Profile Finsterle GeoConsulting

Finsterle GeoConsulting is a sole proprietorship company registered in Contra Costa County, California (License No. 029655).

Finsterle GeoConsulting provides advanced analyses and numerical simulation services, technical reviews, scientific evaluations, and training. These services are offered worldwide. Finsterle GeoConsulting collaborates with subject matter experts and modelers to fully address the specific demands of a given project.

Prior to launching Finsterle GeoConsulting in October 2016, Stefan Finsterle has been a scientist at the Lawrence Berkeley National Laboratory, where he conducted applied research in the areas of nuclear waste isolation, geothermal reservoir engineering, fracture hydrology, environmental remediation, and oil and gas production. He specializes in inverse modeling, multiphase flow simulation, sensitivity analysis, uncertainty quantification, and geostatistics.

Stefan is the main developer of the iTOUGH2 simulation-optimization framework and a co-developer of the TOUGH suite of nonisothermal multiphase flow and transport codes.


Journal Articles, Stefan Finsterle

  1. Kim, J.-S., G.-Y. Kim, M.H. Bail, S. Finsterle, and G.C. Cho, A new approach for quantitative damage assessment of in-situ rock mass by acoustic emission, Geomechanics and Engineering, 18(1), 11–21, doi: 10.12989/gae.2019.18.1.011, 2019.

  2. Muller, R.A., S. Finsterle, J. Grimsich, R. Baltzer, E.A. Muller, J.W. Rector, J. Payer, and J. Apps, Disposal of high-level nuclear waste in deep horizontal drillholes, Energies, 12, 2052, doi: 10.3390/en12112052,, 2019.

  3. Payer, J., S. Finsterle, J.A. Apps, and R.A. Muller, Corrosion performance of engineered barrier system in deep horizontal drillholes, Energies, 12(8), 1491, doi: 1020193390/en12081491,, 2019.

  4. Finsterle, S., R.A. Muller, R. Baltzer, J. Payer, and J.W. Rector, Thermal evolution near heat-generating nuclear waste canisters disposed in horizontal drillholes, Energies, 12(4), 596, doi: 10.3390/en12040596,, 2019.

  5. Finsterle, S., B. Lanyon, M. Åkesson, S. Baxter, M. Bergström, N. Bockgård, W. Dershowitz, B. Dessirier, A. Frampton, Å. Fransson, A. Gens, B. Gylling, I. Hančilová, D. Holton, J. Jarsjö, J.-S. Kim, K.-P. Kröhn, D. Malmberg, V.-M. Pulkkanen, A. Sawada, A. Sjöland, U. Svensson, P. Vidstrand, H. Viswanathan, Conceptual uncertainties in modelling the interaction between engineered and natural barriers of nuclear waste repositories in crystalline rock, In: Norris, S., E.A.C. Neeft, and M. Van Geet (eds.): Multiple Roles of Clays in Radioactive Waste Confinement, Geological Society, London, Special Publications, 482, doi: 10.1144/SP482.12, 2018.

  6. Michael, K., A. Avijegon, L. Ricard, T. Dance, C.D. Piane, B. Freifeld, M. Woitt, L. Stalker, J. Myers, M. Peruvkhina, L. Langhi, A. Hortle, D. Geeves, and S. Finsterle, Multi-level CO2 injection testing and monitoring at the South West Hub In-Situ Laboratory, Applied Energy Symposium and Forum, Carbon Capture, Utilization and Storage, CCUS 2018, 27–29 June 2018, Perth, Australia, Energy Procedia, 154, 151–156, doi: 10.1016/j.egypro.2018.11.025, 2018.

  7. Lee, K.J., S. Finsterle, and G.J. Moridis, Analyzing the impact of reaction models on the production of hydrocarbons from thermally upgraded oil shales, J. Petrol. Science and Engineering, 168, 448–464, doi: 10.1016/j.petrol.2018.05.021, 2018.

  8. Zhang, Y., Y. Jung, B. Freifeld, and S. Finsterle, Using distributed temperature sensing to detect CO2 leakage along the injection well casing, International Journal of Greenhouse Gas Control, 74, 9–18, doi:10.1016/j.ijggc.2018.04.011, 2018.

  9. Lee, K.J., S. Finsterle, and G.J. Moridis, Estimating the reaction parameters of oil shale pyrolysis and oil shale grade using temperature transient analysis and inverse modeling, Journal of Petroleum Science and Engineering,  165, 765–776, doi:10.1016/j.petrol.2018.03.020, 2018.

  10. Morzfeld, M., M.S. Day, R.W. Grout, G.H.S. Pau, S.A. Finsterle, and J.B. Bell, Iterative importance sampling algorithms for parameter estimation, SIAM J. Sci. Comput., 40(2), B329–B352, doi:10.1137/16M1088417, 2018.

