Sensitivity of PCS-2012 PA Releases to Parameters

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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: ................................................................................................................... ............. 32Sensitivity of PCS-2012 PA Releases to Parameters

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### Introduction The Waste Isolation Pilot Plant (WIPP), loca ted in southeastern New Mexico, has been developed by the U.S. Department of En ergy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) wa ste. Containment of TRU waste at the WIPP is regulated by the U.S. Environmental Protection Agency (EPA) accordi ng 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 Certif ication Criteria in Title 40 CFR Part 194 by means of performance assessment (PA) calcula tions performed by Sandi a National Laboratories (SNL). WIPP PA calculations estimate the probability and cons equence of potential radionuclide releases from the repository to the a ccessible environment for a regulatory period of 10,000 years after facility closure. The models us ed in PA are maintained and updated with new information as part of an ongoing process. Improved information regarding important WIPP features, events, and processes t ypically results in refinements a nd modifications to PA models and the parameters used in them. Planned cha nges to the repository and/or the components therein also result in updates to WIPP PA models. WIPP PA m odels are used to support the repository recertification process th at occurs at five-year interval s 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 cl osures are included in WIPP PA models principally because they are a pa rt of the disposal system, not because they play a substantive role in inhi biting the release of radionuclides to the outside e nvironment (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 transu ranic 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 e ngineering 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 f eet of ROM salt with barriers at each end. The ROM salt is generated from ongoing mining operati ons at the WIPP and may be compacted and/or moistened as it is emplaced in a panel entry. The barriers c onsist of ventilation bulkheads, similar to those currently used in the pane ls as room closures.
The DOE has submitted a planned change request (PCR) to the EPA requesting that EPA modify Condition 1 of the Final Cer tification 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 comp leted 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).Sensitivity of PCS-2012 PA Releases to Parameters

Page 4 of 34 This report documents the analysis of the sensitiv ity of releases to parameters sampled using the code LHS for the PCS-2012 PA. Results are co mpared with those from the PABC-2009 PA. Variability in the releases computed using CCDFG F 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 stochast ic 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 pr ocesses 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 expl ain sensitivity of the results to the changes implemented in the models or constant (fixed) model para meters following the PABC-2009 PA. Likewise, variability due to the stochastic processes cannot be partitione d among the aleatory parameters because all aleatory parameters are independently sampled in all futures and because the mean releases computed across the 10,00 0 futures are regresses against the parameters and these means subsume the within-vector (aleat ory) 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 releas es 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 ar e provided on the attached CD_ROM and are also stored in the CMS library LI BAP161_STPW in the compressed file AP161SimpleRegressionSpeadsh eets.zip (Attachment 1).

WIPP PA employs stepwise linear multiple regressi on 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 regre ssion models is constructed, starting with the input parameter that has the strongest simple correlation with the out put variable. Partial correlations between the output and the remaini ng variables are then computed. The partial correlations remove the linear eff ects of variables already include d in the model. The variable having the largest significant partial correlati on coefficient is added next, and the partial correlations for the remaining input variables ar e recomputed. Significance is determined using an F-test, and the significance level for a dding 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 va riability 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 betwee n the input and output vari ables. This process is repeated until there are no variables remaining having significant correlations with the outputSensitivity of PCS-2012 PA Releases to Parameters

Page 5 of 34 variable. Variables excluded from the regres sion model contribute no st atistically significant information in relation to the unexplained variab ility 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 regr ession coefficient, i.e. the linear contribution of an input to the prediction of the output variable, mu st be statistically distinguishabl e 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 zer o. The hypothesis is not rejected when random effects can give rise to the obser ved regression coefficient with probability  out . The random effects are caused by the stochastic va riability contributed by the input variables not in the regression model. In other words, the hypothes is 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% c onfident 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 cau ses 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 anal ysis. 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 respon se is non-linear but monotonic, replacing the values of the data with their ranks tends to linear ize 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 represen ting 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 predic t values of an output va riable given an input variable, the results are usually well suited for rank ing the importance of th e contributions of the input variables to the respons e of the output variable. The STEPWISE procedure has the functionality to perform ranked regressions. For the cases described below, the ranked regressions showed stronger corr elations than the regressions based on the unranked data. This result suggests that there are non-linear relati onships between the depe ndent and independent variables, but it does not eliminate the possibility that ther e are also non-monotonic relationships.Sensitivity of PCS-2012 PA Releases to Parameters

Page 6 of 34 Ranked regression was used to evaluate the sens itivity of the output variables to the sampled parameters. Analysts examined scatter plots of the dependent versus independent ranked variables to determine if th ere were any obvious non-monoton ic relationships. Obvious nonmonotonic relationships were not found although there are case s involving inputs that are categorized as discrete variables (e.g. GLOB AL: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 re gression 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 importan ce of the parameters these issues are probably not significant.

