Library Subscription: Guest
Journal of Machine Learning for Modeling and Computing

Published 4 issues per year

ISSN Print: 2689-3967

ISSN Online: 2689-3975

Indexed in

GEOTHERMALCLOUD: MACHINE LEARNING FOR GEOTHERMAL RESOURCE EXPLORATION

Volume 3, Issue 4, 2022, pp. 57-72
DOI: 10.1615/JMachLearnModelComput.2022046445
Get accessDownload

ABSTRACT

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, play fairway analysis (PFA) studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) have vast potential. However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive, with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCloud https://github.com/SmartTensors/GeoThermalCloud.jl. The GeoThermalCloud uses a series of un-supervised, supervised, and physics-informed ML methods available in SmartTensors AI platform https://github.com/SmartTensors. Through GeoThermalCloud, we can identify hidden patterns in the geothermal field data needed to discover blind systems efficiently. Crucial geothermal signatures often overlooked in traditional PFA are extracted using the GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA (ePFA), which is informative for efficient exploration. We applied our ML methodology to various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work). The results provide valuable insights into resource types within those regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

REFERENCES
  1. Ahmmed, B., Non-Negative Matrix Factorization to Discover Dominant Attributes in Utah FORGE Data, GRC Trans., vol. 44, p. 1281,2020a.

  2. Ahmmed, B., Unsupervised Machine Learning to Extract Dominant Geothermal Attributes in Hawaii Island Play Fairway Data, in Proc. of the Geothermal Resources Council's Annual Meeting & Expo, Reno, NV, pp. 18-21,2020b.

  3. Ahmmed, B. and Vesselinov, V.V., Exploration of Groundwater and Geothermal Characteristics of Tohatchi Hot Springs Aquifer at Local and Regional Scales, Tech. Rep., Los Alamos National Lab., Los Alamos, NM, 2022a.

  4. Ahmmed, B. and Vesselinov, V.V., Machine Learning and Shallow Groundwater Chemistry to Identify Geothermal Prospects in the Great Basin, USA, Renew. Energy, vol. 197, pp. 1034-1048,2022b.

  5. Ahmmed, B., Vesselinov, V.V., Woods, S., Singer, S., and Lake, S., Geothermal Resource Analysis at Tohatchi Hot Springs, New Mexico, Tech. Rep., Los Alamos National Lab., Los Alamos, NM, 2021.

  6. Alexandrov, B.S. and Vesselinov, V. V., Blind Source Separation for Groundwater Pressure Analysis Based on Nonnegative Matrix Factorization, Water Resour. Res., vol. 50, no. 9, pp. 7332-7347,2014.

  7. Allis, R., Moore, J., Davatzes, N., Gwynn, M., Hardwick, C., Kirby, S., McLennan, J., Pankow, K., Potter, S., and Simmons, S., EGS Concept Testing and Development at the Milford, Utah FORGE Site, in Proc. of 41st Workshop on Geohermal Reservoir Engineering, Stanford, CA, 2016.

  8. Brandt, A., Tularosa Basin Play Fairway Analysis: Methodology Flow Charts, Tech. Rep., USDOE Geothermal Data Repository, University of Utah, Salt Lake, UT, 2015.

  9. Brown, D.W., Duchane, D.V., Heiken, G., and Hriscu, V.T., Mining the Earth's Heat: Hot Dry Rock Geothermal Energy, New York: Springer Science & Business Media, 2012.

  10. Cheng, A., President's Page: Geothermal Energy: Current and Future, Leading Edge, vol. 41, no. 9, pp. 588-589, 2022.

  11. Cichocki, A., Zdunek, R., Phan, A.H., and Amari, S.I., Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation, Hoboken, NJ: John Wiley & Sons, 2009.

  12. Cuomo, S., Di Cola, V.S., Giampaolo, F., Rozza, G., Raissi, M., and Piccialli, F., Scientific Machine Learning through Physics-Informed Neural Networks: Where We Are and What's Next, arXiv: 2201.05624, 2022.

  13. Dobson, P.F., A Review of Exploration Methods for Discovering Hidden Geothermal Systems, GRC Trans., vol. 40, pp. 695-706,2016.

  14. Faulds, J., Hinz, N., Coolbaugh, M., Shevenell, L., and Siler, D., The Nevada Play Fairway Project - Phase II: Initial Search for New Viable Geothermal Systems in the Great Basin Region, Western USA, GRC Trans., vol. 40, pp. 535-540,2016.

  15. Fournier, R., Chemical Geothermometers and Mixing Models for Geothermal Systems, Geothermics, vol. 5, nos. 1-4, pp. 41-50,1977.

  16. Frash, L.P., Geothermal Design Tool (GeoDT), in Proc. of the 46th Workshop on Geothermal Reservoir Engineering, Stanford, CA, vol. 17, February 15,2021.

  17. Fridriksson, T. and Armannsson, H., Application of Geochemistry in Geothermal Resource Assessments, Short Course on Geothermal Development in Central America Resource Assessment and Environmental Management, El Salvador, 2007.

