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Table of Contents 

Introduction ................................................................................................................................. 3

The STEPWISE Procedure ......................................................................................................... 4

Changes from the PABC-2009 PA ........................................................................................... 13

Total Releases ........................................................................................................................... 15

Cuttings and Cavings Releases ................................................................................................. 17

Spallings Releases ..................................................................................................................... 20

Direct Brine Release ................................................................................................................. 23

Culebra Releases ....................................................................................................................... 27

Summary and Conclusions ....................................................................................................... 30

References: ................................................................................................................................ 32

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Introduction 

The Waste Isolation Pilot Plant (WIPP), located in southeastern New Mexico, has been 

developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) 

disposal of transuranic (TRU) waste. Containment of TRU waste at the WIPP is regulated by the 

U.S. Environmental Protection Agency (EPA) according to the regulations set forth in Title 40 of 

the Code of Federal Regulations (CFR), Part 191. The DOE demonstrates compliance with the 

containment requirements according to the Certification Criteria in Title 40 CFR Part 194 by 

means of performance assessment (PA) calculations performed by Sandia National Laboratories 

(SNL). WIPP PA calculations estimate the probability and consequence of potential 

radionuclide releases from the repository to the accessible environment for a regulatory period of 

10,000 years after facility closure. The models used in PA are maintained and updated with new 

information as part of an ongoing process. Improved information regarding important WIPP 

features, events, and processes typically results in refinements and modifications to PA models 

and the parameters used in them. Planned changes to the repository and/or the components 

therein also result in updates to WIPP PA models. WIPP PA models are used to support the 

repository recertification process that occurs at five-year intervals following the receipt of the 

first waste shipment at the site in 1999. 

Waste panel closures comprise a repository feature that has been represented in WIPP PA since 

the original Compliance Certification Application (CCA) of 1996. Panel closures are included in 

WIPP PA models principally because they are a part of the disposal system, not because they 

play a substantive role in inhibiting the release of radionuclides to the outside environment (DOE 

1996). The DOE stated in the CCA (DOE 1996) that “The panel closure system was not 

designed or intended to support long-term repository performance.” The 1998 rulemaking that 

certified WIPP to receive transuranic waste placed conditions on the panel closure design to be 

implemented in the repository. The mandated design consists of a concrete block wall, an open 

drift section, and a concrete monolith, and was termed the “Option D” panel closure. Following 

the selection of the Option D design in 1998, the engineering of the panel closure has been reassessed, and a revised design has been established that is simpler, cheaper, and easier to 

construct. The revised panel closure design, termed the Run-of-Mine Panel Closure System 

(ROMPCS), is comprised of 100 feet of ROM salt with barriers at each end. The ROM salt is 

generated from ongoing mining operations at the WIPP and may be compacted and/or moistened 

as it is emplaced in a panel entry. The barriers consist of ventilation bulkheads, similar to those 

currently used in the panels as room closures. 

The DOE has submitted a planned change request (PCR) to the EPA requesting that EPA modify 

Condition 1 of the Final Certification Rulemaking for 40 CFR Part 194 (EPA 1998, DOE 2011) 

for the WIPP. The PCR submitted to EPA requests that Condition 1 be changed, and that the 

ROMPCS design be approved for use in all panels (DOE 2011). In support of this rulemaking 

change, a performance assessment has been completed that incorporates the ROMPCS design 

into the current PA baseline established by the 2009 Performance Assessment Baseline 

Calculation (PABC-2009) (Clayton et al. 2010). The name given to this new panel closure PA is 

PCS-2012, and the plan for its execution is detailed in AP-161 (Camphouse 2012a). 

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This report documents the analysis of the sensitivity of releases to parameters sampled using the 

code LHS for the PCS-2012 PA. Results are compared with those from the PABC-2009 PA. 

Variability in the releases computed using CCDFGF arises from two sources of uncertainty. First 

is systemic uncertainty in the values of certain parameters. This uncertainty represents a lack of 

knowledge about the values of parameters that are assumed to be constant and hence can be 

interpreted as the confidence one has that a parameter will have a particular value. Uncertainty in 

the results is also affected by the stochastic processes represented in CCDFGF, e.g. the 

occurrence of drilling intrusions. These stochastic processes are controlled by the aleatory 

parameters. The impact of the stochastic processes is assessed by running 10,000 possible futures 

for each vector of the sampled parameters. The sensitivity analysis performed using stepwise 

regression cannot be used to explain sensitivity of the results to the changes implemented in the 

models or constant (fixed) model parameters following the PABC-2009 PA. Likewise, 

variability due to the stochastic processes cannot be partitioned among the aleatory parameters 

because all aleatory parameters are independently sampled in all futures and because the mean 

releases computed across the 10,000 futures are regresses against the parameters and these means 

subsume the within-vector (aleatory) variability. The sensitivity analysis can only attempt to 

resolve the question of which sampled parameters contribute the most to the variability 

(uncertainty) observed in the mean releases across the 10,000 futures for each vector. 

The STEPWISE Procedure 

The code STEPWISE version 2.21 was used to determine the relative importance of the sampled 

parameters in the PCS-2012 analysis. STEPWISE receives sampled input parameter values and 

calculated release data that correspond to those input data. STEPWISE relates the sampled input 

parameter values to the calculated release data by performing a multiple regression analysis and 

reporting the results in tables. Additional analyses were performed on selected subsets of the data 

using Microsoft® Excel 2010. The spreadsheets are provided on the attached CD_ROM and are 

also stored in the CMS library LIBAP161_STPW in the compressed file 

AP161SimpleRegressionSpeadsheets.zip (Attachment 1). 

WIPP PA employs stepwise linear multiple regression to evaluate the relative importance of the 

various sampled parameters on the estimates of potential releases. In the forward stepwise 

approach used by STEPWISE, a sequence of regression models is constructed, starting with the 

input parameter that has the strongest simple correlation with the output variable. Partial 

correlations between the output and the remaining variables are then computed. The partial 

correlations remove the linear effects of variables already included in the model. The variable 

having the largest significant partial correlation coefficient is added next, and the partial 

correlations for the remaining input variables are recomputed. Significance is determined using 

an F-test, and the significance level for adding an input variable to the model is 1-in, where in 

is the significance level for a Type I error that is set by the analyst. The F-test compares the 

variability contributed by the variable to the variability not accounted for by the regression, i.e. 

the variability of the residuals. By default STEPWISE sets in = 0.05, so that one is 95% 

confident that there is a partial correlation between the input and output variables. This process is 

repeated until there are no variables remaining having significant correlations with the output 

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variable. Variables excluded from the regression model contribute no statistically significant 

information in relation to the unexplained variability and hence the results are judged to be 

relatively insensitive to those parameters. 

Input variables that are added to the regression model are not necessarily retained. For an input 

variable to be retained, its regression coefficient, i.e. the linear contribution of an input to the 

prediction of the output variable, must be statistically distinguishable from zero. A t-test is used 

to determine whether a regression coefficient is significantly different than zero. The t-test 

evaluates the null hypothesis that the regression coefficient is zero. The hypothesis is not rejected 

when random effects can give rise to the observed regression coefficient with probability out. 

The random effects are caused by the stochastic variability contributed by the input variables not 

in the regression model. In other words, the hypothesis is rejected, and the variable is included in 

the model when the 1-out confidence interval of the regression coefficient does not encompass 

zero. By default the STEPWISE out -value for allowing a variable to enter the regression model 

is 0.05. Thus, in the default case, one is 95% confident that the input variables make a linear 

contribution to the response of the output variable. The user may specify different -values in 

the input control file. However, the value allowing a variable to enter the model, in, must be less 

than or equal to the value by which a variable is allowed to leave the model, out, to avoid 

looping. In the following analyses, in was 0.05, and out was 0.05. 

Setting out to in maximizes the number of variables in the model (as requested by EPA in C-23-

18, U.S. Environmental Protection Agency 2004) but can increase the number of spurious 

correlations (Kirchner 2004a,b). A spurious correlation implies a linear relationship exists 

between two variables but in reality no such relationship exists. The predicted error sum of 

squares (PRESS) was computed to detect over-fitting of the regression model to the data. Overfitting can occur when the regression methodology causes the fit to favor specific points rather 

than the general shape of the data curve. In such a case the minimum value of PRESS may occur 

earlier than the last step in the regression analysis. No such condition was observed in any of the 

rank correlation analyses reported herein. 

The STEPWISE procedure constructs a multivariate linear regression model. One of the 

assumptions of this statistical model is that the dependent (output) variable shows a linear 

response to the independent (input) variables. In cases where the response is non-linear but 

monotonic, replacing the values of the data with their ranks tends to linearize the response curves 

and standardizes the variability in the outputs and parameters by mapping the data into identical 

ranges. The rank of a value is an integer representing its position in the sorted list of the values. 

Ranking also tends to de-emphasize the impact of “outliers,” which are points having 

considerably larger or smaller values than the remainder of the sample population. Although the 

use of ranks precludes using the model to predict values of an output variable given an input 

variable, the results are usually well suited for ranking the importance of the contributions of the 

input variables to the response of the output variable. The STEPWISE procedure has the 

functionality to perform ranked regressions. For the cases described below, the ranked 

regressions showed stronger correlations than the regressions based on the unranked data. This 

result suggests that there are non-linear relationships between the dependent and independent 

variables, but it does not eliminate the possibility that there are also non-monotonic relationships. 