Most of the regression models pr oduced 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 in significance can arise because the output variable has a low functiona l 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 sa y that these non-signifi cant 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-monotoni c then the regression analysis might fail to properly identify that vari able’s importance. This possibility is unlikely for total releases and cuttings and cavings releases because the R 2 value (coefficient of determination) indicates that more than 83% of the variability in the output variables has been accounted for by the liste d input variables.

Several of the parameters that appear in th e model often contribute very little to the R 2 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 percen t to the variability in an outp ut from one replicate may show different rankings, or can even be absent, in another re plicate. Thus, it is difficult to assess the importance of the parameters that improve the re gression model very littl e and, in reality, they may have no importance at all. Therefore, only the parameters that app ear to have significant impacts on the regression model w ill be explained in detail.

Analyses were performed using Microsoft Ex cel 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 re leases 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 correl ation 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 correl ation 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 resu lts presented herein are documented inSensitivity of PCS-2012 PA Releases to Parameters

Page 7 of 34 Camphouse et al. (2012). The files associated wi th the PCS-2012 PA STEPWISE analysis can be found in the CMS libraries LIBPAP1 61_EVAL, LIBPABC09_STPW, LIBAP161_LHS, LIBSTP and LIBAP161_CCGF (Table 1). These files include the run scripts (EVAL_STP.COM, EVAL_STP_AP161_RAW_R r .COM and EVAL_STP_AP161_RANK_R r .COM, the CCGFDF output files CCGF_AP161_R r .OUT, the input files containing the mean values of the model output variables (STP_AP161_MEANS_R r .TRN), the STEPWISE control files (STP_AP161_RANK_ALL_R r .INP and STP_AP161_RAW_ALL_R r .INP), the input file containing the sampled parameters (STP_AP161_LHS_R r .TRN) and the results of the regression analysis (STP_AP161_RANK_R r .TXT and STP_AP161_RAW_R r .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_R r .TRN.TRN, were generated from the CCGF_AP161_R r .OUT files using the Micr osoft® 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 “b ins” are a series of equally spaced intervals on a loga rithmic scale that encompass the data. Each datum is assigned to a bin and the frequency of values within th e bin tabulated. The databases are provided on the attached CD in zipped format and the AP161_ANA LYSISDATABASE.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 ra nked and raw (un-ranked) data.

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

File Library CCDFGF_Analysis.mdb 1 LIBAP161_STPW CCDFGF_Data.mdb 1 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_R 1. COM LIBAP161_STPW EVAL_STP_AP161_RANK_R2.COM LIBAP161_STPWSensitivity of PCS-2012 PA Releases to Parameters

Page 8 of 34 File Library EVAL_STP_AP161_RANK_R3.COM LIBAP161_STPW STP_AP161_LHS_R 1 .TRN LIBAP161_STPW STP_AP161_LHS_R 2 .TRN LIBAP161_STPW STP_AP161_LHS_R 3 .TRN LIBAP161_STPW STP_AP161_MEANS_R1.TRN LIBAP161_STPW STP_AP161_MEANS_R 2 .TRN LIBAP161_STPW STP_AP161_MEANS_R 3 .TRN LIBAP161_STPW STP_AP161_RANK_ALL_R 1 .INP LIBAP161_STPW STP_AP161_RANK_ALL_R 2 .INP LIBAP161_STPW STP_AP161_RANK_ALL_R 3 .INP LIBAP161_STPW STP_AP161_RAW_ALL_R 1 .INP LIBAP161_STPW STP_AP161_RAW_ALL_R 2 .INP LIBAP161_STPW STP_AP161_RAW_ALL_R 3 .INP LIBAP161_STPW STP_AP161_RANK_R 1 .TXT LIBAP161_STPW STP_AP161_RANK_R 1 .SP LIBAP161_STPW STP_AP161_RANK_R 2 .TXT LIBAP161_STPW STP_AP161_RANK_R 2. SP LIBAP161_STPW STP_AP161_RANK_R 3 .TXT LIBAP161_STPW STP_AP161_RANK_R3.SP LIBAP161_STPW STP_AP161_RAW_R 1 .TXT LIBAP161_STPW STP_AP161_RAW_R1 2.SP LIBAP161_STPW STP_AP161_RAW_R 2 .TXT LIBAP161_STPW STP_AP161_RAW_R 2 .SP LIBAP161_STPW STP_AP161_RAW_R 3 .TXT LIBAP161_STPW STP_AP161_RAW_R 3 .SP LIBAP161_STPW STEPWISE_PA96_2.EXE LIBSTP

