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GEYSER: 3D thermo-hydrodynamic reactive transport numerical simulator including porosity and permeability evolution using GPU clusters

  • Reza SohrabiEmail author
  • Samuel Omlin
  • Stephen A. Miller
Original Paper
  • 19 Downloads

Abstract

GEYSER, an acronym for Graphic processing units (GPU) cluster computing for Enhanced hYdrothermal SystEms with Reactive transport, is a 3D simulator that includes porosity and permeability evolution for mass and heat transport processes in fractured geological media. The simulator also includes mass porosity and permeability evolution in response to dehydration reactions of hydrous minerals. GEYSER utilizes a finite difference scheme to solve the governing PDEs associated with 3D large-scale hydrothermal systems or geothermal reservoirs. This tool is a high performance code using GPU workstations or cluster technology. The physical processes implemented into the code are those associated with deep hydrogeological complexes where high fluid pressures generated by dehydration reactions can be sufficient to induce hydrofractures that significantly influence the porosity and permeability structures within geological formation. The governing equations are described and implemented and applied to a simplified 3D model of a magmatic intrusion at depth underlying a deep sedimentary cover. Close to ideal, weak scaling is demonstrated on GPU clusters with up to 128 GPUs. The numerical model can be used to investigate and understand coupled and time-dependent hydromechanical and thermodynamic processes at high resolution of the 3D computational domain. Applications include the hydrogeology of volcanic environments or exploitation of sediment-hosted geothermal resources. The code can also be suited for porosity and permeability evolution regarding pressure and temperature reaction rate to rock decarbonization for CO2 sequestration in deep sedimentary formations.

Keywords

High-performance computing (HPC) Graphic processing unit (GPU) 3D numerical modelling Hydrothermal systems Geothermal reservoirs Dehydration processes 

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Notes

Acknowledgements

We would like to thank Y. Y. Podladchikov, B. Malvoisin, and L. Räss for support and computing resources at the Swiss Geocomputing Centre at the University of Lausanne. We appreciate constructive discussions with G. Jansen and B. Galvan. We acknowledge reviewers that provide thorough and constructive comments of the original draft.