  11. Shi, X., S. Finsterle, K. Zhang, and D. Lu, Editorial: Advances in multiphase flow and transport in the subsurface environment, Geofluids, 2906325, 1–2, doi:10.1155/2018/2906326, 2018.

  12. Hannon, M.J., and S. Finsterle, The effect of anisotropy on multi-dimensional pressure-pulse-decay experiments, Transp. Porous Med.123, 545–562, doi:10.1007/s11242-017-0941-x, 2018.

  13. Tokunaga, T.K., W. Shen, J. Wan, Y. Kim, A. Cihan, Y. Zhang, and S. Finsterle, Water saturation relations and their diffusion-limited equilibration in gas shale: Implications for gas flow in unconventional reservoirs, Water Resour. Res., 53(11), 9757–9770, doi:10.1002/2017WR021153, 2017.

  14. Tokunaga, T.K., S. Finsterle, Y. Kim, J. Wan, A. Lanzirotti, and M. Newville, Ion diffusion within water films in unsaturated porous media, Environmental Science & Technology, 51(8), 4338–4346, doi:10.1021/acs.est.6b05891, 2017.

  15. Finsterle, S., M. Commer, J. Edmiston, Y. Jung, M.B. Kowalsky, G.S.H. Pau, H. Wainwright, and Y. Zhang, iTOUGH2: A simulation-optimization framework for analyzing multiphysics subsurface systems, Computers and Geosciences, 108, 8–20doi:10.1016/j.cageo.2016.09.005, 2017.

  16. Rinaldi, A.P., J. Rutqvist, S. Finsterle, and H.-H. Liu, Inverse modeling of ground surface uplift and pressure with iTOUGH-PEST and TOUGH-FLAC: The case of CO2 Injection at In Salah, Algeria, Computers and Geosciences, 108, 98–109, doi:10.1016/j.cageo.2016.10.009, 2017.

  17. Liu, Y., G.S.H. Pau, and S. Finsterle, Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data, Computers and Geosciences108, 21–32, doi:10.1016/j.cageo.2017.04.001, 2017.

  18. Jung, Y., G.S.H. Pau, S. Finsterle, and R. Pollyea, TOUGH3: A new efficient version of the TOUGH suite of multiphase flow and transport simulators, Computers and Geosciences108, 2–7, doi:10.1016/j.cageo.2016.09.009, 2017.

  19. Borgia, A., C.M. Oldenburg, R. Zhang, L. Pan, T.M. Daley, S. Finsterle, and R.S. Ramakrishnan, Simulation of CO2 injection into fractures and faults for improving their geophysical characterization at EGS sites, Geothermics, 69, 189–201, doi:10.1016/j.geothermics.2017.05.002, 2017.

  20. Blanco-Martin, L., J. Rutqvist, C. Doughty, Y. Zhang, S. Finsterle, and C.M. Oldenburg, Coupled geomechanics and flow modeling to investigate thermally induced compaction of diatomite in heavy oil reservoirs produced using cyclic steaming, Journal of Petroleum Science and Engineering, doi:10.1016/j.petrol.2016.09.002, 2016.

  21. Zhang, Y., Y. Liu, G.S.H. Pau, S. Oladyshkin, and S. Finsterle, Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses, International Journal of Greenhouse Gas Control, 49, 217–226, doi:10.1016/j.ijggc.2016.03.003, 2016.

  22. Pau, G.S.H., S. Finsterle, and Y. Zhang, Fast high-resolution prediction of multi-phase flow in fractured formations, Adv. Water Resour., 88, 80–85, doi:10.1016/j.advwatres.2015.12.008, 2016.

  23. Finsterle, S., Practical notes on local data-worth analysis, Water Resour. Res., 51(12), 9904–9924, doi:10.1002/2015WR017445, 2015.

  24. Yuan, Y., Y. Khare, S. Wang, P. Parajuli, I. Kisekka, and S. Finsterle, Hydrologic and water quality models: Sensitivity, Transactions of the ASABE, 58(6), 1721–1744, doi:10.13031/trans.58.10611, 2015.

  25. Magnusdottir, L., and S. Finsterle, An iTOUGH2 equation-of-state module for modeling supercritical conditions in geothermal reservoirs, Geothermics, 57, 8–17, doi:10.1016/j.geothermics.2015.05.003, 2015.

  26. Takeda, M., T. Hiratsuka, M. Manaka, S. Finsterle, and K. Ito, Experimental examination of the relationships among chemico-osmotic, hydraulic and diffusion parameters of Wakkanai mudstones, Journal of Geophysical Research: Solid Earth, doi:10.1002/2013JB010421, 2014.

  27. Freedman, V.L., X. Chen, S. Finsterle, M.D. Freshley, I. Gorton, L.J. Gosink, E.H. Keating, C.S. Lansing, W.A.M. Moerglein, C.J. Murray, G.S.H. Pau, E. Porter, S. Purohit, M. Rockhold, K.L. Schuchardt, C. Sivaramakrishnan, V.V. Vesselinov, and S. R. Waichler, A high-performance workflow system for subsurface simulation, Environmental Modelling & Software, doi:10.1016/j.envsoft.2014.01.030, 55, 176–189, 2014.