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Ranked regression was used to evaluate the sensitivity of the output variables to the sampled 

parameters. Analysts examined scatter plots of the dependent versus independent ranked 

variables to determine if there were any obvious non-monotonic relationships. Obvious nonmonotonic relationships were not found although there are cases involving inputs that are 

categorized as discrete variables (e.g. GLOBAL:OXSTAT, which is sampled as a uniform 

distribution but is then mapped to one of two discrete values) and cases where there are large 

proportions of the vectors showing no release (e.g. releases from the Culebra in the CRA-2009 

analysis). Application of linear regression to such cases is somewhat problematic in terms of the 

assumptions of normally-distributed residuals and homogeneous variance among the residuals. 

However, in terms of ranking the relative importance of the parameters these issues are probably 

not significant. 

Most of the regression models produced by STEPWISE do not include all of the variables, even 

after ranking the data. This simply indicates that the uncertainties in many of the parameters have 

statistically insignificant effects on the output variable. Statistical insignificance can arise 

because the output variable has a low functional response to the input variable, because the 

magnitude of uncertainty in the input variable is small relative to the other inputs, or from a 

combination of both conditions. This is not to say that these non-significant variables have no 

influence on the releases. Their exclusion from the tables reflects the inability of this statistical 

technique to rank their importance with an acceptable degree of confidence. For example, if the 

response of the output variable to an input variable was non-monotonic then the regression 

analysis might fail to properly identify that variable’s importance. This possibility is unlikely for 

total releases and cuttings and cavings releases because the R2

 value (coefficient of 

determination) indicates that more than 83% of the variability in the output variables has been 

accounted for by the listed input variables. 

Several of the parameters that appear in the model often contribute very little to the R2 value and, 

therefore, explain very little of the variability in the output variable. Parameters that have minor 

contributions can appear by chance, simply due to random correlations. Many of the parameters 

that account for only a few percent to the variability in an output from one replicate may show 

different rankings, or can even be absent, in another replicate. Thus, it is difficult to assess the 

importance of the parameters that improve the regression model very little and, in reality, they 

may have no importance at all. Therefore, only the parameters that appear to have significant 

impacts on the regression model will be explained in detail. 

Analyses were performed using Microsoft Excel to supplement the STEPWISE results for 

spallings and Culebra releases because some of those releases were zero. These analyses 

illustrate the relationship between the non-zero releases and various parameters. Simple linear 

regression analyses were conducted on the ranks of the parameters and non-zero releases. 

Significance of the regressions is denoted by the probability that the correlation is due to random 

error, as in p = 0.09. A regression is considered significant only if p≤0.05, i.e. that there is less 

than a 5 % chance that the correlation is due to random error. 

This report documents the results of the PCS-2012 PA sensitivity analysis and shows, for 

comparison, the results obtained for the PABC-2009 analysis (Kirchner 2009). The details of run 

control for the PRECCDFGF and CCDFGF results presented herein are documented in 

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Camphouse et al. (2012). The files associated with the PCS-2012 PA STEPWISE analysis can be 

found in the CMS libraries LIBPAP161_EVAL, LIBPABC09_STPW, LIBAP161_LHS, 

LIBSTP and LIBAP161_CCGF (Table 1). These files include the run scripts (EVAL_STP.COM, 

EVAL_STP_AP161_RAW_Rr.COM and EVAL_STP_AP161_RANK_Rr.COM, the CCGFDF 

output files CCGF_AP161_Rr.OUT, the input files containing the mean values of the model 

output variables (STP_AP161_MEANS_Rr.TRN), the STEPWISE control files 

(STP_AP161_RANK_ALL_Rr.INP and STP_AP161_RAW_ALL_Rr.INP), the input file 

containing the sampled parameters (STP_AP161_LHS_Rr.TRN) and the results of the regression 

analysis (STP_AP161_RANK_Rr.TXT and STP_AP161_RAW_Rr.TXT), where the r in the file 

names represents replicate number 1, 2 or 3. 

The files of mean values of releases STP_AP161_MEANS_Rr.TRN.TRN, were generated from 

the CCGF_AP161_Rr.OUT files using the Microsoft® Access 2010 database 

CCDFGF_Analysis.mdb. The CCDFGF output files store the mean values and those mean 

values are extracted from those files along with the “binned” data. The “bins” are a series of 

equally spaced intervals on a logarithmic scale that encompass the data. Each datum is assigned 

to a bin and the frequency of values within the bin tabulated. The databases are provided on the 

attached CD in zipped format and the AP161_ANALYSISDATABASE.zip file is also stored in 

the CMS library LIBAP161_STPW (Attachment 1). 

The EVAL_STP.COM script runs STEPWISE.EXE to perform the regression analysis for all 

three replicates on both the ranked and raw (un-ranked) data. 

Table 1. Files used in the PCS-2012 PA sensitivity analysis. 

File Library 

CCDFGF_Analysis.mdb1 LIBAP161_STPW 

CCDFGF_Data.mdb1 LIBAP161_STPW 

CCGF_AP161_R1.OUT LIBAP161_CCGF 

CCGF_AP161_R2.OUT LIBAP161_CCGF 

CCGF_AP161_R3.OUT LIBAP161_CCGF 

LHS1_AP161_R1.TRN LIBAP161_LHS 

LHS1_AP161_R2.TRN LIBAP161_LHS 

LHS1_AP161_R3.TRN LIBAP161_LHS 

LHS2_AP161_R1_CON.TRN LIBAP161_LHS 

LHS2_AP161_R2_CON.TRN LIBAP161_LHS 

LHS2_AP161_R3_CON.TRN LIBAP161_LHS 

EVAL_STP_AP161_RAW_R1.COM LIBAP161_STPW 

EVAL_STP_AP161_RAW_R2.COM LIBAP161_STPW 

EVAL_STP_AP161_RAW_R3.COM LIBAP161_STPW 

EVAL_STP_AP161_RANK_R1.COM LIBAP161_STPW 

EVAL_STP_AP161_RANK_R2.COM LIBAP161_STPW 

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File Library 

EVAL_STP_AP161_RANK_R3.COM LIBAP161_STPW 

STP_AP161_LHS_R1.TRN LIBAP161_STPW 

STP_AP161_LHS_R2.TRN LIBAP161_STPW 

STP_AP161_LHS_R3.TRN LIBAP161_STPW 

STP_AP161_MEANS_R1.TRN LIBAP161_STPW 

STP_AP161_MEANS_R2.TRN LIBAP161_STPW 

STP_AP161_MEANS_R3.TRN LIBAP161_STPW 

STP_AP161_RANK_ALL_R1.INP LIBAP161_STPW 

STP_AP161_RANK_ALL_R2.INP LIBAP161_STPW 

STP_AP161_RANK_ALL_R3.INP LIBAP161_STPW 

STP_AP161_RAW_ALL_R1.INP LIBAP161_STPW 

STP_AP161_RAW_ALL_R2.INP LIBAP161_STPW 

STP_AP161_RAW_ALL_R3.INP LIBAP161_STPW 

STP_AP161_RANK_R1.TXT LIBAP161_STPW 

STP_AP161_RANK_R1.SP LIBAP161_STPW 

STP_AP161_RANK_R2.TXT LIBAP161_STPW 

STP_AP161_RANK_R2.SP LIBAP161_STPW 

STP_AP161_RANK_R3.TXT LIBAP161_STPW 

STP_AP161_RANK_R3.SP LIBAP161_STPW 

STP_AP161_RAW_R1.TXT LIBAP161_STPW 

STP_AP161_RAW_R12.SP LIBAP161_STPW 

STP_AP161_RAW_R2.TXT LIBAP161_STPW 

STP_AP161_RAW_R2.SP LIBAP161_STPW 

STP_AP161_RAW_R3.TXT LIBAP161_STPW 

STP_AP161_RAW_R3.SP LIBAP161_STPW 

STEPWISE_PA96_2.EXE LIBSTP 1

 Compressed and stored in AP161_ANALYSISDATABASE.ZIP 

The input files for STEPWISE use short names rather than material:property designations. These 

short names are required because of a limitation in the length of variable names in STEPWISE. 

Table 2 associates these names with the material and property names. 

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Table 2. Material and property values associated with the variable names used in the PCS-2012 PA 

sensitivity analysis. References in this table refer to DOE(2004). 

Variable Name Material Name Property Name Description 

CMKDAM3 AM+3 MKD_AM 

Matrix distribution coefficient (m3/kg) for 

Am in +3 oxidation state. Defined in 

Equation (231). 

BHPERM BH_SAND PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of the silty sand-filled borehole (Table 

PA-5). Used in regions Upper Borehole 

and Lower Borehole in Figure PA-8. 

DOMEGA BOREHOLE DOMEGA Drill string angular velocity (rad/s). 

Defined in Equation (112b). 

WTAUFAIL BOREHOLE TAUFAIL Shear strength of waste (Pa). Defined in 

Equation (111). 

BPCOMP CASTILER COMP_RCK 

Bulk compressibility (Pa–1) of Castile 

brine reservoir. Defined fB in Equation 

(29) for region CASTILER of Figure PA-8

BPINTPRS CASTILER PRESSURE 

Initial brine pore pressure in the Castile 

brine reservoir. Defined in Equation (50) 

for region CASTILER in Figure PA-8. 

BPPRM CASTILER PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of the Castile brine reservoir. Used in 

region CASTILER in Figure PA-8. 

PLGPRM CONC_PLG PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of the concrete borehole plugs (Table 

PA-5). Used in region Borehole Plugs in 

Figure PA-8. 

CFRACPOR CULEBRA APOROS 

Culebra fracture (i.e., advective) porosity 

(dimensionless). Defined in Equation 

(223). 

CMTRXPOR CULEBRA DPOROS 

Culebra matrix (i.e., diffusive) porosity 

(dimensionless). Defined in Equation 

(230). 

CFRACSP CULEBRA HMBLKLT 

Culebra fracture spacing (m). Equal to 

half the distance between fractures (i.e., 

the Culebra half matrix block length). 