1 Compressed and stored in AP161_ANALYSISDATABASE.ZIP
The input files for STEPWISE use short names ra ther 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.Sensitivity of PCS-2012 PA Releases to Parameters

Page 9 of 34 Table 2. Material and property values associated wi th the variable names used in the PCS-2012 PA sensitivity analysis. References in this table ref er 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_SA ND 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. Defi ned 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 inSensitivity of PCS-2012 PA Releases to Parameters

Page 10 of 34 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 selectingtransmissivity 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 timeduration 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 regionSensitivity of PCS-2012 PA Releases to Parameters

Page 11 of 34 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 statesSensitivity of PCS-2012 PA Releases to Parameters

Page 12 of 34 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-20for 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 forSensitivity 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 tr ansformation of the variable GLOBAL:OXSTAT, the indicator variable for ox idation states of uranium and plutonium. GLOBAL:OXSTAT is sampled as a [0,1) uniform di stribution 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 K d s for the +VI and +IV oxida tion states of uranium and plutonium, respectively. A K d value represents the matrix:water partitioning coefficient. If GLOBAL:OXSTAT is 0 then CMKDU is assi gned U+6:MKD_U and CMKDPU is assigned PU+4:MKD_PU, i.e. the K d s for the +VI and +IV oxidation st ates of uranium and plutonium, respectively. If GLOBAL:OXS TAT is 1 then CMKDU is assigned U+4:MKD_U and CMKDPU is assigned PU+3:MKD_PU, i.e. the K d s for the +IV and +III oxida tion states of uranium and plutonium, respectively. In the discussion be low 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 Pa nel 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 comp letely 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 undert aken 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 th e 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 impa ct 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_T 3, particularly their implementation in regardSensitivity of PCS-2012 PA Releases to Parameters

Page 14 of 34 to BRAGFLO capillary pressure modeling, is gi ven in Camphouse (2012b). In addition to the sampled parameters listed in Table 2, there we re 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 al l 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 10 8 Pa has been used for all BRAGFLO materials since the CCA) PCT_A Threshold Capill ary Pressure Linear Parameter PCT_EXP Threshold Capillar y Pressure Exponential Parameter PO_MIN Minimum Brine Pressure for Capillary Model 3 (CAP_MOD = 3 has never been used in PA) PRMY_LOG 1 Log of Intrinsic Permeability, Y-Direction PRMZ_LOG 1 Log of Intrinsic Permeability, Z-Direction RELP_MOD Relative Permeability Model Number

1 Assigned the sampled values of PRMX_LOG (Camphouse 2012c).Sensitivity of PCS-2012 PA Releases to Parameters

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 an alysis 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_RBR N, SAT_RGAS), PCS_T2 (properties POR2PERM and POROSITY) and PCS_T3:POROS ITY which were not sampled in the PABC2009. For material CONC_PCS, the following prope rties 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 PA BC-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 R 2 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 rel ease is accounted for in the full re gression model (Tables 4 and 5). The 

### R = Release (EPA Units) 0.0001 0.001 0.01 0.1 1 10 100Probability 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 LimitsSensitivity of PCS-2012 PA Releases to Parameters