  28. Poskas, P., A. Narkuniene, D. Grigaliuniene, and S. Finsterle, Comparison of radionuclide releases from a conceptual geological repository for RBMK-1500 and BWR spent nuclear fuel, Nuclear Technology, 185(3), 322–335, doi:10.1382/NT13-52, 2014.

  29. Finsterle, S., and E.L. Sonnenthal, Foreword to the Special Issue on the TOUGH Symposium 2012, Computers & Geosciences, 65, 1, doi:10.1016/j.cageo.2014.01.003, 2014.

  30. Finsterle, S., E.L. Sonnenthal, and N. Spycher, Advances in subsurface modeling: The TOUGH suite of simulators, Computers & Geosciences, doi:10.1016/j.cageo.2013.06.009, 65, 2–12, 2014.

  31. Wellmann, J.F., S. Finsterle, and A. Croucher, Integrating structural geological data into the inverse modelling framework of iTOUGH2, Computers & Geosciences, 65, 95–109, doi:10.1016/j.cageo.2013.10.014, 2014.

  32. Wainwright, H., S. Finsterle, Y. Jung, Q. Zhou, and J.T. Birkholzer, Making sense of global sensitivity analyses, Computers & Geosciences, 65, 84–94, doi:10.1016/j.cageo.2013.06.006, 2014.

  33. Pau, G.S.H., Y. Zhang, S. Finsterle, H. Wainwright, and J. Birkholzer, Reduced order modeling in iTOUGH2, Computers & Geosciences, 65, 118–126, doi:10.1016/j.cageo.2013.08.008, 2014.

  34. Commer, M., M.B. Kowalsky, J. Doetsch, G. Newman, and S. Finsterle, MPiTOUGH2: A parallel parameter estimation framework for hydrological and hydrogeophysical applications, Computers & Geosciences, 65, 127–135, doi:10.1016/j.cageo.2013.06.011, 2014.

  35. Rechard, R.P., H.H. Liu, Y.W. Tsang, and S. Finsterle, Characterization of natural barrier of Yucca Mountain disposal system for spent nuclear fuel and high-level radioactive waste, Reliability Engineering & System Safety, 122, 32–52, doi:10.1016/j.ress.2013.06.020, 2014.

  36. Akhavan, M., P.T. Imhoff, S. Andres, and S. Finsterle, Model evaluation of impacts on denitrification under rapid infiltration basin systems, J. Contam. Hydrol., 152, 18–34, doi:10.1016/j.jconhyd.2013.05.007, 2013.

  37. Wainwright, H.M., S. Finsterle, Q. Zhou, and J.T. Birkholzer, Modeling the performance of large-scale CO2 storage systems: A comparison of different sensitivity analysis methods, International Journal of Greenhouse Gas Control, 17,189–205, doi:10.1016/j.ijggc.2013.05.007, 2013.

  38. Doetsch, J., M.B. Kowalsky, C. Doughty, S. Finsterle, J.B. Ajo-Franklin, C.R. Carrigan, X. Yang, S.D. Hovorka, and T.M. Daley, Constraining CO2 simulations by coupled modeling and inversion of electrical resistance and gas composition data, International Journal of Greenhouse Gas Control, 18, 510–522, doi:10.1016/j.ijggc.2013.04.011, 2013.

  39. Pau, G., Y. Zhang, and S. Finsterle, Reduced order models for many-query subsurface flow applications, Computational Geosciences, 17(4), 705–721, doi:10.1007/s10596-013-9349-z, 2013.

  40. Finsterle, S., Y. Zhang, L. Pan, P. Dobson, and K. Oglesby, Microhole arrays for improved heat mining from enhanced geothermal systems, Geothermics, 47, 104–115, doi:10.1016/j.geothermics.2013.03.001, 2013.

  41. Shi, X., M. Ye, S. Finsterle, and J. Wu, Comparing nonlinear regression and Markov Chain Monte Carlo methods for assessment of prediction uncertainty in vadose zone modeling, Vadose Zone J., 11(4), doi:10.2136/vzj2011.0147, 2012.

  42. Finsterle, S., M.B. Kowalsky, and K. Pruess, TOUGH: Model use, calibration and validation, Transactions of the ASABE, 55(4), 1275–1290, doi:10.13031/2013.42240, 2012.

  43. Akhavan, M., P.T. Imhoff, S. Finsterle, and A.S. Andres, Application of a coupled overland flow-vadose zone model to rapid infiltration basin systems, Vadose Zone J., 11(2), doi:10.2136/vzj2011.0140, 2012.