Defined in Equation (236) and Figure 

PA-26. 

CTRANSFM CULEBRA MINP_FAC 

Multiplier (dimensionless) applied to 

transmissivity of the Culebra within the 

land withdrawal boundary after mining of 

potash reserves. MF defined in Equation 

(216) (see section PA-4.8.2). 

DRZPRM DRZ_1 PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of the DRZ. Used in regions Upper DRZ 

and Lower DRZ in Figure PA-8. 

DRZPCPRM DRZ_PCS PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of the DRZ immediately above the panel 

closure concrete (Section PA-4.2.8.3). 

Used in region DRZ_PCS in Figure PA8. 

CCLIMSF GLOBAL CLIMTIDX Climate scale factor (dimensionless) for 

Culebra flow field. SFC defined in 

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Variable Name Material Name Property Name Description 

Equation (221). 

WOXSTAT GLOBAL OXSTAT 

Indicator variable for elemental oxidation 

states (dim3ensionless). WOXSTAT ≤0.5 

indicates use of CMKDPU3, CMKDU4, 

WSOLPU3C, WSOLPUS, , WSOLU4C, 

and WSOLU4S. WOXSTAT >0.5 implies 

use of CMKDPU4, CMKDU6, 

WSOLPU4C, WSOLPU4S, WSOLU6C, 

and WSOLU6S. 

BPPROB GLOBAL PBRINE 

Probability that a drilling intrusion 

penetrates pressurized brine in the 

Castile Formation. See Section PA-3.5 

for definition. 

CTRAN GLOBAL TRANSIDX 

Indicator variable for selecting 

transmissivity field. See Section PA4.8.2. 

T1PDIS PCS_T1 PORE_DIS Panel Closure System for an initial time 

duration Brooks-Corey pore distribution 

parameter 

T1POROS PCS_T1 POROSITY Panel Closure System for an initial time 

duration effective porosity 

T1PRMX PCS_T1 PRMX_LOG Panel Closure System for an initial time 

duration log of intrinsic permeability, Xdirection 

T1SIBRN PCS_T1 SAT_IBRN Panel Closure System for an initial time 

duration initial brine saturation 

T1SRBRN PCS_T1 SAT_RBRN Panel Closure System for an initial time 

duration Residual Brine Saturation 

T1SRGAS PCS_T1 SAT_RGAS Panel Closure System for an initial time 

duration residual gas saturation 

T2P2PERM PCS_T2 POR2PERM Panel Closure System for a secondary 

time duration, distribution used to 

calculate permeability from sampled 

porosity values 

T2POROS PCS_T2 POROSITY Panel Closure System for a secondary 

time duration effective porosity 

T3POROS PCS_T3 POROSITY Run-of-Mine Panel Closure System, 

tertiary time period effective porosity 

WPHUMOX3 PHUMOX3 PHUMCIM 

Ratio (dimensionless) of concentration of 

actinides attached to humic colloids to 

dissolved concentration of actinides for 

oxidation state +III in Castile brine. 

Defined SFHum(Castile, +3, Am) and 

SFHum(Castile, +3, Pu) for Equation 

(90). 

CMKDPU3 PU+3 MKD_PU 

Matrix distribution coefficient (m3/kg) for 

Pu in +3 oxidation state. Defined in 

Equation (231). 

CMKDPU4 PU+4 MKD_PU 

Matrix distribution coefficient (m3/kg) for 

Pu in +4 oxidation state. Defined in 

Equation (231). 

HALCROCK S_HALITE COMP_RCK Bulk compressibility of halite (Pa–1). 

Defined fB in Equation (31) for region 

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Variable Name Material Name Property Name Description 

Salado of Figure PA-8. 

HALPOR S_HALITE POROSITY 

Halite porosity (dimensionless). Defined 

in Equation (25g) for region Salado in 

Figure PA-8. 

SALPRES S_HALITE PRESSURE 

Initial brine pore pressure (Pa) in the 

Salado halite, applied at an elevation 

consistent with the intersection of MB 

139. Defined in Equation (49) for region 

Salado. See Figure PA-8. 

HALPRM S_HALITE PRMX_LOG 

Logarithm of intrinsic halite permeability 

(m2). Used in region Salado in Figure 

PA-8. 

ANHBCEXP S_MB139 PORE_DIS 

Brooks-Corey pore distribution 

parameter for anhydrite (dimensionless). 

Defined in Equation (32) for regions MB 

138, Anhydrite AB, and MB 139 of Figure 

PA-8 for use with Brooks-Corey model; 

defined in Equations (36) for use with 

van Genuchten-Parker model in the 

same regions. 

ANHPRM S_MB139 PRMX_LOG 

Logarithm of intrinsic anhydrite 

permeability (m2). Used in regions MB 

138, Anhydrite AB, and MB 139 in Figure 

PA-8. 

ANHBCVGP S_MB139 RELP_MOD 

Indicator for relative permeability model 

(dimensionless) for regions MB 138, 

Anhydrite AB and MB 139 in Figure PA8. See Table PA-3. 

ANRBRSAT S_MB139 SAT_RBRN 

Residual brine saturation in anhydrite 

(dimensionless). Sbr defined in Equation 

(35) for regions MB 138, Anhydrite AB, 

and MB 139 in Figure PA-8. 

SHLPRM2 SHFTL_T1 PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of lower shaft seal materials for the first 

200 years after closure. Used in region 

Lower Shaft in Figure PA-8. 

SHLPRM3 SHFTL_T2 PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of lower shaft seal materials from 200 

years to 10,000 years after closure. Used 

in region Lower Shaft in Figure PA-8. 

SHUPRM SHFTU PRMX_LOG 

Logarithm of intrinsic permeability (m2) 

of upper shaft seal materials. Used in 

region Upper Shaft in Figure PA-8. 

SHURBRN SHFTU SAT_RBRN 

Residual brine saturation in upper shaft 

seal materials (dimensionless). Sbr 

defined in Equation (35) for region Upper 

Shaft in Figure PA-8. 

SHURGAS SHFTU SAT_RGAS 

Residual gas saturation in upper shaft 

seal materials (dimensionless). Sgr 

defined in Equation (34) for region Upper 

Shaft in Figure PA-8. 

WSOLVAR3 SOLMOD3 SOLVAR Solubility multiplier for +III oxidation 

states 

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Variable Name Material Name Property Name Description 

WSOLVAR4 SOLMOD4 SOLVAR Solubility multiplier for +IV oxidation 

states 

SPLPTDIA SPALLMOD PARTDIAM Particle diameter of disaggregated 

waste. 

REPIPERM SPALLMOD REPIPERM Waste permeability of gas local to 

intrusion borehole. 

SPLRPOR SPALLMOD REPIPOR Waste porosity at time of drilling intrusion

TENSLSTR SPALLMOD TENSLSTR Tensile strength of waste. 

WGRCOR STEEL CORRMCO2 

Rate of anoxic steel corrosion (m/s) 

under brine inundated conditions and 

with no CO2 present. Defined Rci in 

Equation (59) for areas Waste Panel, 

South RoR, and North RoR in Figure PA8. 

CMKDTH4 TH+4 MKD_TH 

Matrix distribution coefficient (m3/kg) for 

Th in +4 oxidation state. Defined in 

Equation (231). 

CMKDU4 U+4 MKD_U 

Matrix distribution coefficient (m3/kg) for 

U in +4 oxidation state. Defined in 

Equation (231). 

CMKDU6 U+6 MKD_U 

Matrix distribution coefficient (m3/kg) for 

U in +6 oxidation state. Defined in 

Equation (231). 

WBIOGENF WAS_AREA BIOGENFC Probability of obtaining sampled 

microbial gas generation rates. 

WGRMICH WAS_AREA GRATMICH 

Rate of CPR biodegradation (mol 

C6H10O5 / kg C6H10O5 / s) under 

anaerobic, humid conditions. Defined 

Rmh in Equation (61) for areas Waste 

Panel, South RoR, and North RoR, in 

Figure PA-8. 

WGRMICI WAS_AREA GRATMICI 

Rate of CPR biodegradation (mol 

C6H10O5 / kg C6H10O5 / s) under 

anaerobic, brine-inundated conditions. 

Defined Rmi in Equation (61) for areas 

Waste Panel, South RoR, and North 

RoR, in Figure PA-8. 

WMICDFLG WAS_AREA PROBDEG 

Index for model of microbial degradation 

of CPR materials (dimensionless). Used 

in areas Waste Panel, South RoR, and 

North RoR in Figure PA-8. 

WRBRNSAT WAS_AREA SAT_RBRN 

Residual brine saturation in waste 

(dimensionless). Defined Sbr in Equation 

(34) for areas Waste Panel, South RoR, 

and North RoR, in Figure PA-8; also 

used in waste material in Figure PA-20 

for calculation of DBR; see Section PA4.7. 

WRGSSAT WAS_AREA SAT_RGAS 

Residual gas saturation in waste 

(dimensionless). Sgr defined in Equation 

(35) for areas Waste Panel, South RoR, 

and North RoR in Figure PA-8; also used 

in waste material in Figure PA-20 for 

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 13 of 34 

Variable Name Material Name Property Name Description 

calculation of DBR; see Section PA-4.7. 

WASTWICK WAS_AREA SAT_WICK 

Increase in brine saturation of waste due 

to capillary forces (dimensionless). 

Defined Swick in Equation (78) for areas 

Waste Panel, South RoR, and North 

RoR, in Figure PA-8. 

In addition, three variables are created in STEPWISE through transformation of the variable 

GLOBAL:OXSTAT, the indicator variable for oxidation states of uranium and plutonium. 

