DOI: https://doi.org/10.61435/ijred.2025.61071

A scoping review of numerical modelling studies of geothermal reservoirs: Trends and opportunities post-COP25

1ETICA Consulting & Research, Melbourne, Australia

2Faculty of Engineering, Universidad de Medellín, Medellín, Colombia

Received: 24 Jan 2025; Revised: 29 Mar 2025; Accepted: 8 Apr 2025; Available online: 17 May 2025; Published: 1 Jul 2025.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

At the 28th Conference of Parties (COP28) a commitment to triple renewable energy capacity by 2030 was made. Currently at 16 GW, geothermal accounts for 0.5% of world-wide installed renewable electricity capacity. In this scoping review, Elsevier’s database was used to determine the role reservoir simulation has played and could continue to play in assisting the geothermal industry in achieving COP28's goal. The review includes journal papers published in English from 2020 to 2023. Particular attention was paid to the applications of TOUGH2 and COMSOL, the benefits of Machine Learning (ML) and recent projects that could assist in promoting the geothermal industry. The topics' categories comprised: Enhanced Geothermal Systems (EGS), hydrothermal, laboratory, and technology synergies. Outcomes of a bibliometric analysis elucidate these trends: ML is vital to ensuring the optimisation of geothermal resources; EGS and cross-industry projects are showing growing global interest. The likelihood of meeting the COP28 target for geothermal would be enhanced with increased participation from the South American and African countries. However, the industry’s growth in these continents is restricted by high initial investment costs, technical complexities, unclear regulatory frameworks, social acceptance, and difficulties with electrical grid integration. Suggestions for overcoming these barriers to development are proposed. A brief country case study is also presented. It focuses on the economic, environmental and technical context to understand the unique challenges and opportunities for geothermal. Finally, five areas for research and development opportunities were identified: Thermo-Hydro-Mechanical-Chemical processes, reinjection and induced seismicity, reservoir characterization, cross-industry collaborations, and laboratory studies.

Fulltext View|Download
Keywords: Geothermal; energy; modelling; TOUGH2; COMSOL; Machine Learning

Article Metrics:

Article Info
Section: Review Article
Language : EN
Recent articles
Optimised PCBM electron transport layer in inverted lead-free Cs3Bi2I9 flexible perovskite solar cells via FIRA Electrical performance for in-situ doping of phosphorous in silver paste screen-printed contact on p-type silicon solar cell A bilevel zonal dispatch strategy considering electric vehicle users' demand response Mapping the research landscape of energy market and renewable energy: A bibliometric analysis A scoping review of numerical modelling studies of geothermal reservoirs: Trends and opportunities post-COP25 More recent articles
Most cited articles
Numerical Performance Analyses of Different Airfoils for Use in Wind Turbines Energy Resource of Charcoals Derived from Some Tropical Fruits Nuts Shells Modelling the Kinetics of Biogas Production from Mesophilic Anaerobic Co-Digestion of Cow Dung with Plantain Peels MPPT Schemes for PV System under Normal and Partial Shading Condition: A Review Evaluation of Physico-Mechanical-Combustion Characteristics of Fuel Briquettes made from blends of Areca Nut Husk, Simarouba Seed Shell and Black Liquor More cited articles
  1. Abrasaldo, P. M. B., Zarrouk, S. J., & Kempa-Liehr, A. W. (2023). Characterising and predicting low discharge pressure events in less permeable geothermal production wells. Geothermics, 112. https://doi.org/10.1016/j.geothermics.2023.1027
  2. Adombi, A. V. D. P., Chesnaux, R., & Boucher, M. A. (2022). Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: A first study case application. Journal of Hydrology, 615. https://doi.org/10.1016/j.jhydrol.2022.128600
  3. Aguilar-Ojeda, J. A., Campos-Gaytán, J. R., Villela, A., Herrera-Oliva, C. S., Ramírez-Hernández, J., & Kretzschmar, T. G. (2021a). Understanding hydrothermal behavior of the Maneadero geothermal system, Ensenada, Baja California, Mexico. Geothermics, 89. https://doi.org/10.1016/j.geothermics.2020.101985
  4. Aguilar-Ojeda, J. A., Campos-Gaytán, J. R., Villela-Y-Mendoza, A., Herrera-Oliva, C. S., Ramírez-Hernández, J., & Kretzschmar, T. G. (2021b). A numerical tool in MATLAB used to adapt three-dimensional conceptual models from ArcMap to TOUGH3. Environmental Modelling & Software, 146, 105223. https://doi.org/10.1016/J.ENVSOFT.2021.105223
  5. Ahmad, M. W., Mourshed, M., & Rezgui, Y. (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147, 77–89. https://doi.org/10.1016/j.enbuild.2017.04.038
  6. Akhmetova, G., Panfilova, I., Bourlange, S., & Pereira, A. (2023). Simulation of wellbore temperature and heat loss in production well in geothermal site. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 45(2), 6262–6273. https://doi.org/10.1080/15567036.2023.2213673
  7. Alfaro, C., Rueda-Gutiérrez, J. B., Casallas, Y., Rodríguez, G., & Malo, J. (2021). Approach to the geothermal potential of Colombia. Geothermics, 96. https://doi.org/10.1016/j.geothermics.2021.102169
  8. Ali, M., Zhu, P., Jiang, R., Huolin, M., Ehsan, M., Hussain, W., Zhang, H., Ashraf, U., & Ullaah, J. (2023). Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based ensemble machine learning. Applied Soft Computing, 148. https://doi.org/10.1016/j.asoc.2023.110843
  9. Aliyu, M. D., & Archer, R. A. (2021a). A thermo-hydro-mechanical model of a hot dry rock geothermal reservoir. Renewable Energy, 176, 475–493. https://doi.org/10.1016/j.renene.2021.05.070
  10. Aliyu, M. D., & Archer, R. A. (2021b). Numerical simulation of multifracture HDR geothermal reservoirs. Renewable Energy, 164, 541–555. https://doi.org/10.1016/j.renene.2020.09.085
  11. Aliyu, M. D., Finkbeiner, T., Chen, H.-P., & Archer, R. A. (2023). A three-dimensional investigation of the thermoelastic effect in an enhanced geothermal system reservoir. Energy, 262. https://doi.org/10.1016/j.energy.2022.125466