  44. Kowalsky, M.B., S. Finsterle, K.H. Williams, C. Murray, M. Commer, D. Newcomer, A. Englert, C.I. Steefel, and S.S. Hubbard, On parameterization of the inverse problem for estimating aquifer properties using tracer data, Water Resour. Res., 48, W06535, doi:10.1029/2011WR011203, 2012.

  45. Zhang, Y., L. Pan, K. Pruess, and S. Finsterle, A time-convolution approach for modeling heat exchange between a wellbore and surrounding formation, Geothermics, 40(4), 251–266, doi:10.1016/j.geothermics.2011.08.003, 2011.

  46. Oldenburg, C.M., B.M. Freifeld, K. Pruess, L. Pan, S. Finsterle, and G.J. Moridis, Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas, Proceedings of the National Academy of Sciences, 109(50), 20254–20259, doi:10.1073/pnas.1105165108, 2011.

  47. Williamson, M., J. Meza, D. Moulton, I. Gorton, M. Freshley, P. Dixon, R. Seitz, C. Steefel, S. Finsterle, S. Hubbard, M. Zhu, K. Gerdes, R. Patterson, and Y.T. Collazo, Advanced Simulation Capability for Environmental Management (ASCEM): An overview of initial results, Technology and Innovation, 13, 175–199, doi:10.3727/194982411X13085939956625, 2011.

  48. Finsterle, S., and M.B. Kowalsky, A truncated Levenberg-Marquardt algorithm for the calibration of highly parameterized nonlinear models, Computers and Geosciences, 37, 731–738, doi:10.1016/j.cageo.2010.11.005, 2011.

  49. Finsterle, S., and Y. Zhang, Error handling strategies in multiphase inverse modeling, Computers and Geosciences, 37, 724–730, doi:10.1016/j.cageo.2010.11.009, 2011.

  50. Jung, Y., P. Imhoff, and S. Finsterle, Estimation of landfill gas generation rate and gas permeability field of refuse using inverse modeling, Transport in Porous Media, 90(1), 41–58, doi:10.1007/s11242-010-9659-8, 2011.

  51. Xu, T., R. Senger, and S. Finsterle, Bentonite alteration due to thermal-hydro-chemcial processes during the early thermal period in a nuclear waste repository, Nuclear Technology, 174(3), 438–451, 2011.

  52. Zhang, Y., S. Hubbard, and S. Finsterle, Factors governing sustainable groundwater pumping near a river, Ground Water, 49(3), 432–444, doi:10.1111/j.1745-6584.2010.00743.x, 2011.

  53. Birkholzer, J.T., Q. Zhou, A. Cortis, and S. Finsterle, A sensitivity study on regional pressure buildup from large-scale CO2 storage projects, Energy Procedia, 4, 4371–4378, 2011.

  54. Finsterle, S., and Y. Zhang, Solving iTOUGH2 simulation and optimization problems using the PEST protocol, Environmental Modelling and Software, 26, 959–968, doi:10.1016/j.envsoft.2011.02.008, 2011.

  55. Takeda, M., T. Hiratsuka, K. Ito, and S. Finsterle, An asymmetric diffusion experiment for the determination of diffusion and sorption coefficients of rock samples, Journal of Contaminant Hydrology, 123, 114–129, doi:10.1016/j.jconhyd.2010.12.012, 2011.

  56. Kowalsky, M.B., E. Gasperikova, S. Finsterle, D. Watson, and S.S. Hubbard, Coupled modeling of hydrogeochemical and electrical resistivity data for exploring the impact of recharge on subsurface contamination, Water Resour. Res., 47, W02509, doi:10.1029/2009WR008947, 2011.

  57. Zhang, Y., B. Freifeld, S. Finsterle, M. Leahy, J. Ennis-King, L. Paterson, and T. Dance, Single-well experimental design for studying residual trapping of supercritical carbon dioxide, International Journal of Greenhouse Gas Control, 5, 88–98, doi:10.1016/j.ijggc.2010.06.011, 2011.

  58. Neerdael, B., and S. Finsterle, The use of numerical models in support of site characterization and performance assessment studies for geological repositories, Nuclear Engineering and Technology, 42(2), 145–150, 2010.

  59. Lehikoinen, A., J.M.J. Huttunen, S. Finsterle, M.B. Kowalsky, and J.P. Kaipio, Dynamic inversion for hydrological process monitoring with electrical resistance tomography under model uncertainties, Water Resour. Res., 46, W04513, doi:10.1029/2009WR008470, 2010.

  60. Zhang, Y., C.M. Oldenburg, and S. Finsterle, Percolation-theory and fuzzy rule-based probability estimation of fault leakage at geologic carbon sequestration sites, Env. Earth Sci., 59, 1447–1459, doi:10.1007/s12665-009-0131-4, 2010.

  61. Lehikoinen, A., S. Finsterle, A. Voutilainen, M.B. Kowalsky, and J.P. Kaipio, Dynamical inversion of geophysical ERT data: state estimation in the vadose zone, Inverse Problems in Science and Engineering, 17(6), 715–736, doi:10.1080/17415970802475951, 2009.