GLOBAL:OXSTAT is sampled as a [0,1) uniform distribution but is treated in the code as a 

Bernoulli distribution (a distribution having only two discrete states). The computed variable 

OXSTAT is assigned 0 if GLOBAL:OXSTAT is less than 0.5 and is assigned 1 otherwise. The 

other two computed variables represent the Kds for the +VI and +IV oxidation states of uranium 

and plutonium, respectively. A Kd value represents the matrix:water partitioning coefficient. If 

GLOBAL:OXSTAT is 0 then CMKDU is assigned U+6:MKD_U and CMKDPU is assigned 

PU+4:MKD_PU, i.e. the Kds for the +VI and +IV oxidation states of uranium and plutonium, 

respectively. If GLOBAL:OXSTAT is 1 then CMKDU is assigned U+4:MKD_U and CMKDPU 

is assigned PU+3:MKD_PU, i.e. the Kds for the +IV and +III oxidation states of uranium and 

plutonium, respectively. In the discussion below these variables are referenced as 

Composite:MKD_U and Composite:MKD_PU in order to denote their status as composites of 

pairs of sampled parameters. 

Changes from the PABC-2009 PA 

The changes implemented in the PCS-2012 analysis from the PABC-2009 PA analysis are 

documented in Camphouse (2012a).These changes focus on a revised design of the panel closure 

system (PCS), known as the Run-of-Mine Panel Closure System (ROMPCS). This design 

employs panel closures comprised of 100 feet of run-of-mine (ROM) salt with barriers at each 

end. The ROM salt is generated from ongoing mining operations at the WIPP and may be 

compacted and/or moistened as it is emplaced in a panel entry. The ROM salt will be emplaced 

to all salt surfaces (back, walls, etc.) as completely as practicable. After emplacement, creep 

closure of the panel entries will cause the ROM salt to consolidate to a condition approaching 

intact salt, with low porosity and low permeability. 

The ROMPCS properties modeled in the PA were based on three time periods: from 0 to 100 

years, from 100 years to 200 years, and from 200 years to 10,000 years. This was a refinement to 

the granularity of panel closure modeling undertaken in the 2006 PCS PA (Vugrin and Dunagan 

2006) and the 2011 PC3R PA (Camphouse et al. 2011). Three time periods are appropriate 

because the process to consolidate the ROM salt occurs over a primary time scale of 

approximately 100 years, while the process to heal fractures in the DRZ surrounding the PCS 

occurs over a longer time scale of approximately 200 years. The ROM salt was therefore 

represented by three materials, denoted as PCS_T1 for the first 100 years, PCS_T2 for 100 to 

200 years, and PCS_T3 for 200 to 10,000 years. The effective permeability and porosity of the 

ROMPCS are the two parameters expected to have the greatest impact on calculations of 

pressure and brine saturation in repository waste areas. A brief discussion of the two-phase flow 

parameters used for PCS_T1, PCS_T2, and PCS_T3, particularly their implementation in regard 

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 14 of 34 

to BRAGFLO capillary pressure modeling, is given in Camphouse (2012b). In addition to the 

sampled parameters listed in Table 2, there were changes or additions of some non-sampled 

(constant) parameters (Table 3). 

Table 3: Non-sampled PCS_T1, PCS_T2, and PCS_T3 Properties to be used in the PCS-2012 PA 

Property Description 

CAP_MOD Capillary Pressure Model Number (CAP_MOD = 1 or 2 has been used in 

every PA to date for all materials in BRAGFLO) 

COMP_RCK Bulk Compressibility 

KPT Flag to Enable Dynamic Updating of Threshold Capillary Pressure as a 

Function of Permeability (KPT = 0 has been used in every PA to date for all 

materials in BRAGFLO) 

PC_MAX Maximum Allowable Capillary Pressure (PC_MAX = 1 x 108 Pa has been 

used for all BRAGFLO materials since the CCA) 

PCT_A Threshold Capillary Pressure Linear Parameter 

PCT_EXP Threshold Capillary Pressure Exponential Parameter 

PO_MIN Minimum Brine Pressure for Capillary Model 3 (CAP_MOD = 3 has never 

been used in PA) 

PRMY_LOG1 Log of Intrinsic Permeability, Y-Direction 

PRMZ_LOG1 Log of Intrinsic Permeability, Z-Direction 

RELP_MOD Relative Permeability Model Number 

1

 Assigned the sampled values of PRMX_LOG (Camphouse 2012c). 

 

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 Page 15 of 34 

Figure 1. Mean CCDFs from Replicate 1 of the PCS-2012 analysis and the PABC-2009 PA analysis. 

Most of the distributions used for the PCS-2012 analysis were identical to those used in the 

PABC-2009. The exceptions were those for material PCS_T1 (properties POR_DIS, 

POROSITY, PRMX_LOG, SAT_IBRN, SAT_RBRN, SAT_RGAS), PCS_T2 (properties 

POR2PERM and POROSITY) and PCS_T3:POROSITY which were not sampled in the PABC2009. For material CONC_PCS, the following properties were sampled in PABC-2009 but not 

PCS-2012: PORE_DIS, PRMX_LOG, SAT_RBRN, and SAT_RGAS. The material:property 

CELLULS:FBETA was also sampled in the PABC-2009 but was not used in that analysis. 

Total Releases 

As in the PABC-2009 (Kirchner 2009), cuttings and cavings, direct brine and spallings releases 

account for the majority of the total releases estimated in the PABC-2009 (Figure 1). In the 

tables below the cumulative R2

 value represents the proportion of total variation explained by the 

fitted regression using the listed variables, starting with the greatest contributor to the variance. 

The number of variables used in the regression model is determined by the stepwise regression 

procedure, as discussed above. Across the three replicates of the PCS-2012 83 % to 86 % of the 

variability in total release is accounted for in the full regression model (Tables 4 and 5). The 

R = Release (EPA Units)

0.0001 0.001 0.01 0.1 1 10 100

Probability Release > R

0.0001

0.001

0.01

0.1

1

10

PCS-2012 Total

PABC-2009 Total

PCS-2012 Cuttings and Cavings

PABC-2009 Cuttings and Cavings

PCS-2012 Direct Brine

PABC-2009 Direct Brine

PCS-2012 Spallings

PABC-09 Spallings

PCS-2012 Culebra

PABC-2009 Culebra

Release Limits

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 16 of 34 

difference in the R2

 values for total releases between the first replicates of the PABC-2009 (87 

%) and the PSC-2012 PA (83 %) is inconsequential (Table 4). 

In both the PCS-2012 and PABC-2009 analyses the uncertainty in total normalized releases is 

largely (69 % to 74 % in PCS-2012) due to uncertainty in waste shear strength 

(BOREHOLE:TAUFAIL). Cuttings and cavings releases are normally combined because they 

are a direct result of drilling operations. The volumes of cavings are primarily controlled by 

shear strength, and the negative correlation found in the analysis is expected. There is little 

consistency in the ranking of the other variables identified as significant contributors to the 

variability in total releases. These other variables contribute less than an additional 14 % to the 

variability in total releases. CASTILER:PRESSURE is the initial brine pore pressure in the 

Castile and it contributes between 2% and 4% variability, depending on the replicate. 

S_HALITE:POROSITY is the effective porosity in intact halite and it contributes 3 % variability 

in replicate 3 and 2 % in replicates 1 and 2. DRZ_1:PRMXLOG, the log of intrinsic permeability 

in the X-direction for the disturbed rock zone in the initial time period, contributes 3% and 1% 

variability in replicates 1 and , respectively, but contributes nothing to replicate 2. The thirdranked parameter in replicate 2 is CULEBRA:MINP_FAC, the mining transmissivity multiplier. 

It is not included in the regression models of the other two replicates. The remaining variables in 

each of the three replicates contribute only 1 % to 2 % of the variability in total release, and 

hence are not important to controlling the total release. Correlations that explain only a few 

percent of the variation can occur due to random sampling and may well be spurious. 

Table 4. Stepwise ranked regression analysis for mean total releases, replicate 1 of PCS-2012 PA and the 

ABC-2009. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BOREHOLE:TAUFAIL 0.69 -0.83 BOREHOLE:TAUFAIL 0.76 -0.88 

 2 CASTILER:PRESSURE 0.71 0.16 SOLMOD3:SOLVAR 0.79 0.17 

 3 DRZ_1:PRMX_LOG 0.74 0.17 CELLULS:FBETA 0.81 -0.14 

 4 SOLMOD3:SOLVAR 0.76 0.14 CASTILER:PRESSURE 0.83 0.12 

 5 S_HALITE:POROSITY 0.78 0.13 GLOBAL:PBRINE 0.85 0.13 

 6 PCS_T1:SAT_IBRN 0.79 -0.12 SHFTU:SAT_RGAS 0.85 -0.10 

 7 GLOBAL:TRANSIDX 0.80 0.12 GLOBAL:TRANSIDX 0.86 0.09 

 8 CULEBRA:MINP_FAC 0.81 0.09 BOREHOLE:DOMEGA 0.87 0.08 

 9 S_MB139:RELP_MOD 0.82 -0.10 

 10 S_MB139:SAT_RBRN 0.83 -0.09 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

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Table 5. Stepwise ranked regression analysis for mean total releases, replicates 2 and 3 of PCS-2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BOREHOLE:TAUFAIL 0.71 -0.84 BOREHOLE:TAUFAIL 0.74 -0.85 

 2 CASTILER:PRESSURE 0.75 0.22 S_HALITE:POROSITY 0.77 0.18 

 3 CULEBRA:MINP_FAC 0.77 -0.14 CASTILER:PRESSURE 0.80 0.15 

 4 S_HALITE:POROSITY 0.79 0.12 SOLMOD3:SOLVAR 0.81 0.12 

 5 SOLMOD3:SOLVAR 0.80 0.12 BOREHOLE:DOMEGA 0.82 0.12 

 6 SPALLMOD:REPIPERM 0.81 -0.10 GLOBAL:PBRINE 0.83 0.10 

 7 PCS_T1:POROSITY 0.82 -0.10 CULEBRA:MINP_FAC 0.84 0.09 

 8 PCS_T1:PORE_DIS 0.83 -0.09 DRZ_1:PRMX_LOG 0.85 0.09 

 9 CASTILER:PRMX_LOG 0.84 0.09 GLOBAL:TRANSIDX 0.86 0.08 

 10 STEEL:CORRMCO2 0.86 -0.08 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

Cuttings and Cavings Releases 

CCDFGF only records the combined volume of cuttings and cavings, but only the cavings 

component is computed using sampled parameters. Tables 6 and 7 list the parameters that 

showed significant correlations to cuttings and cavings releases based on a stepwise regression 

using ranked data. The uncertainty in mean cuttings and cavings releases is primarily due to the 

uncertainty in the cavings volume. The other source of uncertainty is the stochastic selection of 

the waste streams intersected by the borehole. The results from the regression analyses of the 

PABC-2009 and PCS-2012 PA are identical for each of the three replicates (see Kirchner 2009). 