Page 16 of 34 difference in the R 2 values for total releases between th e 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 unc ertainty in total nor malized releases is largely (69 % to 74 % in PCS-2012) due to uncertainty in waste shear strength (BOREHOLE:TAUFAIL). Cuttings a nd cavings releases are norma lly combined because they are a direct result of drilling operations. The vol umes of cavings are primarily controlled by shear strength, and the negative co rrelation found in the analysis is expected. There is little consistency in the ranking of th e other variables identified as significant contributors to the variability in total releases. These other variable s 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 effectiv e porosity in intact halite and it contributes 3 % variability in replicate 3 and 2 % in replicates 1 and 2. DR Z_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:MIN P_FAC, the mining transmissivity multiplier. It is not included in the regressi on 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 re lease. Correlations that explain only a few percent of the variation ca n 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 value with entry of each va riable into regression model b Variables listed in order of selection d Standardized Rank Regression CoefficientSensitivity of PCS-2012 PA Releases to Parameters

Page 17 of 34 Table 5. Stepwise ranked regression analysis for mean total releases, replicat es 2 and 3 of PCS-2012. Expected Normalized Release PCS-2012 Replicate 2 PCS-2012 Replicate 3 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 corre lations to cuttings and cavings rel eases 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 st ochastic selection of the waste streams intersected by the borehole. Th e 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 a nd 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 (BORE HOLE: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 variab ility in cuttings and cavings and are spurious because they have no influence on cuttings and ca vings 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 value with entry of each variable into regression model b Variables listed in order of selection d Standardized Rank Regression CoefficientSensitivity of PCS-2012 PA Releases to Parameters

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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 value with entry of each variable into regression model b Variables listed in order of selection d Standardized Rank Regression CoefficientSensitivity of PCS-2012 PA Releases to Parameters

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 100Cuttings and Cavings Volume (m3)

0 5 10 15 20 25

PCS-2012 PA PABC-2009 PA

BOREHOLE:TAUFAIL 0.01 0.1 1 10 100Cuttings and Cavings Releases (EPA Units)

0.00 0.02 0.04 0.06 0.08 0.10 0.12 PCS-2012 PA PABC-2009 PASensitivity of PCS-2012 PA Releases to Parameters

Page 20 of 34 

### Spallings Releases The probability of a spalling release volume less th an 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 releas es (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 norma lly distributed. In addition, the distribution along the independent ax is of the ranked parameter valu es associated with the zeros can exert a lot of control on the slope of the line. The ranking of duplicate va lues (zeros in this case) is under the control of the sorting algorithm.

None of the regression models identified by the stepwise proce dure accounted for even 50 % of the variability in spalling releases (Tables 8 a nd 9). Table 8 lists the parameters that showed correlation to mean spalling releases after a stepwi se ranked regression usi ng data from replicate 1 of both the PCS-2012 PA and the PABC-2009. The fi rst 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_H ALITE:POROSITY) contribu ted the most to the variability in spalling releases across all the re plicates, with the part icle diameter of the disaggregated waste (SPALLMOD:PARTDIAM) be ing 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 fl ow into the repository. A negative correlation between SPALLMOD:PARTDIAM and spalling releases is observed in regressions whether releases of zero are include d (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 ha ve greater fluidization at smaller particle diameters.

The third-ranked parameter in replicate 1, DR Z_PCS:PRMX_LOG (the log of the intrinsic permeability in the X direction), contributes abou t 4 % of the variability in PCS-2012 compared to about 3 % in the PABC-2009

. The fourth parameter, SPA LLMOD: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 si gnificant contributor in e ither of the other two replicates (Table 9) or in the PABC-2009. The log of the intrinsic permeab ility within a borehole filled with silty sand, BH_SAND: PRMX_LOG, was the last signi ficant 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 re serve. SHFTL_T1:PRMX_LOG is the logarithm of intrinsic permeability (m 2 ) 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 inundatesSensitivity 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 CO 2 is not present. While these parameters can perhaps influence pressures in the repository or the streng th of the waste, and he nce 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 100Probability Release > R

0.0001 0.001

0.01

0.1

1 10

PCS-2012 PA PABC-2009 PASensitivity 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 100Probability Release > R

0.0001 0.001

0.01

0.1

1 10

PCS-2012 PA PABC-2009 PASensitivity of PCS-2012 PA Releases to Parameters

Page 23 of 34 Figure 7. Non-zero spallings relea se shows a negative correlation (p = 0.025) with SP ALLMOD:PARTDIAM.


