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Stefan Finsterle et al. Lawrence Berkeley National Laboratory University of California

Use of the TOUGH Suite of Simulators in Support of Site Characterization and Performance Assessment. Stefan Finsterle et al. Lawrence Berkeley National Laboratory University of California Berkeley, California. Outline. Role of Numerical Modeling Code and Model Development TOUGH Simulators

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Stefan Finsterle et al. Lawrence Berkeley National Laboratory University of California

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  1. Use of the TOUGH Suite of Simulators in Support of Site Characterization and Performance Assessment Stefan Finsterle et al. Lawrence Berkeley National Laboratory University of California Berkeley, California

  2. Outline • Role of Numerical Modeling • Code and Model Development • TOUGH Simulators • TOUGH • iTOUGH2 • History and Impact • Examples • Seepage • Radon • Heater Test • Concluding Remarks

  3. Role of Numerical Modeling • Improve process understanding • Test hypotheses • Understand coupled processes • Understand nonlinearities • Evaluate non-observable quantities • Identify relevant parameters • Design experiments • Identify experimental procedures yielding data that contain information about relevant properties • Analyze data (inverse modeling) • Determine parameters from data • Identify model structure • Make predictions • Deterministic simulations • Uncertainty propagation analysis • Basis for abstraction

  4. Role of Hydrological Modeling in Nuclear Waste Isolation Projects • Coupled thermal-hydrological-chemical-mechanical (THCM) conditions affect integrity of engineered barriers • Groundwater is main vehicle for release of radionuclides and transport to accessible environment • Radionuclide transport is affected by hydrogeochemical conditions • Obtain and demonstrate process understanding • Data integration for site characterization • Predictive modeling for repository design and post-closure performance assessment

  5. Abstraction Conceptual Model Quantification Mathematical Model Discretization Numerical Model Verification Analytical Solution Data Calibration Validation Data Prediction Code and Model Development Problem Code Development Model Development

  6. TOUGH Simulators

  7. That’s TOUGH! TOUGH: Transport Of Unsaturated Groundwater and Heat multidimensional multiphase multicomponent nonisothermal flow and transport fractured-porous media 1D, 2D, 3D liquid, gas, NAPL water, air, VOC, radionuclides heat multiphase Darcy law dual-f, dual-k, MINC, ECM EOS: Equation-Of-State Accurate description of thermophysical properties

  8. TOUGH Summary Description • multiphase flow • pressure, viscous, gravitational forces • capillary pressure, relative permeability • vapor pressure lowering • appearance/disappearance of phases • accurate thermodynamic properties • multicomponent • phase partitioning, dissolution/precipitation • sorption, multiphase diffusion • parent-daughter decay of radionuclides

  9. TOUGH Summary Description (cont.) • nonisothermal • evaporation/condensation • heat convection/conduction/diffusion • radiative heat transfer • semi-analytical heat exchange with confining layers • fractured-porous media • equivalent continuum model • double-porosity • dual-permeability • multiple interacting continua (MINC)

  10. TOUGH Summary Description (cont.) • Integral Finite Differences • Fully coupled (mass and energy) • Fully implicit • Permeability/mobility weighting options • Newton-Raphson • Absolute and relative residual convergence criteria • Direct and iterative linear equation solvers • Special versions: • Research modules • Parallel version (MPI) • http://www-esd.lbl.gov/TOUGH2

  11. Measured parameters Forward model Estimated system state Estimated parameters Inverse model Measured system state Forward vs. Inverse Modeling p* contains n measured parameters (prior information) z contains calculated system response at m calibration points r contains m residuals (z* -z) p contains n parameters to be estimated z* contains observations at m calibration points

  12. Input Parameter Set p TOUGH2 Output Variables z z(p) z/p p=f(z*-z) Further Analyses iTOUGH2: Inverse Modeling • Provides inversemodeling capabilities for TOUGH2 • What iTOUGH2 Does • Runs TOUGH2 with different parameter sets • Evaluates selected TOUGH2 output • Application Modes • Sensitivity analysis • Parameter estimation by automatic model calibration • Uncertainty propagation analysis