  18. Gehringer, M. and Loksha, V., Geothermal Handbook: Planning and Financing Power Generation, Tech. Rep., The World Bank, 2012.

  19. Geothermal Technologies Office, GeoVision: Harnessing the Heat Beneath Our Feet, accessed from https: //www. energy. gov/eere/geothermal/articles/geovision-harnessing-heat-beneath-our-feet, 2019.

  20. Hamm, S.G., Anderson, A., Blankenship, D., Boyd,L.W., Brown, E.A., Frone, Z., Hamos, I., Hughes, H.J., Kalmuk, M., and Marble, A., Geothermal Energy R&D: An Overview of the US Department of Energy's Geothermal Technologies Office, J. Energy Resour. Technol., vol. 143,no. 10,p. 100801,2021.

  21. Hammond, G.E., Lichtner, P.C., and Mills, R., Evaluating the Performance of Parallel Subsurface Simula-tors: An Illustrative Example with PFLOTRAN, Water Resour. Res., vol. 50, no. 1, pp. 208-228,2014.

  22. Holmes, R.C. and Fournier, A., Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration, Energies, vol. 15, no. 5, p. 1929,2022.

  23. Iliev, F.L., Stanev, V.G., Vesselinov, V.V., and Alexandrov,B.S.,Nonnegative Matrix Factorization for Iden-tification of Unknown Number of Sources Emitting Delayed Signals, PLoS One, vol. 13, p. e0193974, 2018.

  24. Imolauer, K., Richter, B., and Berger, A., Non-Technical Barriers of Geothermal Projects, in Proc. World Geothermal Congress, Bali, Indonesia, 2010.

  25. Innes,M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E., Shah, V.B., andTebbutt, W., A Differentiable Programming System to Bridge Machine Learning and Scientific Computing, arXiv: 1907.07587,2019.

  26. Ito, G., Frazer, N., Lautze, N., Thomas, D., Hinz, N., Waller, D., Whittier, R., and Wallin, E., Play Fairway Analysis of Geothermal Resources Across the State of Hawaii: 2. Resource Probability Mapping, Geothermics, vol. 70, pp. 393-405,2017.

  27. Klein, C., Advances in the Past 20 Years: Geochemistry in Geothermal Exploration, Resource Evaluation and Reservoir Management, GRC Trans., vol. 31, pp. 17-22,2007.

  28. Lautze, N., Thomas, D., Hinz, N., Apuzen-Ito, G., Frazer, N., and Waller, D., Play Fairway Analysis of Geothermal Resources Across the State of Hawaii: 1. Geological, Geophysical, and Geochemical Datasets, Geothermics, vol. 70, pp. 376-392,2017.

  29. Lee, D.D. and Seung, H.S., Learning the Parts of Objects by Non-Negative Matrix Factorization, Nature, vol. 401, p. 788-791,1999.

  30. Levine, A. and Young, K.R., Crossing the Barriers: An Analysis of Land Access Barriers to Geothermal Development and Potential Improvement Scenarios, GRC Trans., vol. 41, pp. 2164-2192,2017.

  31. Levitte, D. and Gambill, D., Geothermal Potential of West-Central New Mexico from Geochemical and Thermal Gradient Data, Tech. Rep., Los Alamos Scientific Lab., NM, 1980.

  32. Lichtner, P.C., Hammond, G.E., Lu, C., Karra, S., Bisht, G., Andre, B., Mills, R.T., Kumar, J., and Frederick, J.M., PFLOTRAN User Manual, Tech. Rep., 2020.

  33. Lindsey, C.R., Ayling,B.F., Asato, G., Seggiaro,R., Carrizo,N.,Larcher,N., Marquetti, C.,Naon, V., Serra, A.C., and Faulds, J.E., Play Fairway Analysis for Geothermal Exploration in North-Western Argentina, Geothermics, vol. 95, p. 102128,2021.

  34. Moore, J., McLennan, J., Allis, R., Pankow, K., Simmons, S., Podgorney, R., Wannamaker, P., Bartley, J., Jones, C., and Rickard, W., The Utah Frontier Observatory for Research in Geothermal Energy (FORGE): An International Laboratory for Enhanced Geothermal System Technology Development, in Proc. of 44th Workshop on Geohermal Reservoir Engineering, Stanford, CA, 2019.

  35. Moraga, J., Duzgun, H., Cavur, M., and Soydan, H., The Geothermal Artificial Intelligence for Geothermal Exploration, Renew. Energy, vol. 192, pp. 134-149,2022.

  36. Mudunuru, M.K., Ahmmed, B., Karra, S., Vesselinov, V.V., Livingston, D.R., and Middleton, R.S., Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration, in Proc. of 45th Workshop on Geothermal Reservoir Engineering, Stanford, CA, 2020.

  37. Nash, G., Tularosa Basin Play Fairway Analysis: White Sands Missile Range Main Cantonment and NASA Area Faults, New Mexico, Tech. Rep., USDOE Geothermal Data Repository, Energy and Geoscience, 2017.