  62. Mukhopadhyay, S., Y.W, Tsang, and S. Finsterle, Parameter estimation from flowing fluid temperature logging data in unsaturated fractured rock using multiphase inverse modeling, Water Resour. Res., 45, W04414, doi:10.1029/2008WR006869, 2009.

  63. Senger, R., T. Xu, P. Marschall, and S. Finsterle, Investigation of two-phase flow phenomena associated with corrosion in an SF/HLW repository in Opalinus clay, Switzerland, Physics and Chemistry of the Earth, 33, S317–S326, doi:10.1016/j.pce.2008.10.034, 2008.

  64. Xu, T., S. Senger, and S. Finsterle, Corrosion-induced gas generation in a nuclear waste repository: Reactive geochemistry and multiphase flow effect, Appl. Geochem., 23, 3423–3433, doi:10.1016/j.apgeochem.2008.07.012, 2008.

  65. Kowalsky, M.B., J. Birkholzer, J. Peterson, S. Finsterle, S. Mukhopadhyay, and Y. Tsang, Sensitivity analysis for joint inversion of ground-penetrating radar and thermal-hydrological data from a large-scale underground heater test, Nuclear Technology, 164(2), 169–179, 2008.

  66. Freifeld, B.M., S. Finsterle, T.C. Onstott, T. Toole, and L.M. Pratt, Ground surface temperature reconstructions: Using in situ estimates for thermal conductivity acquired with a fiber-optic distributed thermal perturbation sensor, Geophysical Research Letter, 35, L14309, doi:10.1029/2008GL034762, 2008.

  67. Kiryukhin, A.V., N.P. Asaulova, and S. Finsterle, Inverse modeling and forecasting for the exploitation of the Pauzhetsky geothermal field, Kamchatka, Russia, Geothermics, 37, 540–562, doi:10.1016/j.geothermics.2008.04.003, 2008.

  68. Finsterle, S., C. Doughty, M.B. Kowalsky, G.J. Moridis, L. Pan, T. Xu, Y. Zhang, and K. Pruess, Advanced vadose zone simulations using TOUGH, Vadose Zone J., 7:601–609, doi:10.2136/vzj2007.0059, 2008.

  69. Salve, R., N.Y. Krakauer, M.B. Kowalsky, and S. Finsterle, A qualitative assessment of microclimatic perturbations in a tunnel, Int. J. Climatol., 28(15), 2081­U3, doi:10.1002/joc.1697, 2008.

  70. Finsterle, S., and M.B. Kowalsky, Joint hydrological-geophysical inversion for soil structure identification, Vadose Zone J., 7:287–293, doi:10.2136/vzj2006.0078, 2008.

  71. Revil, A.,  N. Linde, A. Cerepi, D. Jougnot, S. Matthäi, and S. Finsterle, Electrokinetic coupling in unsaturated porous media, J. Colloid Interface Sci., 313, 315–327, doi:10.1016/j.jcis.2007.03.037, 2007.

  72. Zhang, Y. C. M. Oldenburg, S. Finsterle and G. S. Bodvarsson, System-level modeling for economic evaluation of geological CO2 storage in gas reservoirs, Energy Conservation and Management, 48(6), 1827–1833, doi:10.1016/j.enconman.2007.01.018, 2007.

  73. Lehikoinen, A., S. Finsterle, A. Voutilainen, L. M. Heikkinen, M. Vauhkonen, and J. P. Kaipio, Approximation errors and truncation of computational domains with application to geophysical tomography, Inverse Problems and Imaging, 1(2), 371–389, 2007.

  74. Finsterle, S., Comment on “Seepage into drifts and tunnels in unsaturated fractured rock” by Dani Or, Markus Tuller, and Randall Fedors, Water Resour. Res., 42, W07603, doi:10.1029/2005WR004777, 2006.

  75. Linde, N., S. Finsterle, and S. Hubbard, Inversion of tracer test data using tomographic constraints, Water Resour. Res., 42(4), W04410, doi:10.1029/2004WR003806, 2006.

  76. Zhang, Y., H. H. Liu, Q. Zhou, and S. Finsterle, Effects of diffusive property heterogeneity on effective matrix diffusion coefficient for fractured rock, Water Resour. Res., 42, W04405, doi:10.1029/2005WR004513, 2006.

  77. Finsterle, S., Demonstration of optimization techniques for groundwater plume remediation using iTOUGH2, Environmental Modelling and Software, 21(5), 665–680, doi:10.1016/j.envsoft.2004.11.012, 2005.

  78. Kowalsky, M.B., S. Finsterle, J. Peterson, S. Hubbard, Y. Rubin, E. Majer, A. Ward, and G. Gee, Estimation of field-scale soil hydraulic parameters and dielectric parameters through joint inversion of GPR and hydrological data, Water Resour. Res., 41, W11425, doi:10.1029/2005WR004237, 2005.

