCMpredi \ iTOUGH2 input file for prediction run with FCM, Section 5.3.7 
./ContDisc/FCMpredi.tec \ iTOUGH2 output file for prediction run with FCM, Section 5.3.7, Figure 10 
./ContDisc/FCMseep \ TOUGH2 input file for seepage threshold determination with FCM, Section 5.3.7

 
 Attachment II-5 
./ContDisc/FCMseepi \ iTOUGH2 input file for seepage threshold determination with FCM, Section 5.3.7 
./ContDisc/FCMseepi.out \ iTOUGH2 output file for seepage threshold determination with FCM, Section 5.3.7, Figure 11 
./ContDisc/FCMseepMC \ TOUGH2 input file for seepage threshold determination with FCM, Section 5.3.7 
./ContDisc/FCMseepMCi \ iTOUGH2 input file for seepage threshold determination with FCM, Section 5.3.7 
./ContDisc/FCMseepMCi.tec \ iTOUGH2 output file for seepage threshold determination with FCM, Section 5.3.7, Figure 11 
./ContDisc/sh.DFNM \ Unix shell script file for all DFNM runs 
./ContDisc/sh.FCM \ Unix shell script file for all FCM runs 
\ 
./Heterogeneity/N2xyk_2-dpost.prn \ Input data for gamv2-4.par, all post-excavation data, Section 6.2.3 
./Heterogeneity/N2xyk_2-dp_tr.prn \ Input data for gamv2-8.par, truncated post-excavation data, Section 6.2.3, Table 5, Figure 16 
./Heterogeneity/gamv2-4.par \ Input parameter file for gamv2, all post-excavation data, Section 6.2.3 
./Heterogeneity/gamv2-4.var \ Output variogram from gamv2, all post-excavation data, Section 6.2.3, Figure 15 
./Heterogeneity/gamv2-8.par \ Input parameter file for gamv2, truncated post-excavation data, Section 6.2.3 
./Heterogeneity/gamv2-8.var \ Output variogram from gamv2, truncated post-excavation data, Section 6.2.3, Figures 15, 17 
./Heterogeneity/condUM442_post.dat \ Conditioning points for test UM 4.42, Section 6.3.2 
./Heterogeneity/UM442_3.par \ Input parameter file for gamv2, permeability field for test UM 4.42, Section 6.3.2 
./Heterogeneity/UM442_3.dat \ Output permeability field for test UM 4.42, Section 6.3.2 
./Heterogeneity/condUM564_post.dat \ Conditioning points for test UM 5.64, Section 6.5 
./Heterogeneity/UM564_1.par \ Input parameter file for gamv2, permeability field for test UM 5.64, Section 6.5 
./Heterogeneity/UM564_1.dat \ Output permeability field for test UM 5.64, Section 6.5 
./Heterogeneity/conditioning.dat \ Conditioning points for FCM model, Section 5.3.5 
./Heterogeneity/fract2dc.par \ Input parameter file for gamv2, FCM, Section 5.3.5 
./Heterogeneity/fract2dc.dat \ Output permeability field, FCM, Section 5.3.5 
./Heterogeneity/fract0.par \ Input parameter file for gamv2, 0-degree fracture set, Section 5.3.2 
./Heterogeneity/fract0.dat \ Output permeability field, 0-degree fracture set, Section 5.3.2 
./Heterogeneity/fract45.par \ Input parameter file for gamv2, 45-degree fracture set, Section 5.3.2 
./Heterogeneity/fract45.dat \ Output permeability field, 45-degree fracture set, Section 5.3.2 
./Heterogeneity/fract90.par \ Input parameter file for gamv2, 90-degree fracture set, Section 5.3.2 
./Heterogeneity/fract90.dat \ Output permeability field, 90-degree fracture set, Section 5.3.2 
./Heterogeneity/fractm45.par \ Input parameter file for gamv2, -45-degree fracture set, Section 5.3.2 
./Heterogeneity/fractm45.dat \ Output permeability field, -45-degree fracture set, Section 5.3.2 
./Heterogeneity/sh.2dcont \ Unix shell script file used to generate permeability field for FCM 
./Heterogeneity/sh.2dfract \ Unix shell script file used to generate permeability field for DFNM 
./Heterogeneity/var \ Dummy TOUGH2 input file used for variogram model matching, Section 6.3.2 
./Heterogeneity/vari \ iTOUGH2 input file used for variogram model matching, Section 6.3.2 
./Heterogeneity/vari.out \ iTOUGH2 input file used for variogram model matching, Section 6.3.2, Figure 17 
\ 
./LRT/ThresGS_het-2d \ TOUGH2 input file for 2d-heterogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_het-2di \ iTOUGH2 input file for 2d-heterogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_het-2di.out \ iTOUGH2 output file for 2d-het. grid search of seepage threshold, Section 6.6, Figure 25 