Funding

This work was funded by a grant from the Swiss National Science Foundation (SNSF) Project No. 200021-16005/1.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Ayachit, U., Bauer, A., Geveci, B., O'Leary, P., Moreland, K., Fabian, N., Mauldin, J.: Paraview catalyst: enabling in situ data analysis and visualization. In: Proceedings of the first workshop on in situ infrastructures for enabling extreme-scale analysis and visualization, pp. 25–29. ACM (2015)Google Scholar
  2. 2.
    Bangerth, W., Hartmann, R., Kanschat, G.: Deal. II—a general-purpose object-oriented finite element library. ACM transactions on Mathematical Software (TOMS). 33(4), 24 (2007)CrossRefGoogle Scholar
  3. 3.
    Bear, J.: Dynamics of fluids in porous media. Dover, New York (1972)Google Scholar
  4. 4.
    Bowen, R.: Geothermal resources. Halsted Press, New York (1979)Google Scholar
  5. 5.
    Carman, P.C.: Fluid flow through granular beds. Trans. Inst. Chem. Eng. 15, 150–166 (1937)Google Scholar
  6. 6.
    Carman, P.C.: Flow of gases through porous media. Academic Press (1956)Google Scholar
  7. 7.
    Connolly, J.A.D., Holness, M.B., Rubie, D.C., Rushmer, T.: Reaction-induced microcracking: an experimental investigation of a mechanism for enhancing anatectic melt extraction. Geology. 25(7), 591–594 (1997)CrossRefGoogle Scholar
  8. 8.
    Datta, K., Williams, S., Volkov, V., Carter, J., Oliker, L., Shalf, J., Yelick, K.: Auto-tuning the 27-point stencil for multicore. In: In Proc. iWAPT2009: the Fourth International Workshop on Automatic Performance Tuning (2009)Google Scholar
  9. 9.
    Flemisch, B., Darcis, M., Erbertseder, K., Faigle, B., Lauser, A., Mosthaf, K., Müthing, S., Nuske, P., Tatomir, A., Wolff, M., Helmig, R.: DuMux: DUNE for multi-{phase,component,scale,physics,…} flow and transport in porous media. Adv. Water Resour. 34(9), 1102–1112 (2011)CrossRefGoogle Scholar
  10. 10.
    Galvan, B., Miller, S.: A full GPU simulation of evolving fracture networks in a heterogeneous poro-elasto-plastic medium with effective-stress-dependent permeability. In: GPU solutions to multi-scale problems in science and engineering, pp. 305–319. Springer, Berlin (2013)CrossRefGoogle Scholar
  11. 11.
    Gaston, D., Newman, C., Hansen, G., Lebrun-Grandie, D.: MOOSE: a parallel computational framework for coupled systems of nonlinear equations. Nucl. Eng. Des. 239(10), 1768–1778 (2009)CrossRefGoogle Scholar
  12. 12.
    Gysi, T., Osuna, C., Fuhrer, O., Bianco, M., Schulthess, T.C.: STELLA: a domain-specific tool for structured grid methods in weather and climate models. In: 2015 SC—international conference for high performance computing, networking, storage and analysis, pp. 1–12. IEEE (2015)Google Scholar
  13. 13.
    Hammond, G.E., Lichtner, P.C., Mills, R.T.: Evaluating the performance of parallel subsurface simulators: an illustrative example with PFLOTRAN. Water Resour. Res. 50(1), 208–228 (2014)CrossRefGoogle Scholar
  14. 14.
    Hao, Y., Sun, Y., Nitao, J.J.: Overview of NUFT: a versatile numerical model for simulating flow and reactive transport in porous media. Groundw. React. Transp. Models. 212e239(28), (2012).  https://doi.org/10.2174/978160805306311201010212
  15. 15.
    Henderson, A., Ahrens, J., Law, C.: The ParaView Guide, pp. 1–276. Kitware, Clifton Park, NY (2004)Google Scholar
  16. 16.
    Ingebritsen, S.E., Sanford, W.E.: Groundwater in geologic processes. Cambridge University Press, Cambridge (1999)Google Scholar
  17. 17.
    Ingebritsen, S.E., Geiger, S., Hurwitz, S., Driesner, T.: Numerical simulation of magmatic hydrothermal systems. Rev. Geophys. 48(1), (2010)Google Scholar
  18. 18.
    Jansen, G., Sohrabi, R., Miller, S.A.: HULK—simple and fast generation of structured hexahedral meshes for improved subsurface simulations. Comput. Geosci. 99, 159–170 (2017). https://www.sciencedirect.com/science/article/pii/S0098300416307063
  19. 19.
    Jeanne, P., Rutqvist, J., Vasco, D., Garcia, J., Dobson, P.F., Walters, M., Borgia, A.: A 3D hydrogeological and geomechanical model of an enhanced geothermal system at the geysers, California. Geothermics. 51, 240–252 (2014)CrossRefGoogle Scholar
  20. 20.
    Joseph, D.D.: Stability of fluid motions II. Springer, Berlin (1976) [2.3, 6.3, 6.4]CrossRefGoogle Scholar
  21. 21.
    Karyono, K., Obermann, A., Lupi, M., Masturyono, M., Hadi, S., Syafri, I., Abdurrokhim, A., Mazzini, A.: Lusi, a clastic-dominated geysering system in Indonesia recently explored by surface and subsurface observations. Terra Nova. 29, 13–19 (2017)CrossRefGoogle Scholar
  22. 22.
    Kolditz, O., Bauer, S., Bilke, L., Böttcher, N., Delfs, J.O., Fischer, T., et al.: OpenGeoSys: an open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media. Environ. Earth Sci. 67(2), 589–599 (2012)CrossRefGoogle Scholar
  23. 23.
    Kozeny, J.: Uber kapillare leitung der wasser in Boden. R. Acad. Sci., Vienna, Proc Class I. 136, 271–306 (1927)Google Scholar
  24. 24.
    Krotkiewski, M., Dabrowski, M.: Efficient 3D stencil computations using CUDA. Parallel Comput. 39(10), 533–548 (2013)CrossRefGoogle Scholar
  25. 25.
    Lichtner, P.C., Carey, J.W.: Incorporating solid solutions in reactive transport equations using a kinetic discrete-composition approach. Geochimica et Cosmochimica Acta. 70(6), 1356–1378 (2006).  https://doi.org/10.1016/j.gca.2005.11.028 CrossRefGoogle Scholar