  79. Finsterle, S., and C.M. Oldenburg, Research advances in vadose zone hydrology through simulations with the TOUGH codes, Preface to special section of Vadose Zone J., 3, 737, 2004.

  80. Finsterle, S., Multiphase inverse modeling: Review and iTOUGH2 applications, Vadose Zone J., 3, 747–762, doi:10.2113/3.3.747, 2004.

  81. Ghezzehei, T. A., R. C. Trautz, S. Finsterle, P. J. Cook, and C. F. Ahlers, Modeling coupled evaporation and seepage in ventilated tunnels, Vadose Zone J., 3, 806–818, doi:10.2136/vzj2004.0806, 2004.

  82. Gallagher, P. M., and S. Finsterle, Physical and numerical model of colloidal silica injection for passive site stabilization, Vadose Zone J., 3, 917–925, doi:10.1016/j.sandf.2014.12.011, 2004.

  83. Kitterød, N.-O., and S. Finsterle, Simulating unsaturated flow fields based on saturation measurements, Journal of Hydraulic Research, 42, 121–129, 2004.

  84. Kowalsky, M.B., S. Finsterle, and Y. Rubin, Estimating flow parameter distributions using ground-penetrating radar and hydrological measurements during transient flow in the vadose zone, Adv. Water Resour., 27(6), 583–599, doi:10.1016/j.advwatres.2004.03.003, 2004.

  85. Unger, A., S. Finsterle, and G. S. Bodvarsson, Transport of radon gas into a tunnel at Yucca Mountain—estimating large-scale fractured tuff hydraulic properties and implications for the ventilation system, Journal of Contam. Hydrol., 70, 152–171, doi:10.1016/j.jconhyd.2003.07.001, 2004.

  86. Vasco, D.W., S. Finsterle, Numerical trajectory calculations for the efficient inversion of flow and transport observations, Water Resour. Res., 40, W01507, doi:10.1029/2003WR002362, 2004.

  87. Engelhardt, I., S. Finsterle, and C. Hofstee, Experimental and numerical investigation of flow phenomena in nonisothermal, variably saturated bentonite/crushed rock mixtures, Vadose Zone J., 2, 239–246, doi:10.2113/2.2.239, 2003.

  88. Engelhardt, I., and S. Finsterle, Thermal-hydrologic experiments with bentonite/crushed rock mixtures and estimation of effective parameters by inverse modeling, Applied Clay Science, 23, 111–120, doi:10.1029/2006WR005283, 2003.

  89. Houseworth, J. E., S. Finsterle, and G. S. Bodvarsson, Flow and transport in the drift shadow in a dual-continuum model, J. of Contam. Hydrol., 62–63, 133–156, doi:10.1016/S0169-7722(02)00172-9, 2003.

  90. Finsterle, S., C. F. Ahlers, R. C. Trautz, and P. J. Cook, Inverse and predictive modeling of seepage into underground openings, J. Contam. Hydrol., 62–63, 89–109, doi:10.1016/S0169-7722(02)00174-2, 2003.

  91. Mays, D. C., B. Faybishenko, and S. Finsterle, Information entropy to measure temporal and spatial complexity of unsaturated flow in heterogeneous media, Water Resour. Res., 38(12), 1313, doi:10.1029/2001WR001185, 2002.

  92. Liu, H. H., G. S. Bodvarsson, and S. Finsterle, A note on unsaturated flow in two-dimensional fracture networks, Water Resour. Res., 38(9), 1176, doi:10.1027/2001WR000977, 2002.

  93. Finsterle, S., J. T. Fabryka-Martin, and J. S. Y. Wang, Migration of a water pulse through fractured porous media, J. Contam. Hydr., 54 (1–2), 37–57, doi:10.1016/S0169-7722(01)00163-2 , 2002.

  94. Finsterle, S., and R. C. Trautz, Numerical modeling of seepage into underground openings, Mining Engineering, 53(9), 52–56, 2001.

  95. Finsterle, S., Using the continuum approach to model unsaturated flow in fractured rock, Water Resour. Res., 36(8), 2055–2066, doi:10.1029/2000WR900122, 2000.

  96. Moridis, G. J., S. Finsterle, and J. Heiser, Evaluation of alternative designs for an injectable barrier at the Brookhaven National Laboratory Site, Long Island, New York, Water Resour. Res., 35(10), 2937–2953, doi:10.1029/1999WR900184, 1999.

  97. Ahlers, C. F., S. Finsterle, and G. S. Bodvarsson, Characterization of subsurface pneumatic response at Yucca Mountain, J. Contam. Hydr., 38(1–3), 47–68, doi:10.1016/S0169-7722(99)00011-X, 1999.