./LRT/ThresGS_hom-2d \ TOUGH2 input file for 2d-homogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_hom-2di \ iTOUGH2 input file for 2d-homogeneous grid search of seepage threshold, Section 6.6

 
 Attachment II-6 
./LRT/ThresGS_hom-2di.out \ iTOUGH2 output file for 2d-hom. grid search of seepage threshold, Section 6.6, Figure 24 
./LRT/ThresGS_hom-3d \ TOUGH2 input file for 3d-homogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_hom-3di \ iTOUGH2 input file for 3d-homogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_hom-3di.out \ iTOUGH2 output file for 3d-hom. grid search of seepage threshold, Section 6.6, Figure 26 
./LRT/ThresGS_het-3d \ TOUGH2 input file for 3d-heterogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_het-3di \ iTOUGH2 input file for 3d-heterogeneous grid search of seepage threshold, Section 6.6 
./LRT/ThresGS_het-3di.out \ iTOUGH2 output file for 3d-het. grid search of seepage threshold, Section 6.6, Figure 27 
./LRT/Thres_het-2d \ TOUGH2 input file for 2d-heterogenous MC simulations of threshold, Section 6.6 
./LRT/Thres_het-2di \ iTOUGH2 input file for 2d-heterogenous MC simulations of threshold, Section 6.6 
./LRT/Thres_het-2di.out \ iTOUGH2 output file for 2d-heterogenous MC simulations of threshold, Section 6.6, Figure 25 
./LRT/Thres_hom-2d \ TOUGH2 input file for 2d-homogeneous MC simulations of threshold, Section 6.6 
./LRT/Thres_hom-2di \ iTOUGH2 input file for 2d-homogeneous MC simulations of threshold, Section 6.6 
./LRT/Thres_hom-2di.out \ iTOUGH2 output file for 2d-homogeneous MC simulations of threshold, Section 6.6, Figure 24 
./LRT/UM442_0-2d \ TOUGH2 input file for 2d-homogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_0-2di \ iTOUGH2 input file for 2d-homogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_0-2di.out \ iTOUGH2 output file for 2d-homogeneous liquid-release test UM 4.42, Section 6.4, Tables 9, 10 
./LRT/UM442_0-3d \ TOUGH2 input file for 3d-homogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_0-3di \ iTOUGH2 input file for 3d-homogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_0-3di.out \ iTOUGH2 output file for 3d-homogeneous liquid-release test UM 4.42, Section 6.4, Tables 9, 10 
./LRT/UM442_3ss-2d \ TOUGH2 input file for 2d-heterogeneous steady-state run, Section 6.4 
./LRT/UM442_3-2d.inc \ Initial condition file for 2d-heterogeneous runs (output from UM442_3ss-2d), Section 6.4 
./LRT/UM442_3-2d \ TOUGH2 input file for 2d-heterogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_3-2di \ iTOUGH2 input file for 2d-heterogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_3-2di.out \ iTOUGH2 output file for 2d-het. liquid-release test UM 4.42, Section 6.4, Tables 9, 10 
./LRT/UM442_3ss-3d \ TOUGH2 input file for 3d-heterogeneous steady-state run, Section 6.4 
./LRT/UM442_3-3d.inc \ Initial condition file for 3d-heterogeneous runs (output from UM442_3ss-2d), Section 6.4 
./LRT/UM442_3-3d \ TOUGH2 input file for 3d-heterogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_3-3di \ iTOUGH2 input file for 3d-heterogeneous liquid-release test UM 4.42, Section 6.4 
./LRT/UM442_3-3di.out \ iTOUGH2 output file for 3d-het. liquid-release test UM 4.42, Section 6.4, Tables 9, 10 
./LRT/UM564_0-2d \ TOUGH2 input file for 2d-homogeneous validation run, Section 6.5 
./LRT/UM564_0-2di \ iTOUGH2 input file for 2d-homogeneous validation run, Section 6.5 
./LRT/UM564_0-2di.out \ iTOUGH2 output file for 2d-homogeneous validation run, Section 6.5, Figure 20 
./LRT/UM564_0-2di.tec \ iTOUGH2 output file for 2d-homogeneous validation run, Section 6.5, Figure 20 
./LRT/UM564_1-2d \ TOUGH2 input file for 2d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-2d.inc \ Initial condition file for 2d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-2di \ iTOUGH2 input file for 2d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-2di.out \ iTOUGH2 output file for 2d-heterogeneous validation run, Section 6.5, Figure 21 
./LRT/UM564_1-2di.tec \ iTOUGH2 output file for 2d-heterogeneous validation run, Section 6.5, Figure 21 
./LRT/UM564_0-3d.inc \ Initial condition file for 3d-homogeneous validation run, Section 6.5 