### Direct Brine Release Direct brine releases (DBR) are re leases of contaminated brine or iginating 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 pressu re 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). Overal l, brine volumes increased in all of the PCS2012 DBR scenarios modeled by BRAGF LO except one (Malama 2012). SPALLMOD:PARTDIAM (Rank) 0 2 0 4 0 6 0 8 0 1 0 0Spallings Release (Rank)

60 70 80 90 100 110Sensitivity 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 gr eatest 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 ove rall increase in most or all vectors, as shown in Fig. 8. PCS-2012 had eleven vectors across all thre e replicates that had negligib le (<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 re plicates 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 para meters, PCS_T3:POROSITY, appeared in the analysis, and DBR was only w eakly 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 (Xi ong et al. 2009). STEEL:CORRMCO2 is the inundated corrosion rate for steel in the absence of CO 2 . 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 hydroge n would outweigh the minimal impact of the consumption of water. However, a ne gative correlation (p=0.001, spreadsheet PCS-2012 PA ( m 2 ) 0 2 0 4 0 6 0 8 0 1 0 0PABC-2009 DBR Volumes (m2)

0 20 40 60 80 100Sensitivity of PCS-2012 PA Releases to Parameters

Page 25 of 34 DBRVsSteelCorr.xlsx) is observed between th e ranked variables (Fig. 9 and Table 10), suggesting that the corrosion of st eel is having its strongest influe nce 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:P RESSURE 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) ne gative correlation is observed between the ranks of Direct Brine Releases and the corrosion rate of steel (STEEL:CORR MCO2) in replicate 1 of the PCS-2012 PA. There is no consistency in the names and ordering s 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 uncer tainty 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, a nd 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 rema inder have the potential for im pacting 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 contri butes from 1% to 3% to the variability in DBR. It influences the gas pressures in the repository, which in turn im pacts 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 re duce gas pressures and potentially increase the inflow of brine with subsequent gas generati on within the inundated panel, increasing brine saturation above the residua l saturation levels and thus yieldi ng higher direct brine releases. On the other hand, lower porosities could maintain higher gas pressu res in the repository for longer periods and thus increase the release volumes. The overall impact is like ly controlled by the timing of inundation and subsequent penetration of the repository, which is under the control of the aleatory parameters and hence not account ed for in the regression models. Another STEEL:CORRMCO2 (Rank) 0 2 0 4 0 6 0 8 0 1 0 0Direct Brine Release (Rank)

0 20 40 60 80 100 120Sensitivity 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 in crease in iron consumption and thereby a reduction of brine as WAS_AREA :SAT_WICK increases. However, increased saturation could also lead to highe r 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, fift h and eleventh in in this analysis.

Table 10. Stepwise ranked regression an alysis for mean Direct Brine releases. Expected Normalized Release PCS-2012 Replicate 1 PABC-2009 Replicate 1 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 value with entry of each variable into regression model b Variables listed in order of selection d Standardized Rank Regression CoefficientSensitivity of PCS-2012 PA Releases to Parameters

Page 27 of 34 

### Culebra Releases

The regression results for releases both to a nd from the Culebra in the PCS-2012 analysis are very similar to those computed for the PABC-2009, with the regres sion 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 replicat es 1, 2 and 3. The releases of zero are due, for the most part, to transport rates frequently bei ng too small to enable contaminants to reach the boundary within the simulation period, 10,000 years. Th e 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 parame ters 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 K d s for the radionuclides appear to be the most important factors controlling Culebra releases to the Culebr a 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_L OG, 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_SA ND:PRMX_LOG (Stein a nd 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 value with entry of each variable into regression model b Variables listed in order of selection d Standardized Rank Regression CoefficientSensitivity of PCS-2012 PA Releases to Parameters

Page 28 of 34 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 acro ss all of the regression models for the three replicates of the PCS-2012 PA: Com posite: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/w ater partition coefficient (K d ) 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 K d s for the +VI and +IV oxidation states of uranium, respectively. The negative correlation between Culebra re leases and Composite:MKD_U was expected because high values of K d 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 a ppear 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 sixt h in importance (Table 14).