  13. iTOUGH2 Features • Objective Functions • Weighted Least Squares • L1-Estimator • Robust Estimators • Minimization Algorithms • Gauss-Newton • Levenberg-Marquardt • Downhill Simplex • Global search algorithms • Grid search • Simulated annealing • Differential evolutionary algorithm • Harmony search • Error Propagation • First order • Monte Carlo (Latin Hypercube)

  14. iTOUGH2 Features • Parameters to be estimated • All TOUGH2 input parameters (hydrologic, thermal, geochemical) • Initial and boundary conditions • Geometrical parameters • Soil structure parameters • Observations (calibration data) • All data with corresponding TOUGH2 output variable • Geophysical data • http://www-esd.lbl.gov/iTOUGH2

  15. 1980 1990 2000 MULKOM TOUGH TOUGH2-PC TOUGH2-V2 TOUGH2 STMFLD STMVOC M2NOTS TMVOC TMVOCBio T2VOC iTOUGH iTOUGH2-PVM iTOUGH2 EWASG T2DM T2DMR T2TNT T2R3D T2LBM T2CA T2CG1 T2CG2 EOS1 EOS2 EOS3 EOS4 EOS5 EOS6 EOS7 EOS8 EOS9 EOS1sc EOS3ecm EOS7R EOS9ecm ECH4 EOS11 EOS3nn EOSN EOS7C EOS9nT EOS16 ECO2N TOUGH-FLAC TOUGHREACT TOUGH+ 2006 TOUGH-MP V-TOUGH A-TOUGH TOUGH-AMD 1980 1990 2000 2006 TOUGH Workshops Countries attending 7 10 12 14 18

  16. TOUGH Developers Alfredo Battistelli Grimur Björnsson Ron Falta René Lefebvre Auli Niemi Mike O’Sullivan Torben Sonnenborg Steve Webb Steve White … Karsten Pruess Matthew Reagan Jonny Rutqvist Chao Shan Eric Sonnenthal Nic Spycher Yu-Shu Wu Tianfu Xu Guoxiang Zhang Keni Zhang … Rick Ahlers Jens Birkholzer Bo Bodvarsson Chris Doughty Stefan Finsterle Mike Kowalsky George Moridis Curt Oldenburg Lehua Pan

  17. TOUGH family of codes installed in ~300 organizations in ~30 countries: Academia Government Organizations Industry Applications in: Geothermal Reservoir Engineering Nuclear Waste Isolation Environmental Remediation CO2 Sequestration Gas Hydrates Vadose Zone Hydrology … Code development driven by research needs Strong support from active user community Distributed by LBNL (http://esd.lbl.gov/TOUGH+/licensing.html) TOUGH Applications and Impact • TOUGH Workshops (1990, 1995, 1998) • TOUGH Symposia (2003, 2006, 2009) • TOUGH Short Courses • Special Issues (recent): Geothermics, Vol. 33(4), 2004 Energy Technology and Management, Vol. 28, 2007 Vadose Zone Journal, Vol. 3(3), 2004; Vol. 7(1), 2008 Nuclear Technology, Vol. 164(2), 2008 http://www-esd.lbl.gov/TOUGH2 http://www-esd.lbl.gov/iTOUGH2 http://www-esd.lbl.gov/TOUGH+ http://www-esd.lbl.gov/TOUGHREACT

  18. http://www-esd.lbl.gov/TOUGH+

  19. Reasons to Use TOUGH for Nuclear Waste Problems • TOUGH2 can simulate saturated, unsaturated, and multiphase flow, heat flow, and radionuclide transport in fractured porous media. • Ventilation effects as well as gas- and heat-generation lead to nonisothermal two-phase conditions also in repositories located in the saturated zone. • Deep repositories often encounter saline solutions (brines), leading to density-driven flows. • Ability to simulate fracture flow and fracture-matrix interaction • Coupled process simulations: • TH: TOUGH2 • THC: TOUGHREACT • THM: TOUGH-FLAC • Inverse modeling capabilities (iTOUGH2)