  26. 26.
    Lichtner, P.C., Kang, Q.: Upscaling pore-scale reactive transport equations using a multiscale continuum formulation. Water Resources Research. 43, W12S15 (2007).  https://doi.org/10.1029/2006WR005664 CrossRefGoogle Scholar
  27. 27.
    Lizarralde, D., Soule, S.A., Seewald, J.S., Proskurowski, G.: Carbon release by off-axis magmatism in a young sedimented spreading centre. Nat. Geosci. 4(1), 50–54 (2011)CrossRefGoogle Scholar
  28. 28.
    Malvoisin, B., Podladchikov, Y.Y., Vrijmoed, J.C.: Coupling changes in densities and porosity to fluid pressure variations in reactive porous fluid flow: local thermodynamic equilibrium. Geochem. Geophys. Geosyst. 16(12), 4362–4387 (2015)CrossRefGoogle Scholar
  29. 29.
    Mazzini, A., Etiope, G., Svensen, H.: A new hydrothermal scenario for the 2006 Lusi eruption, Indonesia. Insights from Gas Geochemistry: Earth and Planetary Science Letters. 317–318(0), 305–318 (2012)Google Scholar
  30. 30.
    Micikevicius, P.: 3D finite difference computation on GPUs using CUDA. In: Proceedings of 2nd workshop on general purpose processing on graphics processing units, pp. 79–84. ACM (2009)Google Scholar
  31. 31.
    Miller, S.A., Van Der Zee, W., Olgaard, D.L., Connolly, J.A.D.: A fluid-pressure feedback model of dehydration reactions: experiments, modelling, and application to subduction zones. Tectonophysics. 370(1), 241–251 (2003)CrossRefGoogle Scholar
  32. 32.
    Nield, D.A., Bejan, A.: Convection in porous media (vol. 3). Springer, New York (2006)Google Scholar
  33. 33.
    Omlin, S., Räss, L., & Podladchikov, Y. Y. (2015). HPC.M—the MATLAB HPC compiler and its use for solving 3D poromechanics on supercomputers, platform for advanced scientific computing conference ETH Zurich, SwitzerlandGoogle Scholar
  34. 34.
    Omlin, S., Malvoisin, B., Podladchikov, Y.Y.: Pore fluid extraction by reactive solitary waves in 3-D. Geophys. Res. Lett. 44(18), 9267–9275 (2017)CrossRefGoogle Scholar
  35. 35.
    Pollack, H.N., Hurter, S.J., Johnson, J.R.: Heat flow from the Earth's interior: analysis of the global data set. Rev. Geophys. 31(3), 267–280 (1993)CrossRefGoogle Scholar
  36. 36.
    Steefel, C.I., DePaolo, D.J., Lichtner, P.C.: Reactive transport modelling: an essential tool and a new research approach for the earth sciences. Earth Planet. Sci. Lett. 240(3–4), 539–558 (2005).  https://doi.org/10.1016/j.epsl.2005.09.017 CrossRefGoogle Scholar
  37. 37.
    Steefel, C.I., Appelo, C.A.J., Arora, B., Jacques, D., Kalbacher, T., Kolditz, O., et al.: Reactive transport codes for subsurface environmental simulation. Comput. Geosci. 19(3), 445–478 (2015)CrossRefGoogle Scholar
  38. 38.
    Stefano, R. and Hailu, T.S. (2014). Solve. The exascale effect: benefits of supercomputing investment for U.S. Industry, Washington, DC. http://www.compete.org/reports/all/2695-solve
  39. 39.
    Strikwerda, J. C. (2004). Finite difference schemes and partial differential equations. Society for Industrial and Applied MathematicsGoogle Scholar
  40. 40.
    Su, D., Mayer, K.U., MacQuarrie, K.T.: Parallelization of MIN3P-THCm: a high performance computational framework for subsurface flow and reactive transport simulation. Environ. Model Softw. 95, 271–289 (2017)CrossRefGoogle Scholar
  41. 41.
    Sun, H., Feistel, R., Koch, M., Markoe, A.: New equations for density, entropy, heat capacity, and potential temperature of a saline thermal fluid. Deep-Sea Res. I Oceanogr. Res. Pap. 55(10), 1304–1310 (2008)CrossRefGoogle Scholar
  42. 42.
    Svensen, H., Hammer, Ø., Mazzini, A., Onderdonk, N., Polteau, S., Planke, S., Podladchikov, Y.Y.: Dynamics of hydrothermal seeps from the Salton Sea geothermal system (California, USA) constrained by temperature monitoring and time series analysis. J. Geophys. Res.: Solid Earth. 114(B9), (2009)Google Scholar
  43. 43.
    Vidal, O., Dubacq, B.: Thermodynamic modelling of clay dehydration, stability and compositional evolution with temperature, pressure and H2O activity. Geochim. Cosmochim. Acta. 73(21), 6544–6564 (2009)CrossRefGoogle Scholar
  44. 44.
    VisItusers.org, (n.d.). www.visitusers.org. Accessed May 2018
  45. 45.
    White, D.E.: Thermal waters of volcanic origin. Geol. Soc. Am. Bull. 68(12), 1637–1658 (1957)CrossRefGoogle Scholar
  46. 46.
    White, M.D., Oostrom, M.: STOMP subsurface transport over multiple phases version 3.0 user’s guide (No. PNNL-14286). Pacific Northwest National Lab., Richland, WA (2003)Google Scholar
  47. 47.
    Xu, T., Sonnenthal, E., Spycher, N., Pruess, K.: TOUGHREACT user’s guide: a simulation program for non-isothermal multiphase reactive geochemical transport in variably saturated geologic media, v1. 2.1 (No. LBNL-55460-2008). Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (2008)CrossRefGoogle Scholar
  48. 48.
    Zhang, K., Wu, Y.S., Pruess, K.: User’s guide for TOUGH2-MP—a massively parallel version of the TOUGH2 code (No. LBNL--315E). Ernest Orlando Lawrence Berkeley National Laboratory (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Hydrogeology and Geothermics (CHYN)University of NeuchâtelNeuchâtelSwitzerland
  2. 2.Swiss Geocomputing CentreUniversity of LausanneLausanneSwitzerland

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