  12. Aljubran, M., Nwosu, C., Okoroafor, E. R., Ochie, K. I., Nwosu, C. J., Smith, C. M., Ochie, K., & Gudmundsdottir, H. (2022). Recent Trends in Artificial Intelligence for Subsurface Geothermal Applications. 47th Workshop on Geothermal Reservoir Engineering Stanford University, 1–8
  13. Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., Duan, Y., Abdullah, A., Farhan, L., Lu, Y., Gupta, A., Albu, F., Abbosh, A., & Gu, Y. (2023). A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00727-2
  14. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  15. Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616
  16. Aydin, H., & Akin, S. (2021). Estimation of upcoming problems in Alaşehir geothermal field using a numerical reservoir model. Arabian Journal of Geosciences, 14(7). https://doi.org/10.1007/s12517-021-06830-z
  17. Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
  18. Baghban, H., Arulrajah, A., Narsilio, G. A., & Horpibulsuk, S. (2022). Assessing the performance of geothermal pavement constructed using demolition wastes by experimental and CFD simulation techniques. Geomechanics for Energy and the Environment, 29, 100271. https://doi.org/10.1016/J.GETE.2021.100271
  19. Battistelli, A., Finsterle, S., Marcolini, M., & Pan, L. (2020). Modeling of coupled wellbore-reservoir flow in steam-like supercritical geothermal systems. Geothermics, 86. https://doi.org/10.1016/j.geothermics.2019.101793
  20. Beckers, K. F., Lukawski, M. Z., Reber, T. J., Anderson, B. J., Moore, M. C., & Tester, J. W. (2013). Introducing Geophires v1.0: software package for estimating levelized cost of electricity and/or heat from Enhanced Geothermal Systems. Proceedings, Thirty-Eighth Workshop on Geothermal Reservoir Engineering, 1–8
  21. Beckers, K. F., Rangel-Jurado, N., Chandrasekar, H., Hawkins, A. J., Fulton, P. M., & Tester, J. W. (2022). Techno-Economic Performance of Closed-Loop Geothermal Systems for Heat Production and Electricity Generation. Geothermics, 100. https://doi.org/10.1016/j.geothermics.2021.102318
  22. Ben Aoun, M. A., & Madarász, T. (2022). Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site. Energies, 15(12). https://doi.org/10.3390/en15124288
  23. Bento, F., Garotti, L., & Mercado, M. P. (2021). Organizational resilience in the oil and gas industry: A scoping review. Safety Science, 133. https://doi.org/10.1016/j.ssci.2020.105036
  24. Bobadilla, J. (2022). Machine Learning y Deep Learning. Usando Python, Scikit y Kera. RA-MA Editorial
  25. Brigham, W. E., & Morrow, W. B. (1977). p/Z Behavior for Geothermal Steam Reservoirs. Society of Petroleum Engineers Journal, 17(06), 407–412
  26. Brommer, M.B. (2025). International Geothermal Association Academy Course on Policies and Regulations
  27. Burnell, J., Clearwater, E., Croucher, A., Kissling, W., O’sullivan, J., O’sullivan, M., & Yeh, A. (2012). Future directions in geothermal modelling. New Zealand Geothermal Workshop, 1–10
  28. Cacas, M. (1989). Development of a discrete stochastic 3D model for the flow and transport simulation of matter and heat in fracture medium. PhD dissertation, École nationale supérieure des mines de Paris
  29. Cao, H. (2002). Development of Techniques for General Purpose Simulation. PhD dissertation, Stanford University, Department of petroleum engineering
  30. Cariaga, C. (2022, February 16). Construction of Bolivia’s first geothermal plant progressing. ThinkGeoenergy. https://www.thinkgeoenergy.com/construction-of-bolivias-first-geothermal-plant-progressing/#:~:text=Spain%2Dbased%20Sacyr%20has%20provided,will%20be%20operational%20by%20April
  31. Cariaga, C. (2023). The COP28 pledge to triple up by 2030 and implications for geothermal. ThinkGeoenergy
  32. Cariaga, C. (2024, October 11). Italy looks to end decade-long drought of new geothermal power stations. ThinkGeoenergy. https://www.thinkgeoenergy.com/italy-looks-to-end-decade-long-drought-of-new-geothermal-power-stations/
  33. Castañeda-Herrera, C. A., Black, J. R., Llanos, E. M., Stevens, G. W., & Haese, R. R. (2018). Formation of an amorphous silica gel barrier under CO2 storage conditions. International Journal of Greenhouse Gas Control, 78, 27–36. https://doi.org/10.1016/J.IJGGC.2018.07.013
  34. Castanier, L., & Sanyal, S. K. (1980). Geothermal reservoir - A modeling review of approaches. Geothermal Resources Council Transactions, 4
  35. Castillo-Reyes, O., Hu, X., Wang, B., Wang, Y., & Guo, Z. (2023). Electromagnetic imaging and deep learning for transition to renewable energies: a technology review. Frontiers in Earth Science, 11. Frontiers Media SA. https://doi.org/10.3389/feart.2023.1159910
  36. Castillo-Reyes, O., Prol-Ledesma, R. M., Corbo-Camargo, F., & Rojas, O. (2024). Geothermal resources in Latin-America and their exploration using electromagnetic methods. Geothermal Energy, 12(1).. https://doi.org/10.1186/s40517-024-00314-5
  37. Céspedes, S., Cano, N. A., Foo, G., Jaramillo, D., Martinez, D., Gutiérrez, M., Pataquiba, J., Rojas, J., Cortés, F. B., & Franco, C. A. (2022). Technical and Environmental Feasibility Study of the Co-Production of Crude Oil and Electrical Energy from Geothermal Resources: First Field Trial in Colombia. Processes, 10(3). https://doi.org/10.3390/pr10030568
  38. Chen, Y., & Zhang, D. (2020). Physics-Constrained Deep Learning of Geomechanical Logs. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5932–5943. https://doi.org/10.1109/TGRS.2020.2973171
  39. Cheng, P., & Takahashi, P. (1973). The Hawaii Geothermal Project
  40. Chiodi, A. L., Filipovich, R. E., Esteban, C. L., Pesce, A. H., & Stefanini, V. A. (2020). Geothermal Country Update of Argentina: 2015-2020. Proceedings World Geothermal Congress 2020+1
  41. Clauser, C. (2003). Numerical Simulation of Reactive Flow in Hot Aquifers (C. Clauser, Ed.). Springer Berlin, Heidelberg
  42. Clearwater, J., & Franz, P. (2019). Introducing the volsung geothermal simulator: features and applications. Proceedings 41st New Zealand Geothermal Workshop, 1–7. https://vtk.org/
  43. Collard, N., Faney, T., Teboul, P. A., Bachaud, P., Cacas-Stentz, M. C., & Gout, C. (2023). Machine learning model predicting hydrothermal dolomitisation for future coupling of basin modelling and geochemical simulations. Chemical Geology, 637. https://doi.org/10.1016/j.chemgeo.2023.121676