  20. Example 1: Seepage into Tunnel Inversion of Seepage Data to Estimate Drift-Scale Model-Related Seepage-Specific Unsaturated Flow Parameters

  21. Step 1: Create an Underground Opening

  22. Step 2: Perform Liquid Release Test

  23. Step 3: Develop Numerical Model

  24. Step 4: Calibrate Model • Main Purpose • Estimate seepage-relevant parameters • Modeling Approach • 3D heterogeneous fracture-continuum model • Based on air-k data • Calibrated against seepage data from liquid-release tests • Results • Model accurately reproduces and predicts seepage data • Estimated seepage-relevant parameters • Provides conceptual model and parameters for probabilistic seepage model

  25. Step 5: Refine and Repeat

  26. Step 6: Examine Alternative Conceptual Models

  27. Seepage Modeling: Concluding Remarks • Data provide: • Heterogeneity • Evaporation potential • Boundary conditions • Calibration data • Model captures: • Drift geometry • Evaporation effects • Transient effects • Heterogeneity • Unsaturated flow • Capillary barrier effect • Model predicts: • Storage • Flow diversion • Evaporation • Seepage • Effective, model-related parameters capture seepage-relevant mechanisms, including: • Capillarity • Roughness effect • Film flow • Discretization effect

  28. Example 2: Radon Inversion of Radon Concentration and Gas Pressure Data to Estimate Large-Scale Fracture Network Properties

  29. Calibration and Prediction of Radon Concentrations in Tunnel • Problem • Inconsistency between small-scale laboratory core properties and large-scale effective parameters • Upscaling of fracture network flow is very difficult • Radon in tunnel is health risk • Objectives • Estimate formation properties • Examine probability of exceeding radon exposure limit along tunnel

  30. Step 1: Review Available Data • Approach • Find data on appropriate scale • Use inverse modeling to estimate effective parameters • Predict radon concentrations under changed ventilation conditions • Mechanism • Radon accumulates along tunnel… • … as a function of: • Ventilation regime • Barometric pressure • Formation properties ( inverse modeling)

  31. Step 2: Develop and Calibrate Model • Modeling Approach • Develop air flow and radon transport model (includes tunnel and formation) • Perform joint inversion of radon concentration and gas pressure data • Results • Accurately reproduces and predicts concentration and pressure data • Provides estimates of large-scale fracture network permeability and porosity

  32. Step 3: Examine Radon Exposure Risk • Prediction • Predict radon concentrations for different ventilation scenarios • Examine probability of exceeding radon exposure limit • Concluding Remarks • Radon data suitable for estimating large-scale properties • Numerical modeling useful for: • Design of ventilation system • Addressing health and safety issue Journal of Contam. Hydrol., 70, 152–171, 2004.

  33. Example 3: Joint Hydrogeophysical Inversion • Simulate heater experiment • Simulate Ground Penetrating Radar (GPR) signals • Perform joint hydrogeophysical inversion • Validate against ERT data Kowalsky et al., Nuclear Technology, 164(2), 169–179, 2008.

  34. Joint Thermal-Hydrological-Geophysical Inversion hydrological model calibrated using geophysical data independent data

  35. Concluding Remarks • Numerical modeling is a key element in support of site characterization and performance assessment • Process understanding • Hypothesis testing • Experimental design • Data analysis • Performance assessment • TOUGH suite of simulators used internationally in nuclear waste disposal programs • Accurate simulation of coupled processes • Flexibility (geometry, heterogeneity, boundary conditions) • Inverse modeling capabilities • Large developer and user base • Continuous development of simulation capabilities in response to • New scientific insights • Scientific challenges • Application needs

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