  98. Wang, J. S. Y., R. C. Trautz, P. J. Cook, S. Finsterle, A. L. James, and J. Birkholzer, Field tests and model analyses of seepage into drift, J. Contam. Hydr., 38(1–3), 323–347, doi:10.1016/S0169-7722(99)00019-4, 1999.

  99. Finsterle, S., and B. Faybishenko, Inverse modeling of a radial multistep outflow experiment for determining unsaturated hydraulic properties, Adv. Water Resour., 22(5), 431–444, doi:10.1016/S0309-1708(98)00030-X, 1999.

  100. Finsterle, S., and J. Najita, Robust estimation of hydrogeologic model parameters, Water Resour. Res., 34(11), 2939–2947, doi:10.1029/98WR02174, 1998.

  101. Finsterle, S., and P. Persoff, Determining permeability of tight rock samples using inverse modeling, Water Resour. Res., 33 (8), 1803–1811, doi:10.1029/97WR01200, 1997.

  102. Pruess, K., S. Finsterle, G. Moridis, C. Oldenburg and Y.-S. Wu, General-purpose reservoir simulators: the TOUGH2 family, GRC Bulletin, 53–57, 1997.

  103. Finsterle, S., and K. Pruess, Solving the estimation-identification problem in two-phase flow modeling, Water Resour. Res., 31 (4), 913–924, doi:10.1029/94WR03038, 1995.

Book Chapters, Stefan Finsterle

  1. Dwivedi, D., B. Dafflon, B. Arora, H.M. Wainwright, and S. Finsterle, Spatial Analysis and Geostatistical Techniques, in: Singh et al., (eds.), Handbook of Applied Hydrology, McGraw-Hill, 2016.

  2. Saibi, H., S. Finsterle, R. Bertani, and J. Nishijima, Geothermal Energy, in: K. Lee, and J. Kauffman (eds.), Handbook of Sustainable Engineering, Springer, New York, 1019–1042, doi:10.1007/978-1-4020-8939-8_120, 2013.

  3. Bodvarsson, G. S., S. Finsterle, H. H. Liu, C. M. Oldenburg, K. Pruess, E. Sonnenthal, and Y.-S. Wu, Flow and transport modeling of subsurface systems, in: B. B. Looney and R. W. Falta (eds.), Vadose Zone Science and Technology Solutions, Battelle Press, Columbus, Ohio, 2000.

  4. Finsterle, S., K. Pruess, G. Björnsson, and A. Battistelli, Evaluation of geothermal well behavior using inverse modeling, in: Faybishenko (ed.) Dynamics of Fluids in Fractured Rocks, Geophysical Monograph 122, pp. 377–387, American Geophysical Union, Washington DC, 2000.

  5. Faybishenko, B., and S. Finsterle, Tensiometry in fractured rocks, in: Zhang, D., and Winter, C. L., (Eds.), Theory, Modeling, and Field Investigation in Hydrogeology: A Special Volume in Honor of Shlomo P. Neuman’s 60th Birthday, Boulder, Colorado, Geological Society of America Special Paper 348, p. 161–174, 2000.

  6. Finsterle, S., Direct and inverse modeling of multiphase flow systems, In: R. Helmig et al. (eds.), Modeling and Computation in Environmental Sciences, Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden, Germany, 146–157, 1997.

Invited Talks and Seminars, Stefan Finsterle

  1. What’s new in iTOUGH2? Department of Engineering Science, University of Auckland, Auckland, New Zealand, June 18, 2019.

  2. Validating validation: Building confidence in hydrogeological models, 37th SKB Task Force GWFTS Meeting, Solna, Sweden, March 19–21, 2019.

  3. Boundaries, Interfaces, and Discontinuities: Where things happen, MODFLOW & More 2017, Modeling for Sustainability and Adaptation, Colorado School of Mines, Golden, Colorado, May 21–24, 2017.

  4. Multi-physics joint inversion: Simply too complex?, University of Texas Austin, September 16, 2016.

  5. Multi-physics joint inversion: Is it worth the effort?, Department of Earth Sciences, Uppsala University, Uppsala, Sweden, February 24, 2016.

  6. Experimentation and modeling in the Earth sciences, Freie Katholische Schule Sumatra, Zürich, Switzerland, October 26, 2015.

  7. Multi-physics joint inversion: Simply too complex?, MODFLOW and More 2015, Colorado School of Mines, Golden, Colorado, May 31–June 3, 2015.

  8. Fracture characterization through multi-physics joint inversion, AGU Fall Meeting, San Francisco, Calif., December 15–19, 2014.

  9. Multi-physics joint inversion: Simply too complex?, XX. International Conference on Computational Methods in Water Resources, University of Stuttgart, Germany, June 9–13, 2014.

  10. Coupled modeling and multi-physics joint inversion, Nanjing University, Department of Hydrosciences, Nanjing, Jiangsu, China, December 26, 2013.