  44. Croucher, A., O’Sullivan, M., O’Sullivan, J., Yeh, A., Burnell, J., & Kissling, W. (2020). Waiwera: A parallel open-source geothermal flow simulator. Computers and Geosciences, 141. https://doi.org/10.1016/j.cageo.2020.104529
  45. Daniilidis, A., Nick, H. M., & Bruhn, D. F. (2020). Interdependencies between physical, design and operational parameters for direct use geothermal heat in faulted hydrothermal reservoirs. Geothermics, 86. https://doi.org/10.1016/j.geothermics.2020.101806
  46. Danko, G. (2008). MULTIFLUX V5.0 software documentation qualification documents, software tracking number: 1002-5.0-00. Software Management Office, Berkeley National Laboratory
  47. De Silva, V. R. S., Ranjith, P. G., Perera, M. S. A., Wu, B., & Rathnaweera, T. D. (2018). A modified, hydrophobic soundless cracking demolition agent for non-explosive demolition and fracturing applications. Process Safety and Environmental Protection, 119, 1–13. https://doi.org/10.1016/j.psep.2018.07.010
  48. Di Salvo, C. (2022). Improving Results of Existing Groundwater Numerical Models Using Machine Learning Techniques: A Review. Water, 14(15). MDPI. https://doi.org/10.3390/w14152307
  49. DOE. (2019). GeoVision: Harnessing the Heat Beneath Our Feet. https://www.energy.gov/sites/default/files/2019/06/f63/GeoVision-full-report-opt.pdf
  50. DOE (2025). DOE Identifies 16 Federal Sites Across the Country for Data Center and AI Infrastructure Development. https://www.energy.gov/articles/doe-identifies-16-federal-sites-across-country-data-center-and-ai-infrastructure
  51. Duplyakin, D., Beckers, K. F., Siler, D. L., Martin, M. J., & Johnston, H. E. (2022). Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning. Energies, 15(3). https://doi.org/10.3390/en15030967
  52. EPRA (Energy & Petroleum Regulatory Authority). (2023). Energy-and-Petroleum-Biannual-Statistics-Report-July-December-2023
  53. ESMAP Energy Sector Management Assistance Program. (2023). Geothermal Energy: Unveiling the Socioeconomic Benefits
  54. Espinosa Zúñiga, J. J. (2020). Aplicación de algoritmos Random Forest y XGBoost en una base de solicitudes de tarjetas de crédito. Ingeniería Investigación y Tecnología, 21(3), 1–16. https://doi.org/10.22201/fi.25940732e.2020.21.3.022
  55. Ezekiel, J., Ebigbo, A., Adams, B. M., & Saar, M. O. (2020). Combining natural gas recovery and CO2-based geothermal energy extraction for electric power generation. Applied Energy, 269. https://doi.org/10.1016/j.apenergy.2020.115012
  56. Ezekiel, J., Kumbhat, D., Ebigbo, A., Adams, B. M., & Saar, M. O. (2021). Sensitivity of reservoir and operational parameters on the energy extraction performance of combined CO2-EGR–CPG systems. Energies, 14(19). https://doi.org/10.3390/en14196122
  57. Faraz, N., Khamehchi, E., & Tahan, H. N. (2021). Impact of boiling heat transfer on geothermal reservoir simulation using local thermal non-equilibrium model. Geothermics, 90. https://doi.org/10.1016/j.geothermics.2020.102016
  58. Feng, G., Wang, Y., Xu, T., Wang, F., & Shi, Y. (2021). Multiphase flow modeling and energy extraction performance for supercritical geothermal systems. Renewable Energy, 173, 442–454. https://doi.org/10.1016/j.renene.2021.03.107
  59. Flemisch, B., Fritz, J., Helmig, R., Niessner, J., & Wohlmuth, B. (2007). DUMUX: a multi-scale multi-physics toolbox for flow and transport processes in porous media. In A. Ibrahimbegovic, F. Dias, H. Matthies, & P. Wriggers (Eds.), ECCOMAS Thematic Conference on Multi-scale Computational Methods for Solids and Fluids (pp. 1–6)
  60. Franz, P. (2015, April 19). OOMPFS - a new software package for geothermal reservoir simulation. Proceedings World Geothermal Congress
  61. Franz, P., Clearwater, J., & Burnell, J. (2019). Introducing the Volsung geothermal simulator: benchmarking and performance. Proceedings 41st New Zealand Geothermal Workshop
  62. Gao, X., Li, T., Zhang, Y., Kong, X., & Meng, N. (2022). A Review of Simulation Models of Heat Extraction for a Geothermal Reservoir in an Enhanced Geothermal System. Energies 15(19). MDPI. https://doi.org/10.3390/en15197148
  63. Gerardi, G., Dublanchet, P., Jeannin, L., Kazantsev, A., Duboeuf, L., Ramadhan, I., Azis, H., Ganefianto, N., & Nugroho, I. A. (2024). Geomechanical modelling of injection-induced seismicity: the case study of the Muara Laboh geothermal plant. Geophysical Journal International, 237(2), 818–837. https://doi.org/10.1093/gji/ggae084
  64. Gisler, B. M., & Miller, S. A. (2021). A thermo-hydro-chemical model with porosity reduction and enthalpy production: Application to silica precipitation in geothermal reservoirs. Energy Reports, 7, 6260–6272. https://doi.org/10.1016/j.egyr.2021.09.076
  65. Goetzl, G., Stumpf, A. J., Lin, Y.-F., & Kłonowski, M. R. (2023, February). City-Scale Geothermal Energy Everywhere to Support Renewable Resilience-a Transcontinental Cooperation. Proceedings, 48th Workshop on Geothermal Reservoir Engineering. https://www.researchgate.net/publication/378545754
  66. Golden, S. (2023, July 23). This breakthrough could unlock the market for next-generation geothermal. TRELLIS. https://trellis.net/article/breakthrough-could-unlock-market-next-generation-geothermal/
  67. González, J. A. O., & Palacio, J. L. (2021). Wellhead power plants, an option to enhance the “Macizo Volcánico del Ruiz” Geothermal Project. Proceedings World Geothermal Congress 2020+1, 1–11
  68. Gudala, M., & Govindarajan, S. K. (2021). Numerical investigations on a geothermal reservoir using fully coupled thermo-hydro-geomechanics with integrated RSM-machine learning and ARIMA models. Geothermics, 96. https://doi.org/10.1016/j.geothermics.2021.102174
  69. Gudala, M., Tariq, Z., Govindarajan, S. K., Yan, B., & Sun, S. (2024). Fractured Geothermal Reservoir Using CO2 as Geofluid: Numerical Analysis and Machine Learning Modeling. ACS Omega, 9, 7746–7769. https://doi.org/10.1021/acsomega.3c07215
  70. Guerrero, F. J., Prol-Ledesma, R. M., Vidal-García, M. C., García-Zamorano, E. A., Hernández-Hernández, M. A., Pérez-Zárate, D., Rodríguez-Díaz, A. A., & Membrillo-Abad, A. S. (2023). A natural state model in TOUGH2 for the Las Tres Virgenes geothermal field, Baja California Sur, Mexico. Geothermics, 113. https://doi.org/10.1016/j.geothermics.2023.102750