Waste shear strength (BOREHOLE:TAUFAIL) controls about 98 % of the variability in mean 

cuttings and cavings releases in all replicates. Cuttings and caving releases are primarily 

controlled by the volume of cuttings and cavings produced, which in turn is a highly non-linear 

function of BOREHOLE:TAUFAIL through its control on cavings volumes (Fig. 4). The drill 

string angular velocity (BOREHOLE:DOMEGA) has a very minor contribution as well, and is 

discussed in Dunagan (2004) and Ismail and Garner (2010). The remaining parameters in Tables 

6 and 7 explain less than about 1 % of the variability in cuttings and cavings and are spurious 

because they have no influence on cuttings and cavings release volumes or on the selection of 

waste streams. 

Table 6. Stepwise ranked regression analysis for mean Cuttings and Cavings releases. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BOREHOLE:TAUFAIL 0.98 -0.99 BOREHOLE:TAUFAIL 0.98 -0.99 

 2 BOREHOLE:DOMEGA 1.00 0.11 BOREHOLE:DOMEGA 1.00 0.11 

 3 SHFTL_T1:PRMX_LOG 1.00 0.02 SHFTL_T1:PRMX_LOG 1.00 0.02 

 4 (Composite):OXSTAT 1.00 -0.02 (Composite):OXSTAT 1.00 -0.02 

 5 CULEBRA:HMBLKLT 1.00 0.02 CULEBRA:HMBLKLT 1.00 0.02 

 6 DRZ_PCS:PRMX_LOG 1.00 0.01 DRZ_PCS:PRMX_LOG 1.00 0.01 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

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 Page 18 of 34 

Table 7. Stepwise ranked regression analysis for mean Cuttings and Cavings releases, replicates 2 and 3 of 

PCS-2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BOREHOLE:TAUFAIL 0.98 -0.99 BOREHOLE:TAUFAIL 0.98 -0.99 

 2 BOREHOLE:DOMEGA 0.99 0.11 BOREHOLE:DOMEGA 0.99 0.12 

 3 SHFTL_T1:PRMX_LOG 0.99 -0.02 (Composite):MKD_PU 0.99 0.02 

 4 PCS_T2:POR2PERM 0.99 0.02 SOLMOD3:SOLVAR 1.00 -0.02 

 5 STEEL:CORRMCO2 1.00 0.02 

 6 GLOBAL:TRANSIDX 1.00 0.01 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

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 Page 19 of 34 

A. 

B. 

Figure 4. The volume of cuttings and cavings (A) and releases from cuttings and cavings (B) show significant 

nonlinear responses to WTAUFAIL. Note that the response is a curve even though BOREHOLE:TAUFAIL 

is plotted using a logarithmic axis. 

BOREHOLE:TAUFAIL

0.01 0.1 1 10 100

Cuttings and Cavings Volume (m3

)

0

5

10

15

20

25

PCS-2012 PA

PABC-2009 PA

BOREHOLE:TAUFAIL

0.01 0.1 1 10 100

Cuttings and Cavings Releases (EPA Units)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

PCS-2012 PA

PABC-2009 PA

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 20 of 34 

Spallings Releases 

The probability of a spalling release volume less than two cubic meters was greater in the PCS2012 PA as compared to the PABC-2009 (Fig. 5). The spallings releases were also higher in the 

PCS-2012 PA (Fig. 6). This increase was due in part to an increase in the number of vectors that 

produced spallings releases. Thirty-eight percent of the replicate 1 vectors, 47 % of the replicate 

2 vectors and 41 % of the replicate 3 vectors in the PCS-2012 PA showed spallings releases 

(Zeitler 2012). In comparison, the PABC-2009 had 34 %, 41 %, and 36 % of the vectors in the 

three replicates showing releases (Kirchner 2009). The number of zero-releases was high enough 

to reduce the effectiveness of the regression analysis. Zero values in the data tend to negate the 

assumption of linear regression that errors (residuals) are normally distributed. In addition, the 

distribution along the independent axis of the ranked parameter values associated with the zeros 

can exert a lot of control on the slope of the line. The ranking of duplicate values (zeros in this 

case) is under the control of the sorting algorithm. 

None of the regression models identified by the stepwise procedure accounted for even 50 % of 

the variability in spalling releases (Tables 8 and 9). Table 8 lists the parameters that showed 

correlation to mean spalling releases after a stepwise ranked regression using data from replicate 

1 of both the PCS-2012 PA and the PABC-2009. The first two parameters from the analysis in 

both the PCS-2012 and the PABC-2009 PA analyses are the same and of similar magnitudes. 

The effective porosity of the Salado halite (S_HALITE:POROSITY) contributed the most to the 

variability in spalling releases across all the replicates, with the particle diameter of the 

disaggregated waste (SPALLMOD:PARTDIAM) being ranked second in two of the three 

replicates. The positive correlation shown by S_HALITE:POROSITY is likely to be due to 

having greater gas pressures under higher porosities due to greater brine flow into the repository. 

A negative correlation between SPALLMOD:PARTDIAM and spalling releases is observed in 

regressions whether releases of zero are included (Tables 8 and 9) or omitted (p = 0.025, Fig. 7; 

from spreadsheet SpalVsPartDiam.xlsx). The negative correlation with 

SPALLMOD:PARTDIAM is probably due to the tendency to have greater fluidization at smaller 

particle diameters. 

The third-ranked parameter in replicate 1, DRZ_PCS:PRMX_LOG (the log of the intrinsic 

permeability in the X direction), contributes about 4 % of the variability in PCS-2012 compared 

to about 3 % in the PABC-2009. The fourth parameter, SPALLMOD:REPIPOR (initial waste 

porosity), was only found to be a significant contributor to the variability in spallings in replicate 

1 of the PCS-2012 PA; it was not identified as a significant contributor in either of the other two 

replicates (Table 9) or in the PABC-2009. The log of the intrinsic permeability within a borehole 

filled with silty sand, BH_SAND:PRMX_LOG, was the last significant contributor to the 

variability in spalling releases in replicate 1. It was also identified as a contributor in the 

regression analyses for replicates 2 and 3. 

There is little consistency among the replicates in the lists of the variables of step 3 or higher. 

These variables contribute only a few percent to the total variability and the ranking of their 

importance to determining spallings releases is thus questionable. CASTILER:PRESSURE is the 

far-field pore pressure in the Castile brine reserve. SHFTL_T1:PRMX_LOG is the logarithm of 

intrinsic permeability (m2

) of lower shaft seal materials from 0 years to 200 years after closure.

WAS_AREA:GRATMICI is the rate of microbial degradation of CPR materials in inundates 

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 21 of 34 

waste. WAS_AREA:SAT_RGAS is the residual gas saturation in the waste emplacement area. 

SPALLMOD:TENSLSTR is the tensile strength of waste and STEEL:CORRMCO2 is the 

inundated corrosion rate of steel when CO2 is not present. While these parameters can perhaps 

influence pressures in the repository or the strength of the waste, and hence spallings releases, 

the lack of consistency across the replicates should be interpreted as evidence against their 

importance in controlling spallings. 

Figure 5. Spalling volume releases from the PABC-2009 and PCS-2012 analyses. 

R = Release (Cubic Meters)

0.0001 0.001 0.01 0.1 1 10 100

Probability Release > R

0.0001

0.001

0.01

0.1

1

10

PCS-2012 PA

PABC-2009 PA

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 22 of 34 

Figure 6. Spalling releases from the PABC-2009 and PCS-2012 analyses. 