  11. Similarities and differences of deep subsurface systems: Impact on the development of the TOUGH simulators, Geological Survey of Japan, Institute of Geology and Geoinformation, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan, November 29, 2013.

  12. The TOUGH simulator: Can it take the heat?, 35th New Zealand Geothermal Workshop, Rotorua, New Zealand, November 18–21, 2013.

  13. Models of different deep subsurface systems: How different are they?, Department of Earth Sciences, ETH Zürich, Switzerland, June 7, 2013.

  14. Joint hydrogeophysical inversion: Approaches and tools, C12 Energy, Berkeley, California, April 26, 2013.

  15. Joint hydrogeophysical inversion using iTOUGH2, SPE/Golden Gate Section, Chevron, San Ramon, California, April 10, 2013.

  16. Finding leaks using hydrogeophysical data and numerical models, Interagency Workshop on Monitoring for Early Detection of Underground Leaks at Nuclear Facilities, NRC, Rockville, Maryland, February 15, 2012.

  17. Soil structure identification through inverse modeling, AGU Fall Meeting, San Francisco, Dec. 5–9, 2011

  18. Joint inversion in hydrogeology: Standing the test of practical application, MODFLOW and More 2011: Integrated Hydrologic Modeling, Colorado School of Mines, Golden Colorado, June 5–8, 2011.

  19. Joint inversion in hydrogeology: Standing the test of practical application, Earth Science Department, Uppsala University, Uppsala, Sweden, March 18, 2011.

  20. Numerical modeling of tunnel hydrology using the TOUGH2 simulators, Hohei University, College of Civil and Transportation Engineering, Nanjing, China, October 21, 2010.

  21. Hydrogeological modeling in support of site characterization and performance assessment of the Yucca Mountain nuclear waste storage site, Jilin University, College of Environment and Resources, Changchun, Jilin, China, October 14, 2010.

  22. The use of numerical modeling for carbon sequestration research, Beijing Normal University, School of Environment, Beijing, China, October 11, 2010.

  23. Hydrogeological modeling in support of site characterization and performance assessment of the Yucca Mountain nuclear waste storage site, Beijing Normal University, School of Water Resources, Beijing, China, October 11, 2010.

  24. The use of numerical modeling for carbon sequestration research, Korea Institute for Geology, Mining, and Materials, KIGAM, Daejeon, Korea, October 4, 2010.

  25. Joint inversion in hydrogeology: Standing the test of practical application, IPRPI Symposium on Inverse Problems and System Identification in GeoSystems, Rensselaer Polytechnic Institute, Troy, NY, March 29, 2010.

  26. Practical aspects of error handling in multiphase inverse modeling, Seminar, UC Berkeley, Civil and Environmental Engineering, March 5, 2010.

  27. Joint multiphase inverse modeling: Standing the test of practical application, Environmental Systems Seminar, UC Merced, October 7, 2009.

  28. Joint multiphase inverse modeling: Standing the test of practical application, Workshop on “Energy, wind and water: algorithms for simulation, optimization and control”, sponsored by the New Zealand Institute for Mathematics & its Application (NZIMA), University of Auckland, New Zealand, February 9–12, 2009.

  29. Hydrogeological modeling in support of site characterization and performance assessment of the Yucca Mountain nuclear waste site, Colloquium at Sacramento State University, October 30, 2008.

  30. Joint multiphase inverse modeling: Standing the test of practical application, Department of Civil and Environmental Engineering, Environmental Fluid Mechanics and Hydrology, Stanford University, April 28, 2008.

  31. Joint hydrological-geophysical inversion for soil structure identification, presented at Summer Workshop of the New Zealand Institute of Mathematics and Applications “Partial Differential Equations: Analysis, Applications, and Inverse Problems”, Waitangi, New Zealand, January 8–13, 2007.

  32. Forward and inverse modeling of non-isothermal multiphase flow in fractured porous media, presented at Summer Workshop of the New Zealand Institute of Mathematics and Applications “Partial Differential Equations: Analysis, Applications, and Inverse Problems”, Waitangi, New Zealand, January 8–13, 2007.

  33. CO2 sequestration research at LBNL, Chinese Academy of Sciences, Institute of Geology and Geophysics, Sept. 20, 2006.

  34. Use of numerical modeling in support of site characterization and performance assessment at Yucca Mountain, China University of Geosciences, School of Environmental Studies, Wuhan, China, Sept 18, 2006.

  35. Forward and inverse modeling at Ordos Basin Symposium, Xi’an, China, November 7, 2004.

  36. Model conceptualization and heterogeneity, Ordos Basin Symposium, Xi’an, China, November 6, 2004.

  37. Site characterization and performance assessment, Beijing Research Institute of Uranium Geology (BRIUG), China National Nuclear Corporation, Beijing, China, November 5, 2004.

  38. Testing and modeling, Institute of Geology and Geophysics, China Academy of Science, Beijing, China, November 4, 2004.

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