  71. Guo, P., Wang, M., Dang, G., Zhu, T., Wang, J., & He, M. (2023). Evaluation method of underground water storage space and thermal reservoir model in abandoned mine. Rock Mechanics Bulletin, 2(2). https://doi.org/10.1016/j.rockmb.2023.100044
  72. Gutiérrez-Negrín, L. C. A. (2024). Evolution of worldwide geothermal power 2020–2023. Geothermal Energy. 12(1). https://doi.org/10.1186/s40517-024-00290-w
  73. Han, S., Williamson, B. D., & Fong, Y. (2021). Improving random forest predictions in small datasets from two-phase sampling designs. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01688-3
  74. Harlow, F. H., & Pracht, W. E. (1972). A theoretical study of geothermal energy extraction. Journal of Geophysical Research, 77(35), 7038–7048. https://doi.org/10.1029/JB077i035p07038
  75. Harshini, R. D. G. F., Pathegama Gamage, R., & Kumari, W. G. P. (2024). The role of thermal cycling in micro-cracking development and flow alteration: Advanced insights from CT and lab studies. Gas Science and Engineering, 125. https://doi.org/10.1016/j.jgsce.2024.205280
  76. Hayashi, K., Willis-Richards, J., Hopkirk, R. J., & Niibori, Y. (1999). Numerical models of HDR geothermal reservoirs-a review of current thinking and progress. Geothermics, 28(4–5), 507–518. https://doi.org/10.1016/S0375-6505(99)00026-7
  77. Hoang, A. T., Sandro Nižetić, Olcer, A. I., Ong, H. C., Chen, W. H., Chong, C. T., Thomas, S., Bandh, S. A., & Nguyen, X. P. (2021). Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications. Energy Policy, 154, 112322. https://doi.org/10.1016/J.ENPOL.2021.112322
  78. Hu, J., Xie, H., Sun, Q., Li, C., & Liu, G. (2021). Changes in the thermodynamic properties of alkaline granite after cyclic quenching following high temperature action. International Journal of Mining Science and Technology, 31(5), 843–852. https://doi.org/10.1016/j.ijmst.2021.07.010
  79. Hu, K., Liu, X., Chen, Z., & Grasby, S. E. (2023). Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada. Earth and Space Science, 10(12). https://doi.org/10.1029/2023EA003084
  80. Hu, X., Banks, J., Guo, Y., & Liu, W. v. (2022). Utilizing geothermal energy from enhanced geothermal systems as a heat source for oil sands separation: A numerical evaluation. Energy, 238. https://doi.org/10.1016/j.energy.2021.121676
  81. Hu, X., Banks, J., Wu, L., & Liu, W. v. (2020). Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta. Renewable Energy, 148, 1110–1123. https://doi.org/10.1016/j.renene.2019.09.141
  82. Huang, Y., Kong, Y., Cheng, Y., Zhu, C., Zhang, J., & Wang, J. (2023). Evaluating the long-term sustainability of geothermal energy utilization from deep coal mines. Geothermics, 107. https://doi.org/10.1016/j.geothermics.2022.102584
  83. Iaccarino, A. G., & Picozzi, M. (2023). Detecting the Preparatory Phase of Induced Earthquakes at The Geysers (California) Using K-Means Clustering. Journal of Geophysical Research: Solid Earth, 128(10). https://doi.org/10.1029/2023JB026429
  84. IEA (2024), The Future of Geothermal Energy, IEA, Paris https://www.iea.org/reports/the-future-of-geothermal-energy, Licence: CC BY 4.0
  85. IGA. (2024). Centers of Excellence in Geothermal Energy around the world. https://www.linkedin.com/posts/lovegeothermal_centers-of-excellence-in-geothermal-energy-activity-7252262060481298432-ef5f?utm_source=share&utm_medium=member_desktop
  86. IRENA. (2024a). Guyana Energy Profile. https://www.irena.org/-/media/Files/IRENA/Agency/Statistics/Statistical_Profiles/South%20America/Guyana_South%20America_RE_SP.pdf
  87. IRENA. (2024b). Paraguay Energy Profile. https://www.irena.org/-/media/Files/IRENA/Agency/Statistics/Statistical_Profiles/South%20America/Paraguay_South%20America_RE_SP.pdf
  88. IRENA. (2024c). Renewable capacity statistics 2024. https://www.irena.org
  89. IRENA. (2024d). Suriname Energy Profile. https://www.irena.org/-/media/Files/IRENA/Agency/Statistics/Statistical_Profiles/South-America/Suriname_South-America_RE_SP.pdf
  90. IRENA, & IGA. (2023). Global Geothermal Update 2023
  91. Jara-Alvear, J., de Wilde, T., Asimbaya, D., Urquizo, M., Ibarra, D., Graw, V., & Guzmán, P. (2023). Geothermal resource exploration in South America using an innovative GIS-based approach: A case study in Ecuador. Journal of South American Earth Sciences, 122. https://doi.org/10.1016/j.jsames.2022.104156
  92. Jiang, A., Qin, Z., Faulder, D., Cladouhos, T. T., & Jafarpour, B. (2023). A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs. Geothermics, 110. https://doi.org/10.1016/j.geothermics.2022.102643
  93. Kählert, S. (2023). BrineMine - Sustainable raw material and fresh water production from thermal fluids. German-Chilean Newspaper Cóndor. https://www.condor.cl/2023/04/29/deutsch-chilenisches-brinemine-projekt/
  94. Karvounis, D. C. (2013). Simulations of Enhanced Geothermal Systems with an Adaptive Hierarchical Fracture Representation. PhD Dissertation. ETH Zurich
  95. Kaya, E., & Zarrouk, S. J. (2017). Simulation of Reinjection of Non-Condensable Gas-Water Mixture into Geothermal Reservoirs. 42nd Workshop on Geothermal Reservoir Engineering
  96. Keilegavlen, E., Berge, R., Fumagalli, A., Starnoni, M., Stefansson, I., Varela, J., & Berre, I. (2021). PorePy: an open-source software for simulation of multiphysics processes in fractured porous media. Computational Geosciences, 25(1), 243–265. https://doi.org/10.1007/s10596-020-10002-5
  97. Kipp, K. L., & U.S. Geological Survey. (1987). HST3D; a computer code for simulation of heat and solute transport in three-dimensional ground-water flow systems. Water-Resources Investigations Report, https://pubs.usgs.gov/wri/1986/4095/report.pdf
  98. Kiryukhin, A. v, Nazhalova, I. N., & Zhuravlev, N. B. (2022). Hot water-methane reservoirs at southwest foothills of Koryaksky volcano, Kamchatka. Geothermics, 106. https://doi.org/10.1016/j.geothermics.2022.102552
  99. Kohl, T., & Hopkirk, R. J. (1995). “FRACure” — A simulation code for forced fluid flow and transport in fractured, porous rock. Geothermics, 24(3), 333–343. https://doi.org/10.1016/0375-6505(95)00012-F