Table 8. Stepwise ranked regression analysis for mean spallings releases, replicate 1. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 S_HALITE:POROSITY 0.13 0.37 S_HALITE:POROSITY 0.14 0.34 

 2 SPALLMOD:PARTDIAM 0.21 -0.29 SPALLMOD:PARTDIAM 0.22 -0.28 

 3 DRZ_PCS:PRMX_LOG 0.25 -0.21 CULEBRA:DPOROS 0.26 -0.23 

 4 SPALLMOD:REPIPOR 0.29 -0.20 CASTILER:PRESSURE 0.30 0.19 

 5 BH_SAND:PRMX_LOG 0.32 -0.18 SHFTL_T2:PRMX_LOG 0.33 -0.18 

 6 WAS_AREA:PROBDEG 0.36 0.17 

 7 DRZ_PCS:PRMX_LOG 0.39 -0.18 

 8 S_MB139:PRMX_LOG 0.42 0.16 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

Table 9. Stepwise ranked regression analysis for mean spallings releases, replicates 2 and 3 of PCS-2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 S_HALITE:POROSITY 0.15 0.37 S_HALITE:POROSITY 0.17 0.41 

 2 BH_SAND:PRMX_LOG 0.22 -0.28 SPALLMOD:PARTDIAM 0.27 -0.28 

 3 CASTILER:PRESSURE 0.29 0.27 DRZ_PCS:PRMX_LOG 0.34 -0.26 

 4 SPALLMOD:PARTDIAM 0.34 -0.23 WAS_AREA:SAT_RGAS 0.37 -0.18 

 5 WAS_AREA:GRATMICI 0.37 0.18 SPALLMOD:TENSLSTR 0.40 -0.18 

 6 SHFTL_T1:PRMX_LOG 0.40 0.16 STEEL:CORRMCO2 0.43 0.16 

 7 DRZ_PCS:PRMX_LOG 0.43 -0.16 BH_SAND:PRMX_LOG 0.46 -0.16 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

R = Release (EPA Units)

0.0001 0.001 0.01 0.1 1 10 100

Probability Release > R

0.0001

0.001

0.01

0.1

1

10

PCS-2012 PA

PABC-2009 PA

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 23 of 34 

Figure 7. Non-zero spallings release shows a negative correlation (p = 0.025) with SPALLMOD:PARTDIAM. 

Direct Brine Release 

Direct brine releases (DBR) are releases of contaminated brine originating in the repository and 

flowing up an intrusion borehole during the period of drilling and before the hole is plugged. In 

order for DBR to occur volume-averaged pressure near the borehole must exceed 8 MPa and 

brine saturation in the repository must exceed the residual saturation of the waste material 

(Clayton 2008). Volume averaged brine pressure and brine saturation in the intruded panel 

strongly influence DBR volumes. The panel closure system defined by PCS-2012 impacted both 

of these factors (Camphouse et al. 2012). Overall, brine volumes increased in all of the PCS2012 DBR scenarios modeled by BRAGFLO except one (Malama 2012). 

SPALLMOD:PARTDIAM (Rank)

0 20 40 60 80 100

Spallings Release (Rank)

60

70

80

90

100

110

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Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 24 of 34 

Figure 8. Mean DBR volumes for PABC-2009 and PCS-2012 paired by replicate and vector. The line 

represents equality in release volumes. 

Of the pathways for release DBR showed the greatest increase over the PABC-2009 results (Fig. 

1). However, the increase in the mean DBR release was controlled by very large increases in a 

few vectors, rather than an overall increase in most or all vectors, as shown in Fig. 8. PCS-2012 

had eleven vectors across all three replicates that had negligible (<0.0001) release volumes while 

the PABC-2009 had nine such cases. 

The PABC-2009 analysis shows that four variables (SOLMOD3:SOLVAR, 

CASTILER:PRESSURE, STEEL:CORRMCO2 and GLOBAL:PBRINE) account for more than 

50 % of the uncertainty in DBR (Tables 10 and 11). These variables are also the four most 

important contributors to variability in DBR in all three replicates of the PCS-2012 PA analysis 

although variables are ranked differently from those of the PABC-2009 analysis. Somewhat 

surprisingly, only one of the new sampled parameters, PCS_T3:POROSITY, appeared in the 

analysis, and DBR was only weakly correlated with it in one replicate (replicate 3). 

SOLMOD3:SOLVAR is a “solubility multiplier” that represents uncertainty in solubilities for all 

actinides in the +III oxidation state (Xiong et al. 2009). STEEL:CORRMCO2 is the inundated 

corrosion rate for steel in the absence of CO2. The corrosion of iron is expected to produce 

hydrogen but at the same time it consumes water. When the repository is flooded with brine from 

the intrusion of a brine pocket it is likely that the influence of STEEL:CORRMCO2 on DBR 

would be positive since the production of hydrogen would outweigh the minimal impact of the 

consumption of water. However, a negative correlation (p=0.001, spreadsheet 

PCS-2012 PA (m2)

0 20 40 60 80 100

PABC-2009 DBR Volumes (m2)

0

20

40

60

80

100

Information Only
Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 25 of 34 

DBRVsSteelCorr.xlsx) is observed between the ranked variables (Fig. 9 and Table 10), 

suggesting that the corrosion of steel is having its strongest influence when the repository is not 

saturated and DBR releases are expected to be small. CASTILER:PRESSURE is the initial brine 

pore pressure in the Castile. CASTILER:PRESSURE and GLOBAL:PBRINE control the 

frequency with which Castile brine intrudes the repository due to a drilling event and the initial 

pressure of that brine, thus their positive correlation with DBR is obvious. 

Figure 9. A significant (p = 0.001) negative correlation is observed between the ranks of Direct Brine Releases 

and the corrosion rate of steel (STEEL:CORRMCO2) in replicate 1 of the PCS-2012 PA. 

There is no consistency in the names and orderings of the remaining parameters contributing to 

the uncertainty in DBR. These parameters each contribute about 1-5 % of the variability in DBR. 

Once the contribution of a parameter to the uncertainty drops to a few percent the correlation 

could arise from sampling rather than a mechanistic relationship. For example, 

BOREHOLE:DOMEGA, the drill string angular velocity, and the SPALLMOD (spallings 

model) properties should have no impact on DBR releases. That is not to say that all of these 

correlations are spurious. The remainder have the potential for impacting gas pressure, saturation 

levels or concentrations of radionuclides in the brine. For example, S_HALITE:POROSITY is 

the effective porosity in intact halite and it contributes from 1% to 3% to the variability in DBR. 

It influences the gas pressures in the repository, which in turn impacts the volume of direct brine 

releases. However, S_HALITE:POROSITY can have both positive and negative impacts on 

DBR. On the one hand, higher porosities could reduce gas pressures and potentially increase the 

inflow of brine with subsequent gas generation within the inundated panel, increasing brine 

saturation above the residual saturation levels and thus yielding higher direct brine releases. On 

the other hand, lower porosities could maintain higher gas pressures in the repository for longer 

periods and thus increase the release volumes. The overall impact is likely controlled by the 

timing of inundation and subsequent penetration of the repository, which is under the control of 

the aleatory parameters and hence not accounted for in the regression models. Another 

STEEL:CORRMCO2 (Rank)

0 20 40 60 80 100

Direct Brine Release (Rank)

0

20

40

60

80

100

120

Information Only
Sensitivity of PCS-2012 PA Releases to Parameters 

 Page 26 of 34 

parameter, WAS_AREA:SAT_WICK, represents the increase in brine saturation due to capillary 

forces. The negative correlation is probably the result of an increase in iron consumption and 

thereby a reduction of brine as WAS_AREA:SAT_WICK increases. However, increased 

saturation could also lead to higher gas generation and thus greater direct brine releases, so again 

the impact of the parameter could be positive or negative depending on circumstances. 

WAS_AREA:SAT_WICK was ranked seventh, fifth and eleventh in in this analysis. 

Table 10. Stepwise ranked regression analysis for mean Direct Brine releases. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 CASTILER:PRESSURE 0.15 0.40 SOLMOD3:SOLVAR 0.19 0.42 

 2 SOLMOD3:SOLVAR 0.28 0.36 CASTILER:PRESSURE 0.35 0.40 

 3 STEEL:CORRMCO2 0.40 -0.33 GLOBAL:PBRINE 0.45 0.33 

 4 GLOBAL:PBRINE 0.47 0.26 STEEL:CORRMCO2 0.55 -0.30 

 5 S_HALITE:POROSITY 0.50 0.17 S_MB139:RELP_MOD 0.58 -0.18 

 6 S_MB139:RELP_MOD 0 .53 -0.19 WAS_AREA:BIOGENFC 0.60 0.15 

 7 WAS_AREA:SAT_WICK 0.56 -0.15 S_HALITE:POROSITY 0.63 0.15 

 8 DRZ_1:PRMX_LOG 0.58 0.15 CONC_PCS:SAT_RGAS 0.65 -0.14 

 9 SOLMOD4:SOLVAR 0.60 0.16 S_MB139:PRMX_LOG 0.67 0.14 

 10 SHFTU:SAT_RBRN 0.62 0.14 WAS_AREA:SAT_WICK 0.69 -0.14 

 11 BOREHOLE:DOMEGA 0.64 -0.14 

 12 WAS_AREA:BIOGENFC 0.66 0.13 

 13 WAS_AREA:SAT_RBRN 0.67 -0.13 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

Table 11. Stepwise ranked regression analysis for mean Direct Brine releases, replicates 2 and 3 of PCS-2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 SOLMOD3:SOLVAR 0.16 0.43 CASTILER:PRESSURE 0.20 0.41 

 2 CASTILER:PRESSURE 0.31 0.31 SOLMOD3:SOLVAR 0.38 0.42 

 3 STEEL:CORRMCO2 0.44 -0.38 STEEL:CORRMCO2 0.48 -0.32 

 4 GLOBAL:PBRINE 0.52 0.29 GLOBAL:PBRINE 0.56 0.28 

 5 WAS_AREA:SAT_WICK 0.57 -0.22 CULEBRA:MINP_FAC 0.60 0.22 

 6 DRZ_PCS:PRMX_LOG 0.62 0.22 DRZ_PCS:PRMX_LOG 0.65 0.20 

 7 WAS_AREA:SAT_RBRN 0.65 -0.18 WAS_AREA:SAT_RBRN 0.68 -0.18 

 8 S_MB139:PRMX_LOG 0.67 0.17 DRZ_1:PRMX_LOG 0.70 0.17 

 9 WAS_AREA:PROBDEG 0.70 -0.17 CASTILER:COMP_RCK 0.72 -0.16 

 10 DRZ_1:PRMX_LOG 0.72 0.14 SPALLMOD:REPIPOR 0.74 0.13 

 11 SHFTU:PRMX_LOG 0.74 0.16 WAS_AREA:SAT_WICK 0.75 -0.12 

 12 BH_SAND:PRMX_LOG 0.76 -0.14 PCS_T3:POROSITY 0.76 0.11 

 13 S_HALITE:POROSITY 0.78 0.14 SPALLMOD:TENSLSTR 0.78 0.11 

 14 CULEBRA:APOROS 0.80 0.12 GLOBAL:CLIMTIDX 0.79 0.10 

 15 SPALLMOD:REPIPOR 0.81 0.11 S_HALITE:POROSITY 0.80 0.10 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

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Culebra Releases 

The regression results for releases both to and from the Culebra in the PCS-2012 analysis are 

very similar to those computed for the PABC-2009, with the regression models for releases from 

the Culebra for replicates 2 and 3, respectively, being identical (see Kirchner 2009). In the 