  38. Nash, G.D., Brandt, A., Pfaff, B., Hardwick, C., Gwynn, M., Blake, K., Simmons, S., and Bennett, C.R., Phase 2: Updated Geothermal Play Fairway Analysis of the Tularosa Basin, New Mexico, GRC Trans., vol. 41, pp. 2312-2327,2017.

  39. Rackauckas, C., Edelman, A., Fischer, K., Innes, M., Saba, E., Shah, V.B., and Tebbutt, W., Generalized Physics-Informed Learning through Language-Wide Differentiable Programming, accessed from https://ceur-ws.org/Vol-2587/articleiS.pdf, 2021.

  40. Rhodes, G., Roberts, B., Pauling, H., Taverna, N., and Warren, P., Analysis of Selected Publicly Available Geothermal Exploration Data Gaps, Tech. Rep., National Renewable Energy Lab., Golden, CO, 2021.

  41. Rousseeuw, P. J., Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis, J. Comput. Appl. Math, vol. 20, pp. 53-65,1987.

  42. Siler, D.L., Pepin, J.D., Vesselinov, V. V., Mudunuru, M.K., and Ahmmed, B., Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data, Brady Geothermal Field, Nevada, Geotherm. Energy, vol. 9, no. 1,pp. 1-17,2021.

  43. Siler, D.L., Zhang, Y., Spycher, N.F., Dobson, P.F., McClain, J.S., Gasperikova, E., Zierenberg, R.A., Schiffman, P., Ferguson, C., and Fowler, A., Play-Fairway Analysis for Geothermal Resources and Exploration Risk in the Modoc Plateau Region, Geothermics, vol. 69, pp. 15-33,2017.

  44. Smith, C.M., Machine Learning Techniques Applied to the Nevada Geothermal Play Fairway Analysis, PhD, University of Nevada, Reno, 2021.

  45. Thomas, D.M., A Geochemical Model of the Kilauea East Rift Zone, U.S. Geological Survey Professional Paper 1350,1987.

  46. Vesselinov, V., Ahmmed, B., Mudunuru, M., Pepin, J., Burns, E., Siler, D., Karra, S., and Middleton, R., Discovering Hidden Geothermal Signatures Using Non-Negative Matrix Factorization with Customized fc-Means Clustering, Geothermics, vol. 106, p. 102576,2022a.

  47. Vesselinov, V.V., Ahmmed, B., Frash, L., and Mudunuru, M.K., GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources, Tech. Rep., 2022b.

  48. Vesselinov, V.V., Alexandrov, B.S., and O'Malley, D., Contaminant Source Identification Using Semi-Supervised Machine Learning, J. ContaminantHydrol., vol. 212, pp. 134-142,2018.

  49. Vesselinov, V. V., Mudunuru, M.K., Ahmmed, B., Karra, S., and Middleton, R.S., Discovering Signatures of Hidden Geothermal Resources Based on Unsupervised Learning, in Proc. of 45th Workshop on Geothermal Reservoir Engineering, Stanford, CA, 2020.

  50. Vesselinov, V.V., Mudunuru, M.K., Karra, S., O'Malley, D., and Alexandrov, B.S., Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing, J. Comput. Phys., vol. 395, pp. 85-104,2019.

  51. Vesselinov, V.V., O'Malley, D., Frash, L.P., Ahmmed, B., Rupe, A.T., Karra, S., Middleton, R.S., Alexandrov, B., Mudunuru, M.K., and Mims, M., GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models Using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources, Tech. Rep., Los Alamos National Lab., Los Alamos, NM, 2021.

  52. Wagstaff, K., Cardie, C., Rogers, S., and Schrodl, S., Constrained fc-Means Clustering with Background Knowledge, in Proc. of the Eighteenth International Conference on Machine Learning, Williams College, Williamstown, MA, pp. 577-584, June 28-July 1, 2001.

  53. Weers, J. and Anderson, A., The DOE Geothermal Data Repository and the Future of Geothermal Data, Tech. Rep., National Renewable Energy Lab., Golden, CO, 2016.

  54. Weers, J., Anderson, A., and Taverna, N., The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data, Tech. Rep., National Renewable Energy Lab., Golden, CO, 2022.

  55. Wicks, F., New Horizons for Geothermal Energy, Mech. Eng., vol. 144, no. 5, pp. 34-39,2022.

  56. Williams, C.F., Reed, M.J., Mariner, R.H., DeAngelo, J., and Galanis, S.P., Assessment of Moderate and High-Temperature Geothermal Resources of the United States, Tech. Rep., US Geological Survey, 2008.

  57. Young, K.R., Levine, A., Cook, J., Hernandez, K., Ho, J., and Heimiller, D., Crossing the Barriers: An Analysis of Market Barriers to Geothermal Development and Potential Improvement Scenarios, Golden, CO, National Renewable Energy Laboratory Tech. Rep. NREL/CP-6A20-68996,2017.

Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections Prices and Subscription Policies Begell House Contact Us Language English 中文 Русский Português German French Spain