  100. Kohl, T., & Mégel, T. (2005). Coupled hydro-mechanical modelling of the gpk3 reservoir stimulation at the European EGS site Soultz-Sous-Forêts. Thirtieth Workshop on Geothermal Reservoir Engineering, 1–8
  101. Kolditz, O., Bauer, S., Bilke, L., Böttcher, N., Delfs, J. O., Fischer, T., Görke, U. J., Kalbacher, T., Kosakowski, G., McDermott, C. I., Park, C. H., Radu, F., Rink, K., Shao, H., Shao, H. B., Sun, F., Sun, Y. Y., Singh, A. K., Taron, J., Walther, M., Wang, W., Watanabe. N., Wu, Y., Xie, M., Xu, W., & Zehner, B. (2012). OpenGeoSys: an open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media. Environmental Earth Sciences, 67(2), 589–599. https://doi.org/10.1007/s12665-012-1546-x
  102. Krogstad, S., Knut–Andreas, L., Møyner, Halvor Møll, N., Raynaud, X., & Skaflestad, B. (2015, February). MRST-AD – an Open-Source Framework for Rapid Prototyping and Evaluation of Reservoir Simulation Problems. SPE-173317 Reservoir Simulation Symposium, Houston, Texas, USA
  103. Kumari, W. G. P., & Ranjith, P. G. (2022). Experimental and Numerical Investigation of the Flow Behaviour of Fractured Granite under Extreme Temperature and Pressure Conditions. Sustainability (Switzerland), 14(14). https://doi.org/10.3390/su14148587
  104. Lacasse, C. M., Prado, E. M. G., Guimarães, S. N. P., Filho, O. A. de S., & Vieira, F. P. (2022). Integrated assessment and prospectivity mapping of geothermal resources for EGS in Brazil. Geothermics, 100. https://doi.org/10.1016/j.geothermics.2021.102321
  105. Lebbihiat, N., Atia, A., Arıcı, M., & Meneceur, N. (2021). Geothermal energy use in Algeria: A review on the current status compared to the worldwide, utilization opportunities and countermeasures. Journal of Cleaner Production 302. Elsevier Ltd. https://doi.org/10.1016/j.jclepro.2021.126950
  106. Lesmana, A., Pratama, H. B., Ashat, A., & Saptadji, N. M. (2021). Sustainability of geothermal development strategy using a numerical reservoir modeling: A case study of Tompaso geothermal field. Geothermics, 96. https://doi.org/10.1016/j.geothermics.2021.102170
  107. Li, G., Ji, J., Song, X., Shi, Y., Li, S., Song, Z., Song, G., & Xu, F. (2022). Research advances in multi-field coupling model for geothermal reservoir heat extraction. Energy Reviews, 1,(2).. https://doi.org/10.1016/j.enrev.2022.100009
  108. Li, J., Kang, F., Bai, T., Li, Z., Zhao, Q., Zhang, P., Zheng, T., & Sui, H. (2023). Geo-temperature response to reinjection in sandstone geothermal reservoirs. Geothermal Energy, 11(1). https://doi.org/10.1186/s40517-023-00277-z
  109. Liu, F., Wang, G.-L., Zhang, W., Yue, C., & Tao, L.-B. (2020). Using TOUGH2 numerical simulation to analyse the geothermal formation in Guide basin, China. Journal of Groundwater Science and Engineering, 8(4), 328–337. https://doi.org/10.19637/j.cnki.2305-7068.2020.04.003
  110. Liu, H., Wang, H., Lei, H., Zhang, L., Bai, M., & Zhou, L. (2020). Numerical modeling of thermal breakthrough induced by geothermal production in fractured granite. Journal of Rock Mechanics and Geotechnical Engineering, 12(4), 900–916. https://doi.org/10.1016/j.jrmge.2020.01.002
  111. Liu, W., Cheng, J., Yao, H., Zheng, L., Zhang, Q., Zhang, Z., & Yang, F. (2023). A micromechanical thermo-hydro-mechanical coupling model for fractured rocks based on multi-scale structures variations. International Journal of Rock Mechanics and Mining Sciences, 170. https://doi.org/10.1016/j.ijrmms.2023.105545
  112. Liu, Y., Long, X., & Liu, F. (2022). Tracer test and design optimization of doublet system of carbonate geothermal reservoirs. Geothermics, 105. https://doi.org/10.1016/j.geothermics.2022.102533
  113. Llanos, E. M., Castañeda-Herrera, C. A., Black, J. R., & Haese, R. R. (2022). Development of reactive-transport models simulating the formation of a silica gel barrier under CO2 storage conditions. International Journal of Greenhouse Gas Control, 119, 103739. https://doi.org/10.1016/J.IJGGC.2022.103739
  114. Ma, Y., Gan, Q., Zhang, Y., & Zhou, L. (2022). Numerical simulation of the heat production potential of Guide Basin in China considering the heterogeneity and anisotropy of the reservoir. Geothermics, 105. https://doi.org/10.1016/j.geothermics.2022.102508
  115. Maldonado-Cruz, E., & Pyrcz, M. J. (2022). Fast evaluation of pressure and saturation predictions with a deep learning surrogate flow model. Journal of Petroleum Science and Engineering, 212. https://doi.org/10.1016/j.petrol.2022.110244
  116. Marrakech Partnership for Global Climate Action. (2019). UNFCCC COP 25 Outcome Document Action Event: Energy Action Event Marrakech Partnership for Global Climate Action
  117. Mattax, C. C., & Dalton, R. L. (1990). Reservoir Simulation (Calvin C. M., Robert L. D., & Society of Petroleum Engineers (U.S.), Eds.). Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers. https://books.google.com.co/books?id=PzlQAQAAIAAJ
  118. McClure, M. W., & Horne, R. N. (2013). Discrete Fracture Network Modeling of Hydraulic Stimulation. Springer Cham
  119. McDonald, M. G., & Harbaugh, A. W. (1984). A modular three-dimensional finite-difference ground-water flow model. Open-File Report. https://doi.org/10.3133/ofr83875
  120. McLean, M. L., & Espinoza, D. N. (2023). Thermal destressing: Implications for short-circuiting in enhanced geothermal systems. Renewable Energy, 202, 736–755. https://doi.org/10.1016/j.renene.2022.11.102
  121. Mejía-Fragoso, J. C., Flórez, M. A., & Bernal-Olaya, R. (2024). Predicting the geothermal gradient in Colombia: A machine learning approach. Geothermics, 122. https://doi.org/10.1016/j.geothermics.2024.103074
  122. Mercer, J. W. J. (1973). Finite-Element Approach to the Modeling of Hydrothermal Systems
  123. Mills, R. T., Lu, C., Lichtner, P. C., & Hammond, G. E. (2007). Simulating subsurface flow and transport on ultrascale computers using PFLOTRAN. Journal of Physics: Conference Series, 78(1), 012051. https://doi.org/10.1088/1742-6596/78/1/012051
  124. Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
  125. Monsalve, M. L., Rodriguez, G. I., Mendez, R. A., & Bernal, N. F. (1998). Geology of the Well Nereidas 1, Nevado Del Ruiz Volcano, Colombia. Geothermal Resources Council Transactions, 2–6