PABC-2009 and the PCS-2012 PA the percentages of vectors that had Culebra releases of zero 

were 9 %, 7 % and 6 %, respectively for replicates 1, 2 and 3. The releases of zero are due, for 

the most part, to transport rates frequently being too small to enable contaminants to reach the 

boundary within the simulation period, 10,000 years. The times of the intrusions giving rise to 

flows to the Culebra are also likely to influence whether or not such releases occur. These times 

are not represented in the “sampled” input parameters and thus cannot be associated with the 

releases in a sensitivity analysis. 

 The regression models show that 74 % to 82 % of the variability in Culebra releases is 

accounted for across the three replicates of the PCS-2012 PA (Tables 12 and 13). 

BH_SAND:PRMX_LOG and the Kds for the radionuclides appear to be the most important 

factors controlling Culebra releases to the Culebra from the repository within the Culebra to the 

boundary of the WIPP site. The dominant parameter in the PCS-2012 analysis of releases both to

the Culebra and from the Culebra is BH_SAND:PRMX_LOG, the logarithm of intrinsic 

permeability in the X-direction for a sand-filled borehole (Tables 12, 13, 14 and 15). 

Conceptually, the flow of brine up the borehole (and thus to the Culebra) should be positively 

influenced by increasing values for BH_SAND:PRMX_LOG (Stein and Zelinski 2003). This 

parameter accounts for 41 % to 55 % of the variability in releases from the Culebra and 80 % to 

84 % of the releases to the Culebra across the three replicates of the PCS-2012 PA. 

Table 12. Stepwise ranked regression analysis for mean Culebra releases. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BH_SAND:PRMX_LOG 0.46 0.63 BH_SAND:PRMX_LOG 0.46 0.63 

 2 (Composite):MKD_U 0.58 -0.32 (Composite):MKD_U 0.58 -0.36 

 3 PCS_T2:POR2PERM 0.62 -0.20 DRZ_PCS:PRMX_LOG 0.61 0.17 

 4 DRZ_PCS:PRMX_LOG 0.65 0.17 SOLMOD4:SOLVAR 0.63 -0.17 

 5 CULEBRA:HMBLKLT 0.67 0.15 CULEBRA:HMBLKLT 0.65 0.16 

 6 SOLMOD4:SOLVAR 0.70 -0.16 CULEBRA:APOROS 0.67 -0.14 

 7 CULEBRA:APOROS 0.71 -0.13 (Composite):MKD_PU 0.69 -0.14 

 8 STEEL:CORRMCO2 0.73 -0.12 STEEL:CORRMCO2 0.71 -0.13 

 9 GLOBAL:TRANSIDX 0.74 0.11 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

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Table 13. Stepwise ranked regression analysis for mean Culebra releases, replicates 2 and 3 of PCS-2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BH_SAND:PRMX_LOG 0.55 0.71 BH_SAND:PRMX_LOG 0.41 0.62 

 2 GLOBAL:OXSTAT 0.67 0.22 (Composite):MKD_U 0.55 -0.31 

 3 CULEBRA:MINP_FAC 0.70 -0.16 CULEBRA:APOROS 0.59 -0.21 

 4 (Composite):MKD_U 0.73 -0.21 WAS_AREA:SAT_WICK 0.62 -0.15 

 5 CONC_PLG:PRMX_LOG 0.74 -0.12 GLOBAL:CLIMTIDX 0.64 0.18 

 6 STEEL:CORRMCO2 0.75 -0.11 STEEL:CORRMCO2 0.67 -0.17 

 7 CULEBRA:HMBLKLT 0.77 0.11 CASTILER:PRESSURE 0.69 0.14 

 8 AM+3:MKD_AM 0.78 -0.12 PCS_T1:PORE_DIS 0.71 0.14 

 9 CASTILER:PRESSURE 0.79 0.11 DRZ_PCS:PRMX_LOG 0.73 0.12 

 10 PHUMOX3:PHUMCIM 0.80 0.11 PCS_T1:PRMX_LOG 0.74 0.12 

 11 WAS_AREA:SAT_RGAS 0.81 -0.10 S_HALITE:COMP_RCK 0.75 0.11 

 12 CULEBRA:APOROS 0.82 -0.10 CASTILER:PRMX_LOG 0.76 0.11 

 13 S_MB139:SAT_RBRN 0.77 -0.11 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

There are three additional parameters found across all of the regression models for the three 

replicates of the PCS-2012 PA: Composite:MKD_U, CULEBRA:APOROS and 

STEEL:CORRMCO2. The parameter ranked second in two of the three analyses for releases 

from the Culebra is the matrix/water partition coefficient (Kd) for uranium, Composite:MKD_U. 

Composite:MKD_U is assigned U+6:MKD_U or U+4:MKD_U depending on the oxidation state 

for uranium. These are the Kds for the +VI and +IV oxidation states of uranium, respectively. 

The negative correlation between Culebra releases and Composite:MKD_U was expected 

because high values of Kd suppress the transport of the radionuclide. In replicate 2, 

Composite:MKD_U is ranked fourth, preceded by two variables (GLOBAL:OXSTAT and 

CULEBRA:MINP_FAC) that do not appear in the regression models for the other two replicates. 

Composite:MKD_U contributes only to the regression model for releases to the Culebra in 

replicate 1, where it ranks sixth in importance (Table 14). 

Culebra advective porosity (the fracture volume per unit volume of porous media, 

CULEBRA:APOROS), is ranked seventh, twelfth and third in the three replicates. Negative 

correlations are expected for this variable because it affects the velocity of transport. Low 

porosities increase the rate of transport as compared to high porosities and hence increase the 

likelihood of a release at the land withdrawl boundary within 10,000 years. Using the data from 

replicate 3, which ranked CULEBRA:APOROS third, a simple regression between Culebra 

releases and CULEBRA:APOROS (spreadsheet CulVsAporosRep3NZ.xlsx) shows a 

significance of p = 0.01 (releases of 0 excluded, Fig. 10) or p = 0.06 (spreadsheet 

CulVsAporosRep3.xlsx, releases of 0 included). CULEBRA:APOROS does not contribute to the 

variability of releases to the Culebra. 

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 Page 29 of 34 

Figure 10. Correlation between the ranks of CULEBRA:APOROS (a unitless parameter) and Culebra 

Release (excluding zeros) in the PCS-2012 results for replicate 3. 

STEEL:CORRMCO2 is the inundated corrosion rate for steel in the absence of CO2. The 

corrosion of iron is expected to produce hydrogen but at the same time it consumes water. When 

the repository is flooded with brine from the intrusion of a brine pocket it is likely that the 

influence of STEEL:CORRMCO2 on Culebra releases would be positive since the production of 

hydrogen would outweigh the minimal impact of the consumption of water. However, a negative 

correlation is observed between the ranked variables (Tables 12 and 13), suggesting that the 

corrosion of steel is having its strongest influence when the repository is not saturated. 

STEEL:CORRMCO2 also contributes 1 % to 3 % of the variability in releases to the Culebra 

(Tables 14 and 15). 

CULEBRA:APOROS (Rank)

0 20 40 60 80 100

Culebra Release (Rank)

0

20

40

60

80

100

120

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 Page 30 of 34 

Table 14. Stepwise ranked regression analysis for mean releases to Culebra, replicate 1. 

Expected Normalized Release 

PCS-2012 Replicate 1 PABC-2009 Replicate 1 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BH_SAND:PRMX_LOG 0.80 0.90 BH_SAND:PRMX_LOG 0.80 0.91 

 2 STEEL:CORRMCO2 0.82 -0.16 STEEL:CORRMCO2 0.82 -0.15 

 3 DRZ_PCS:PRMX_LOG 0.84 0.13 DRZ_PCS:PRMX_LOG 0.84 0.13 

 4 CASTILER:PRESSURE 0.84 0.08 CASTILER:PRESSURE 0.84 0.08 

 5 SOLMOD3:SOLVAR 0.85 0.11 SOLMOD3:SOLVAR 0.85 0.10 

 6 (Composite):MKD_U 0.86 0.09 (Composite):MKD_U 0.86 0.08 

 7 PCS_T2:POR2PERM 0.87 -0.09 SOLMOD4:SOLVAR 0.86 -0.08 

 8 WAS_AREA:PROBDEG 0.87 0.09 GLOBAL:TRANSIDX 0.87 0.08 

 9 GLOBAL:CLIMTIDX 0.88 -0.08 

 10 SOLMOD4:SOLVAR 0.88 -0.08 

 11 GLOBAL:TRANSIDX 0.89 0.08 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

Table 15. Stepwise ranked regression analysis for mean releases to Culebra, replicates 2 and 3 of the PCS2012. 