./LRT/UM564_0-3d \ TOUGH2 input file for 3d-homogeneous validation run, Section 6.5 
./LRT/UM564_0-3di \ iTOUGH2 input file for 3d-homogeneous validation run, Section 6.5 
./LRT/UM564_0-3di.out \ iTOUGH2 output file for 3d-homogeneous validation run, Section 6.5, Figure 22

 
 Attachment II-7 
./LRT/UM564_0-3di.tec \ iTOUGH2 output file for 3d-homogeneous validation run, Section 6.5, Figure 22 
./LRT/UM564_1-3dss \ TOUGH2 input file for 3d-heterogeneous steady-state run, Section 6.5 
./LRT/UM564_1-3d.inc \ Initial condition file for 3d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-3d \ TOUGH2 input file for 3d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-3di \ iTOUGH2 input file for 3d-heterogeneous validation run, Section 6.5 
./LRT/UM564_1-3di.out \ iTOUGH2 output file for 3d-heterogeneous validation run, Section 6.5, Figure 23 
./LRT/UM564_1-3di.tec \ iTOUGH2 output file for 3d-heterogeneous validation run, Section 6.5, Figure 23 
./LRT/sh.LRT2d \ Unix shell script file used for running 2d liquid-release test calibrations 
./LRT/sh.LRT3d \ Unix shell script file used for running 3d liquid-release test calibrations 
./LRT/sh.THR2d \ Unix shell script file used for running 2d MC simulations of seepage threshold 
./LRT/sh.THRGS2d \ Unix shell script file used for running 2d grid searches of seepage threshold 
./LRT/sh.THRGS3d \ Unix shell script file used for running 3d grid searches of seepage threshold 
./LRT/sh.VAL2d \ Unix shell script used for running 2d validation cases 
./LRT/sh.VAL3d \ Unix shell script used for running 3d validation cases 
\ 
./Meshgeneration/MESH2d \ 2d mesh file for FCM 
./Meshgeneration/MESH2dSS \ 2d mesh file for FCM, for steady-state runs 
./Meshgeneration/MESH2dUM442_0 \ 2d mesh file for test UM 4.42, homogeneous 
./Meshgeneration/MESH2dUM442_0SS \ 2d mesh file for test UM 4.42, homogeneous, for steady-state runs 
./Meshgeneration/MESH2dUM442_3 \ 2d mesh file for test UM 4.42, heterogeneous 
./Meshgeneration/MESH2dUM442_3SS \ 2d mesh file for test UM 4.42, heterogeneous, for steady-state runs 
./Meshgeneration/MESH2dUM564_0 \ 2d mesh file for test UM 5.64, homogeneous 
./Meshgeneration/MESH2dUM564_0SS \ 2d mesh file for test UM 5.64, homogeneous, for steady-state runs 
./Meshgeneration/MESH2dUM564_1 \ 2d mesh file for test UM 5.64, heterogeneous 
./Meshgeneration/MESH2dUM564_1SS \ 2d mesh file for test UM 5.64, heterogeneous, for steady-state runs 
./Meshgeneration/MESH2dfract \ 2d mesh file for DFNM 
./Meshgeneration/MESH2dfractAK \ 2d mesh file for DFNM, for air injection simulations 
./Meshgeneration/MESH2dfractNoNiche \ 2d mesh file for DFNM, for run DFNMperm 
./Meshgeneration/MESH2dfractSS \ 2d mesh file for DFNM, for steady-state runs 
./Meshgeneration/MESH3dUM442_0 \ 3d mesh file for test UM 4.42, homogeneous 
./Meshgeneration/MESH3dUM442_0SS \ 3d mesh file for test UM 4.42, homogeneous, for steady-state runs 
./Meshgeneration/MESH3dUM564_0 \ 3d mesh file for test UM 5.64, homogeneous 
./Meshgeneration/MESH3dUM564_0SS \ 3d mesh file for test UM 5.64, homogeneous, for steady-state runs 
./Meshgeneration/mesh2d \ TOUGH2 input file to generate basic 2d mesh for FCM 
./Meshgeneration/mesh2dfract \ TOUGH2 input file to generate basic 2d mesh for DFNM 
./Meshgeneration/mesh2dlrt \ TOUGH2 input file to generate basic 2d mesh for calibration/validation 
./Meshgeneration/mesh3d \ TOUGH2 input file to generate basic 3d mesh 
./Meshgeneration/onestep \ TOUGH2 input file to run a single time step 
./Meshgeneration/sh.mesh2d \ Unix shell script file used to generate 2d-mesh for FCM 
./Meshgeneration/sh.mesh2dfract \ Unix shell script file used to generate 2d-mesh for DFNM 
./Meshgeneration/sh.mesh2dlrt \ Unix shell script file used to generate 2d-mesh for calibration/validation 
./Meshgeneration/sh.mesh3d \ Unix shell script file used to generate 3d-mesh for calibration/validation

 
 Attachment II-8 
./Meshgeneration/sh.onestep \ Unix shell script file used to run a single time step 
\ 
| compress > U0080.tar.Z \ Compressed tar file containg all files listed above