  126. Morales, E., Veroslavsky, G., Manganelli, A., Marmisolle, J., Pedro, A., Samaniego, L., Plenc, F., Umpiérrez, R., Ferreiro, M., & Morales, M. (2021). Potential of geothermal energy in the onshore sedimentary basins of Uruguay. Geothermics, 95. https://doi.org/10.1016/j.geothermics.2021.102165
  127. Moreno, D., Lopez-Sanchez, J., Blessent, D., & Raymond, J. (2018). Fault characterization and heat-transfer modeling to the Northwest of Nevado del Ruiz Volcano. Journal of South American Earth Sciences, 88, 50–63. https://doi.org/10.1016/j.jsames.2018.08.008
  128. Mungai, E. M., Ndiritu, S. W., & da Silva, I. (2022). Unlocking climate finance potential and policy barriers—A case of renewable energy and energy efficiency in Sub-Saharan Africa. Resources, Environment and Sustainability, 7, 100043. https://doi.org/10.1016/J.RESENV.2021.100043
  129. Nakanishi, S., Kawano, Y., Todaka, N., Akasaka, C., Yoshida, M., & Iwai, N. (1995). A Reservoir Simulation of the Oguni Field, Japan, Using MINC Type Fracture Model. World Geothermal Congress, 1721–1726
  130. Nitao, J. J. (1993). User’s manual for US1 Module of the NUFT code, Version 1.0 (UCRL-ID 113520). Lawrence Berkeley National Laboratory
  131. Niu, Q., Ma, K., Wang, W., Pan, J., Wang, Q., Du, Z., Wang, Z., Yuan, W., Zheng, Y., Shangguan, S., Qi, X., Pan, M., & Ji, Z. (2023). Multifactor analysis of heat extraction performance of coaxial heat exchanger applied to hot dry rock resources exploration: A case study in Matouying uplift, Tangshan, China. Energy, 282. https://doi.org/10.1016/j.energy.2023.128277
  132. Nugraha, R. P., O’sullivan, J., O’sullivan, M. J., & Abdurachman, F. H. (2022). Geothermal Modelling: Industry Standard Practices. Proceedings 47th Workshop on Geothermal Reservoir Engineering, 1–12
  133. Okoroafor, E., Smith, C., Ochie, K., Nwosu, C., Gudmundsdottir, H., & Aljubran, M. (2022). Machine learning in subsurface geothermal energy: Two decades in review. Geothermics, 102. https://doi.org/10.1016/j.geothermics.2022.102401
  134. Oquendo, C., Smith, A., & Asuaje, M. (2024). Approach to Geothermal Energy. A Link Additional for the Energy Transition in Venezuela. Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology. https://doi.org/10.18687/LACCEI2024.1.1.849
  135. O’Sullivan, M. J. (1985). Geothermal reservoir simulation. International Journal of Energy Research, 9(3), 319–332. https://doi.org/10.1002/er.4440090309
  136. O’Sullivan, M.J., & J.P. O’Sullivan. (2024). Chapter 7 - Reservoir Modeling and Simulation for Geothermal Resource Characterization and Evaluation. The Future of Geothermal Energy - Developments and Innovation - Second Edition; Edited by: Ronald DiPippo, Luis C.A. Gutiérrez-Negrín, Andrew Chiasson
  137. O’Sullivan, M. J., Pruess, K., & Lippmann, M. J. (2000). Geothermal reservoir simulation: the state-of-practice and emerging trends. Proceedings World Geothermal Congress, 4065–4070
  138. O’Sullivan, M. J., Pruess, K., & Lippmann, M. J. (2001). State of the art of geothermal reservoir simulation. Geothermics, 30(4), 395–429. https://doi.org/10.1016/S0375-6505(01)00005-0
  139. Owen, S. J., Jones, N. L., & Holland, J. P. (1996). A comprehensive modeling environment for the simulation of groundwater flow and transport. Engineering with Computers, 12(3), 235–242. https://doi.org/10.1007/BF01198737
  140. Pandey, S. N., Vishal, V., & Chaudhuri, A. (2018). Geothermal reservoir modeling in a coupled thermo-hydro-mechanical-chemical approach: A review. Earth-Science Reviews, 185, 1157–1169. https://doi.org/10.1016/J.EARSCIREV.2018.09.004
  141. Podgorney, R. K., Huang, H., Lu, C., Gaston, D., Permann, C., Guo, L., & Andrs, D. (2011). FALCON: A Physics Based, Massively Parallel, Fully-Coupled, Finite Element Model for Simultaneously Solving Multiphase 9 Fluid Flow, Heat Transport, and Rock Deformation for Geothermal Reservoir Simulation
  142. Pratama, H. B., Sutopo, Widiatmo, J. S., & Ashat, A. (2021). Numerical Investigative Modeling of Changes Within the Patuha Geothermal Reservoir and Its Production Sustainability Under Two Different Conversion Technologies. Natural Resources Research, 30(4), 2969–2987. https://doi.org/10.1007/s11053-020-09748-7
  143. Pritchett, J. W. (2008). Guide to the SPFRAC simulator. Science Applications International Corp., Report to DOE
  144. Pritchett, J. W. (1995a). NIGHTS: A single-phase geothermal reservoir simulator. World Geothermal Congress
  145. Pritchett, J. W. (1995b). STAR: Geothermal reservoir simulation. World Geothermal Congress
  146. Project Management Global. (2024). Japan Invests $43 Million in Ecuador’s Chachimbiro Geothermal Project. Project Management Global. https://news.pm-global.co.uk/2024/10/japan-invests-43-million-in-ecuadors-chachimbiro-geothermal-project/
  147. Pruess, K. (1988). SHAFT, MULKOM, TOUGH: A Set of Numerical Simulators for Multiphase Fluid and Heat Flow
  148. Pruess, K., Oldenburg, C., & Moridis, G. (1999). TOUGH2 User’s Guide, Version 2
  149. Pruess, K., & Schroeder, R. C. (1980). SHAFT79, User’s Manual
  150. Pugliese, R., Regondi, S., & Marini, R. (2021). Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Science and Management, 4, 19–29. https://doi.org/10.1016/J.DSM.2021.12.002
  151. Qarinur, M., Ogata, S., Kinoshita, N., & Yasuhara, H. (2020). Predictions of rock temperature evolution at the Lahendong geothermal field by coupled numerical model with discrete fracture model scheme. Energies, 13(12). https://doi.org/10.3390/en13123282
  152. Qi, Y. (2012). Random Forest for Bioinformatics. In: Ensemble machine learning. Berlin: Springer; 2012. p. 307–23
  153. Rajabi, M. M., & Chen, M. (2022). Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects. In Advances in Geo-Energy Research, 6(6), 451–453). https://doi.org/10.46690/ager.2022.06.01
  154. Rasmussen, A. F., Sandve, T. H., Bao, K., Lauser, A., Hove, J., Skaflestad, B., Klöfkorn, R., Blatt, M., Rustad, A. B., Sævareid, O., Lie, K. A., & Thune, A. (2021). The Open Porous Media Flow reservoir simulator. Computers & Mathematics with Applications, 81, 159–185. https://doi.org/10.1016/J.CAMWA.2020.05.014
  155. Rinaldi, A. P., Rutqvist, J., Luu, K., Blanco-Martín, L., Hu, M., Sentís, M. L., Eberle, L., & Kaestli, P. (2022). TOUGH3-FLAC3D: a modeling approach for parallel computing of fluid flow and geomechanics. Computational Geosciences, 26(6), 1563–1580. https://doi.org/10.1007/s10596-022-10176-0