Expected Normalized Release 

PCS-2012 Replicate 2 PCS-2012 Replicate 3 

Stepa Variableb R2c SRRCd Variable R2 SRRC 

 1 BH_SAND:PRMX_LOG 0.83 0.92 BH_SAND:PRMX_LOG 0.84 0.90 

 2 SOLMOD3:SOLVAR 0.86 0.17 STEEL:CORRMCO2 0.86 -0.15 

 3 CASTILER:PRESSURE 0.88 0.12 CASTILER:PRESSURE 0.88 0.15 

 4 STEEL:CORRMCO2 0.89 -0.12 CASTILER:COMP_RCK 0.90 -0.12 

 5 PCS_T1:PORE_DIS 0.90 0.08 PCS_T2:POR2PERM 0.90 0.08 

 6 WAS_AREA:PROBDEG 0.90 -0.08 a Steps in stepwise regression analysis c Cumulative R2

 value with entry of each variable into regression model 

b Variables listed in order of selection d Standardized Rank Regression Coefficient 

Summary and Conclusions 

Of the four pathways of release, DBR and Spallings showed the greatest difference between the 

PABC-2009 and the PCS-2012 PA results. Releases from both of these pathways are controlled 

by pressures within the repository, although DBR is also controlled by brine saturation. In both 

cases it is the volume of the releases that account for the difference in activity releases between 

the PCS-2012 and PABC-2009 analyses since the other factors which impact these releases, such 

as inventory, solubilities, and drilling parameters were the same in both analyses. Surprisingly, 

correlations between these releases and the new PCS sampled parameters were nearly absent, 

with only a weak correlation found between PCS_T3:POROSITY and DBR in one replicate. The 

release tables generated by BRAGFLO for DBR showed increases in release volumes for all 

scenarios but one (Malama 2012), but the PCS-2012 DBR volumes were not consistently larger 

than those for the PABC-2009 analysis. It appears that the difference in mean DBR releases is 

due to just a few vectors having considerably larger releases in the PCS-2012 PA. 

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 Page 31 of 34 

There were only small differences between the PCS-2012 and PABC-2009 sensitivity analyses 

for any of the release pathways (direct brine releases, spallings releases, releases from the 

Culebra , and cuttings and cavings releases). Cuttings and caving releases remain the primary 

contributor to total releases. BOREHOLE:TAUFAIL controls about 98 % of the variability in 

mean cuttings and cavings through its impact on cavings and shows a highly non-linear 

relationship with the volume of cuttings and cavings produced. The drill string angular velocity 

(BOREHOLE:DOMEGA) may also have a small effect on cuttings and cavings release volumes, 

again through its impact on cavings. 

This analysis continues to show many weak correlations that are either undoubtedly (e.g. the 

correlation between BOREHOLE:DOMEGA and the SPALLMOD properties correlated with 

DBR releases) or probably spurious. To some extent these arise because of the request by EPA to 

see more variables listed in the analyses (EPA 2004), which is accomplished at the expense of 

having more parameters retained in the regression model than may be necessary and by setting 

the regression parameters to allow a moderate number of false-positive results. 

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 Page 32 of 34 

References: 

Camphouse, R.C., Clayton, D.J., Kicker, D.C., and Pasch, J.J. 2011. Summary Report for the 

AP-151 (PC3R) Performance Assessment, Revision 1. Sandia National Laboratories, 

Carlsbad, NM. ERMS 555489. 

Camphouse, C. 2012a. Analysis Plan for the 2012 WIPP Panel Closure System Performance 

Assessment, Revision 0. July 2, 2012. Carlsbad, NM: Sandia National Laboratories. 

ERMS #557726 

Camphouse, R.C. 2012b. An overview of the BRAGFLO two-phase flow parameters used to 

model the run-of-mine salt panel closures implemented in the PCS-2012 PA. Memo to 

Paul Shoemaker dated June 7, 2012. C. ERMS 557653. 

Camphouse, R. C. 2012c. A summary of parameters to be implemented in the PCS-2012 PA. 

Memo to Janis Trone dated July 12, 2012. 

Camphouse, R.C., Kicker, D.C., Kirchner, T.B., Long, J.J., Malama, B., and Zeitler, T.R. 2012. 

Summary Report and Run Control for the 2012 WIPP Panel Closure System Performance 

Assessment. Sandia National Laboratories, Carlsbad, NM. 

Clayton, D. J. 2008. Analysis Plan for the Performance Assessment for the 2009 Compliance 

Recertification Application. Sandia National Laboratories, Carlsbad, N.M. ERMS 

#547515. 

Clayton, D.J., Camphouse, R. C., Garner, J. W., Ismail, A. E., Kirchner, T. B., Kuhlman, K. L., 

and Nemer, M. B.. 2010. Summary Report of the CRA-2009 Performance Assessment 

Baseline Calculation, Revision 1. Sandia National Laboratories. Carlsbad, New Mexico, 

ERMS 553039. 

DOE (U.S. Department of Energy). 1996. Title 40 CFR Part 191 Compliance Certification 

Application for the Waste Isolation Pilot Plant. DOE/CAO-1996-2184. U.S. Department 

of Energy, Waste Isolation Pilot Plant, Carlsbad Area Office. Carlsbad, NM. 

U.S. Department of Energy (DOE) 2004. Title 40 CFR Part 191 Compliance Recertification 

Application for the Waste Isolation Pilot Plant, , 10 vols., U.S. Department of Energy 

Waste Isolation Pilot Plant, Carlsbad Area Office, Carlsbad, NM. DOE/WIPP 2004-3231. 

DOE (U.S. Department of Energy), 2011. Panel Closure System Design, Planned Change 

Request to the EPA 40 CFR Part 194 Certification of the Waste Isolation Pilot Plant. U.S. 

Department of Energy, Carlsbad Field Office. Carlsbad, New Mexico. 

Dunagan, S. 2004. Analysis Package for Cuttings and Cavings: Compliance Recertification 

Application. Revision 1.0. Sandia National Laboratories. Carlsbad, NM. ERMS 533541. 

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 Page 33 of 34 

Ismail, A.E. and J.W. Garner. 2010. Analysis Package for Salado Transport Calculations: CRA2009 Performance Assessment Baseline Calculation. Sandia National Laboratories. 

Carlsbad, NM. 

Kirchner, T. 2004a. “Stepwise regression analysis of the final release for each release 

mechanism, in response to C-23-18, Revision 2.” Memorandum to David Kessel dated 

December 6, 2004. Sandia National Laboratories. Carlsbad, NM. ERMS 537992. 

Kirchner, T. 2004b. CRA Reponse Activity, Tracking Number 09/02/04Q, DOE Response to 

Comment C-23-18, Revision 1, ERMS 538114 

Kirchner, T. 2009. Sensitivity of the CRA-2009 Performance Assessment Baseline Calculation 

Releases to Parameters. Sandia National Laboratories. Carlsbad, NM. 

Malama, Bwalya. 2012. Analysis package for Direct Brine releases: 2012 Panel Closure System 

Performance Assessment (PCS_2012 PA). Sandia National Laboratories. Carlsbad, NM. 

ERMS 558226

Stein, J. S., and W. Zelinski. 2003. Analysis Package for BRAGFLO: Compliance 

Recertification Application. Analysis Report. Sandia National Laboratories, Carlsbad, 

NM. ERMS 530163. 

U.S. Environmental Protection Agency (EPA). 1998. 40 CFR 194, Criteria for the Certification 

and Recertification of the Waste Isolation Pilot Plant’s Compliance with the Disposal 

Regulations: Certification Decision: Final Rule, Federal Register. Vol. 63, 27354-27406. 

ERMS 251924. 

U. S. Environmental Protection Agency (EPA). 2004. “EPA’s Completeness Comments, 3rd

Set.” Letter from Elizabeth Cotsworth to Dr. R. Paul Detwiler. September 2, 2004. 

ERMS 536771. 

Vugrin, E.D. and S.C. Dunagan. 2006. Analysis Package for the Impact Assessment of the 

Redesigned Panel Closure System, Rev. 0. Sandia National Laboratories. Carlsbad, NM. 

ERMS 543865. 

Xiong, Y.L., L.H. Brush, A.E. Ismail, and J.J. Long. 2009. Uncertainty Analysis of Actinide 

Solubilities for the WIPP CRA-2009 PABC. Analysis report, December 1, 2009. 

Carlsbad, NM: Sandia National Laboratories. ERMS #552500. 

Zeitler, T. 2012. CCDFGF Analysis Package for the AP-161 (PCS-2012) Performance 

Assessment. Sandia National Laboratories. Carlsbad, NM. 

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Attachment 1. CD containing the CCDFGF_Analysis.mdb and CCDFGF_Data.mdb databases 

The attached CD contains copies of the CCDFGF_Analysis.mdb and CCDFGF_Data.mdb 

databases within the WinZip file AP161_ANALYSISDATABASE.zip and the Excel 

spreadsheets in AP161SimpleRegressionSpreadsheets.zip. To utilize these databases or 

spreadsheets they must be extracted from the zip file and installed on a PC running Microsoft 

Windows XP or a compatible operating system. CCDFGF_Analysis.mdb contains the queries 

and code used in the analysis and contains links to some of the tables in CCDFGF_Data.mdb. 

CCDFGF_Data.mdb contains the data. These links can be updated using the Access menu item 

External Data/Linked Table Manager. The means are computed and the STEPWISE input file is 

generated by selecting the menu button in CCDFGF_Analysis labeled “Compute Vector Means 

for STEPWISE”. This process also produces STEPWISE input files for the parameters. A copy 

of AP161_ANALYSISDATABASE.ZIP and AP161SimpleRegressionSpreadsheets.zip are also 

stored in the CMS library LIBAP161_STPW.

Information Only