Culebra advective porosity (the fracture volume per unit volume of porous media, CULEBRA:APOROS), is ranked seve nth, 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 tr ansport as compared to high porosities and hence increase the likelihood of a release at the la nd withdrawl boundary within 10,000 years. Using the data from replicate 3, which ranked CULE BRA:APOROS third, a simple regression between Culebra releases and CULEBRA:APOROS (spreadsheet CulVsAporosRep3NZ.xlsx) shows a significance of p = 0.01 (releases of 0 ex cluded, 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.Sensitivity of PCS-2012 PA Releases to Parameters

Page 29 of 34 Figure 10. Correlation between the ranks of CULEB RA: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 CO 2 . The corrosion of iron is expected to produce hydrogen but at the same time it consumes water. When the repository is flooded with br ine from the intrusion of a br ine pocket it is likely that the influence of STEEL:CORRMCO2 on Culebra releas es would be positive since the production of hydrogen would outweigh the minimal impact of th e consumption of water. However, a negative correlation is observed between the ranked vari ables (Tables 12 and 13), suggesting that the corrosion of steel is having its strongest infl uence when the repository is not saturated. STEEL:CORRMCO2 also contributes 1 % to 3 % of the variabi lity in releases to the Culebra (Tables 14 and 15). 

### CULEBRA:APOROS (Rank) 0 2 0 4 0 6 0 8 0 1 0 0Culebra Release (Rank)

0 20 40 60 80 100 120Sensitivity of PCS-2012 PA Releases to Parameters

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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 Step a Variable b R 2c SRRC d Variable R 2 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 R 2 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 Spa llings showed the greatest difference between the PABC-2009 and the PCS-2012 PA results. Releases fr om 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 rel eases that account for the differe nce in activity releases between the PCS-2012 and PABC-2009 analyses since the othe r 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 f ound between PCS_T3:POROSITY and DBR in one replicate. The release tables generated by BRAGFLO for DBR s howed increases in re lease 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 considerab ly larger releases in the PCS-2012 PA.Sensitivity of PCS-2012 PA Releases to Parameters

Page 31 of 34 There were only small differences between th e PCS-2012 and PABC-2009 sensitivity analyses for any of the release pathways (direct brine re leases, 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. Th e drill string angular velocity (BOREHOLE:DOMEGA) may also have a small e ffect on cuttings and cavings release volumes, again through its impact on cavings.

This analysis continues to show many weak co rrelations that are e ither undoubtedly (e.g. the correlation between BOREHOLE:DOMEGA and the SPALLMOD properties correlated with DBR releases) or probably spurious. To some exte nt 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 regressi on model than may be necessary and by setting the regression parameters to allow a mode rate number of fals e-positive results.Sensitivity of PCS-2012 PA Releases to Parameters

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, Re vision 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 BR AGFLO two-phase flow parameters used to model the run-of-mine salt pa nel 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 Nationa l 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 Natio nal 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 Pl ant, , 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. Pane l 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 Fiel d 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.Sensitivity of PCS-2012 PA Releases to Parameters

Page 33 of 34 Ismail, A.E. and J.W. Garner. 2010. Analysis Pa ckage for Salado Transport Calculations: CRA2009 Performance Assessment Baseline Calc ulation. Sandia National Laboratories. Carlsbad, NM.

Kirchner, T. 2004a. “Stepwise regression anal ysis of the final re lease for each release mechanism, in response to C-23-18, Revision 2.” Memorandum to David Kessel dated December 6, 2004. Sandia National Laborat ories. Carlsbad, NM. ERMS 537992.

Kirchner, T. 2004b. CRA Reponse Activity, Track ing 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 Nationa l 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. Anal ysis 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 Pi lot Plant’s Compliance with the Disposal Regulations: Certification D ecision: Final Rule, Federa l Register. Vol. 63, 27354-27406. ERMS 251924.
U. S. Environmental Protection Agency (EPA). 2004. “EPA’s Completeness Comments, 3 rd 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. Sandi a 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.Sensitivity of PCS-2012 PA Releases to Parameters

Page 34 of 34 Attachment 1. CD containing the CCDFGF_ Analysis.mdb and CCDFGF_Data.mdb databases
The attached CD contains copies of th e CCDFGF_Analysis.mdb and CCDFGF_Data.mdb databases within the WinZip file AP161_ANALYSISDATABASE.zip and the Excel spreadsheets in AP161SimpleRegressionSpread sheets.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 da ta. 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 CCD FGF_Analysis labeled “Compute Vector Means for STEPWISE”. This process also produces S TEPWISE input files for the parameters. A copy of AP161_ANALYSISDATABASE.ZIP and AP161Sim pleRegressionSpreadsheets.zip are also stored in the CMS library LIBAP161_STPW.