  156. Ritz, V. A., Mizrahi, L., Clasen Repollés, V., Rinaldi, A. P., Hjörleifsdóttir, V., & Wiemer, S. (2024). Pseudo-Prospective Forecasting of Induced and Natural Seismicity in the Hengill Geothermal Field. Journal of Geophysical Research: Solid Earth, 129(3). https://doi.org/10.1029/2023JB028402
  157. Rivera Diaz, A., Kaya, E., & Zarrouk, S. J. (2015). Reinjection in Geothermal Fields: A Worldwide Review Update. Proceedings World Geothermal Congress, 19–25
  158. Santamaría-Bonfil, G., Santoyo, E., Díaz-González, L., & Arroyo-Figueroa, G. (2022). Equivalent imputation methodology for handling missing data in compositional geochemical databases of geothermal fluids. Geothermics, 104. https://doi.org/10.1016/j.geothermics.2022.102440
  159. Sanyal, S. K., Butler, S. J., Swenson, D., & Hardeman, B. (2000). Review of the state-of-the-art of numerical simulation of Enhanced Geothermal Systems. Proceedings World Geothermal Congress
  160. Schiavone, R., de Natale, G., Borgia, A., Troise, C., Moretti, R., & Somma, R. (2020). Seismogenic potential of withdrawal-reinjection cycles: Numerical modelling and implication on induced seismicity. Geothermics, 85. https://doi.org/10.1016/j.geothermics.2019.101770
  161. Scott, S. W., O’Sullivan, J. P., Maclaren, O. J., Nicholson, R., Covell, C., Newson, J., & Guðjónsdóttir, M. S. (2022). Bayesian Calibration of a Natural State Geothermal Reservoir Model, Krafla, North Iceland. Water Resources Research, 58(2). https://doi.org/10.1029/2021WR031254
  162. Semmari, H., Bouaicha, F., Aberkane, S., Filali, A., Blessent, D., & Badache, M. (2024). Geological context and thermo-economic study of an indirect heat ORC geothermal power plant for the northeast region of Algeria. Energy, 290. https://doi.org/10.1016/j.energy.2024.130323
  163. Seyedrahimi-Niaraq, M., Doulati Ardejani, F., Noorollahi, Y., Jalili Nasrabadi, S., & Hekmatnejad, A. (2021). An unsaturated three-dimensional model of fluid flow and heat transfer in NW Sabalan geothermal reservoir. Geothermics, 89. https://doi.org/10.1016/j.geothermics.2020.101966
  164. Seyedrahimi-Niaraq, M., Mohammadzadeh Bina, S., & Itoi, R. (2021). Numerical and thermodynamic modeling for estimating production capacity of NW Sabalan geothermal field, Iran. Geothermics, 90. https://doi.org/10.1016/j.geothermics.2020.101981
  165. Siddique, A., Shahzad, A., Lawler, J., Mahmoud, K. A., Lee, D. S., Ali, N., Bilal, M., & Rasool, K. (2021). Unprecedented environmental and energy impacts and challenges of COVID-19 pandemic. Environmental Research, 193, 110443. https://doi.org/10.1016/J.ENVRES.2020.110443
  166. Smith, C. M., Faulds, J. E., Brown, S., Coolbaugh, M., DeAngelo, J., Glen, J. M., Burns, E. R., Siler, D. L., Treitel, S., Mlawsky, E., Fehler, M., Gu, C., & Ayling, B. F. (2023). Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis. Geothermics, 111. https://doi.org/10.1016/j.geothermics.2023.102693
  167. Stacey, R. W., & Williams, M. J. (2017). Validation of ECLIPSE Reservoir Simulator for Geothermal Problems. Geothermal Resources Council Transactions
  168. Stanford Geothermal Program. (1980). Proceedings, Special Panel on Geothermal Model Intercomparison Study. Stanford Geothermal Program report SGP-TR-42
  169. Stober, I., Jägle, M., & Kohl, T. (2023). Optimizing scenarios of a deep geothermal aquifer storage in the southern Upper Rhine Graben. Geothermal Energy, 11(1). https://doi.org/10.1186/s40517-023-00275-1
  170. Suzuki, A., Fukui, K. I., Onodera, S., Ishizaki, J., & Hashida, T. (2022). Data-Driven Geothermal Reservoir Modeling: Estimating Permeability Distributions by Machine Learning. Geosciences, 12(3). https://doi.org/10.3390/geosciences12030130
  171. Swenson, D., Duteau, R., & Sprecker, T. (1995). Modeling flow in a jointed geothermal reservoir. Proceedings of the World Geothermal Congress, 2553–2558
  172. Szanyi, J., Rybach, L., & Abdulhaq, H. A. (2023). Geothermal Energy and Its Potential for Critical Metal Extraction—A Review. Energies, 16(20). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en16207168
  173. Taillefer, A., Truche, L., Audin, L., Donzé, F. V., Tisserand, D., Denti, S., Manrique Llerena, N., Masías Alvarez, P. J., Braucher, R., Zerathe, S., Monnin, C., Dutoit, H., Taipe Maquerhua, E., & Apaza Choquehuayta, F. E. (2024). Characterization of Southern Peru Hydrothermal Systems: New Perspectives for Geothermal Exploration Along the Andean Forearc. Geochemistry, Geophysics, Geosystems, 25(5). https://doi.org/10.1029/2023GC011344
  174. Tang, F., & Ishwaran, H. (2017). Random forest missing data algorithms. Statistical Analysis and Data Mining, 10(6), 363–377. https://doi.org/10.1002/sam.11348
  175. Tescione, I., Todesco, M., & Giordano, G. (2021). Geothermal fluid circulation in a caldera setting: The Torre Alfina medium enthalpy system (Italy). Geothermics, 89. https://doi.org/10.1016/j.geothermics.2020.101947
  176. Todd, F., McDermott, C., Harris, A. F., Bond, A., & Gilfillan, S. (2024). Modelling physical controls on mine water heat storage systems. Geoenergy, 2(1). https://doi.org/10.1144/geoenergy2023-029
  177. Tonkin, R. A., O’Sullivan, M. J., & O’Sullivan, J. P. (2021). A review of mathematical models for geothermal wellbore simulation. Geothermics, 97, 102255. https://doi.org/10.1016/j.geothermics.2021.102255
  178. Topór, T., Słota-Valim, M., & Kudrewicz, R. (2023). Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools. Energies, 16(13). https://doi.org/10.3390/en16135211
  179. Toronyi, R. M. (1974). Two-Phase, Two-Dimensional Simulation of a Geothermal Reservoir and the Wellbore System
  180. Vargas-Payera, S. (2024). Heat in the news: Geothermal energy in Chilean newspaper coverage. Renewable Energy, 237. https://doi.org/10.1016/j.renene.2024.121509
  181. Vélez, M. I., Blessent, D., Córdoba, S., López-Sánchez, J., & Raymond, J. (2018). Geothermal potential assessment of the Nevado del Ruiz volcano based on rock thermal conductivity measurements and numerical modeling of heat transfer. Journal of South American Earth Sciences, 81, 153–164. https://doi.org/10.1016/j.jsames.2017.11.011
  182. Villarroel Camacho, D. G. (2014, March 23). Geothermal development in Bolivia. Short Course VI on Utilization of Low- and Medium-Enthalpy Geothermal Resources and Financial Aspects of Utilization”, Organized by UNU-GTP and LaGeo
  183. Vinsome, P. K. W. (1991). TETRAD User’s Manual. Dyad Engineering
  184. Voss, C. I. (1984). A finite-element simulation model for saturated-unsaturated, fluid-density-dependent ground-water flow with energy transport or chemically- reactive single-species solute transport. In Water-Resources Investigations Report. U.S. Geological Survey. https://doi.org/10.3133/wri844369
  185. Wang, C., Winterfeld, P., Johnston, B., & Wu, Y.-S. (2020). An embedded 3D fracture modeling approach for simulating fracture-dominated fluid flow and heat transfer in geothermal reservoirs. Geothermics, 86. https://doi.org/10.1016/j.geothermics.2020.101831
  186. Wang, H., Wang, L., & Cheng, Z. (2022). Influence Factors on EGS Geothermal Reservoir Extraction Performance. Geofluids, 2022. https://doi.org/10.1155/2022/5174456
  187. Wang, L., Liang, X., Shi, X., Han, J., Chen, Y., & Zhang, W. (2023). Heat Transfer Analysis of Enhanced Geothermal System Based on Heat-Fluid-Structure Coupling Model. Geofluids, 2023. https://doi.org/10.1155/2023/8840352
  188. Wang, Y., Khait, M., Voskov, D., Bruhn, D., & Saeid, S. (2019). Benchmark test and sensitivity analysis for Geothermal Applications in the Netherlands. Proceedings 44th Workshop on Geothermal Reservoir Engineering, 1–11. https://www.researchgate.net/publication/333449083
  189. Wang, Y., Liu, Y., Bian, K., Zhang, H., Wang, X., Zhang, H., Wang, W., & Qin, S. (2021). Influence of low temperature tail water reinjection on seepage and heat transfer of carbonate reservoirs. Energy Exploration and Exploitation, 39(6), 2062–2079. https://doi.org/10.1177/01445987211020416
  190. White, M. D., Oostrom, M., & Lenhard, R. J. (1995). Modeling fluid flow and transport in variably saturated porous media with the STOMP simulator. 1. Nonvolatile three-phase model description. Advances in Water Resources, 18(6), 353–364. https://doi.org/10.1016/0309-1708(95)00018-E
  191. Whiting, R. L., & Ramey Jr., H. J. (1969). Application of Material and Energy Balances to Geothermal Steam Production. Journal of Petroleum Technology, 21(07), 893–900. https://doi.org/10.2118/1949-PA
  192. Xing, H., Zhang, J., Liu, Y., & Mulhaus, H. (2009). Enhanced Geothermal Reservoir Simulation. Australian Geothermal Energy Conference, 1
  193. Xu, C., Dong, S., Wang, H., Wang, Z., Xiong, F., Jiang, Q., Zeng, L., Faulkner, L., Tian, Z. F., & Dowd, P. (2021). Modelling of Coupled Hydro-Thermo-Chemical Fluid Flow through Rock Fracture Networks and Its Applications. Geosciences, 11(4). https://doi.org/10.3390/geosciences11040153
  194. Xu, T., Hu, Z., Feng, B., Feng, G., Li, F., & Jiang, Z. (2020). Numerical evaluation of building heating potential from a co-axial closed-loop geothermal system using wellbore–reservoir coupling numerical model. Energy Exploration and Exploitation, 38(3), 733–754. https://doi.org/10.1177/0144598719889799
  195. Yamamoto, T., Fujimitsu, Y., & Ohnishi, H. (1995). Hot dry rock reservoir 3D simulation of the Ogachi site. https://www.osti.gov/biblio/175639
  196. Yan, B., Li, C., Tariq, Z., & Zhang, K. (2023). Estimation of heterogeneous permeability using pressure derivative data through an inversion neural network inspired by the Fast Marching Method. Geoenergy Science and Engineering, 228. https://doi.org/10.1016/j.geoen.2023.211982
  197. Yilmaz Turali, E., & Simsek, S. (2023). A three-dimensional numerical model of Yerköy (Yozgat) hydrogeothermal system, Central Anatolia, Türkiye. Journal of African Earth Sciences, 198. https://doi.org/10.1016/j.jafrearsci.2022.104815
  198. Yu, X., Yan, X., Wang, C., Wang, S., & Wu, Y.-S. (2023). Thermal-hydraulic-mechanical analysis of enhanced geothermal reservoirs with hybrid fracture patterns using a combined XFEM and EDFM-MINC model. Geoenergy Science and Engineering, 228. https://doi.org/10.1016/j.geoen.2023.211984
  199. Zareidarmiyan, A., Salarirad, H., Vilarrasa, V., Kim, K.-I., Lee, J., & Min, K.-B. (2020). Comparison of numerical codes for coupled thermo-hydro-mechanical simulations of fractured media. Journal of Rock Mechanics and Geotechnical Engineering, 12(4), 850–865. https://doi.org/10.1016/j.jrmge.2019.12.016
  200. Zeng, Y., Sun, F., & Zhai, H. (2021). Numerical study on the influence of well layout on electricity generation performance of enhanced geothermal systems. Processes, 9(8). https://doi.org/10.3390/pr9081474
  201. Zhang, H., & Wu, W. (2023). DBN with IQPSO Algorithm for Permeability Prediction: A Case Study of the Lizhai Geothermal Field, Zhangye Basin (Northern China). Natural Resources Research, 32(5), 1941–1957. https://doi.org/10.1007/s11053-023-10240-1
  202. Zhu, G., & Turchi, C. (2017). Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant. Journal of Solar Energy Engineering, Transactions of the ASME, 139(3). https://doi.org/10.1115/1.4035518
  203. Zhu, J., Cui, Z., Feng, B., Ren, H., & Liu, X. (2023). Numerical Simulation of Geothermal Reservoir Reconstruction and Heat Extraction System Productivity Evaluation. Energies, 16(1). https://doi.org/10.3390/en16010127
  204. Zhu, X., Luo, Y., & Liu, W. (2020). On the rock-breaking mechanism of plasma channel drilling technology. Journal of Petroleum Science and Engineering, 194. https://doi.org/10.1016/j.petrol.2020.107356
  205. Zou, C., Zhao, L., Xu, M., Chen, Y., & Geng, J. (2021). Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest. Journal of Geophysical Research: Solid Earth, 126(7). https://doi.org/10.1029/2021JB021826
  206. Zuo, G., Wang, H., Lan, L., Zhang, Y., Zuo, Y., Yang, L., Wang, C., Pang, X., Song, X., & Yang, M. (2023). Present geothermal field of the Santos Basin, Brazil. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-39702-5
  207. Zyvoloski, G. A., Dash, Z. V., & Kelkar, S. (1988). FEHM: Finite Element Heat and Mass Transfer Code. Technical Report. Los Alamos National Lab. (LANL), Los Alamos, NM (United States) https://doi.org/10.2172/5495517

Last update:

No citation recorded.

Last update: 2025-07-11 18:46:09

No citation recorded.