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Geothermal resource and reserve assessment methodology: Overview, analysis and future directions

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  • Ciriaco, Anthony E.
  • Zarrouk, Sadiq J.
  • Zakeri, Golbon

Abstract

Resource assessment and reserve estimation play a crucial role in the decision-making, financing, development, and operation of geothermal projects. The present study critically examines all existing resource assessment methodology and practices when quantifying power potential of geothermal fields. The potential generating capacity of geothermal projects at the early stage of development, where there is limited information about the resource, is typically estimated using the volumetric method. Sustainable operation and management of existing geothermal fields, on the other hand, rely on developing and updating a calibrated numerical reservoir model. To-date, the volumetric method and reservoir simulation remain the most appropriate tools to use for geothermal resource assessment. The former method is the recommended approach for projects that are still at the early stage of development, while the latter technique is for predicting sustainable production capacity after exploration drilling. However, building a numerical model for a project at the early due diligence stage is also useful and can complement the volumetric method. Most studies of resource assessment methodologies highlight the difficulty of obtaining accurate, predictable generating output potential. Quantification of uncertainty in predictable output is carried out using the Monte Carlo method. This review demonstrates that the probabilistic assessment using Experimental Design (ED) and Response Surface Methodology (RSM) is a more promising technique that can be easier and quicker to implement.

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  • Ciriaco, Anthony E. & Zarrouk, Sadiq J. & Zakeri, Golbon, 2020. "Geothermal resource and reserve assessment methodology: Overview, analysis and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
  • Handle: RePEc:eee:rensus:v:119:y:2020:i:c:s1364032119307233
    DOI: 10.1016/j.rser.2019.109515
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    3. Zhang, Bo & Gu, Kai & Shi, Bin & Liu, Chun & Bayer, Peter & Wei, Guangqing & Gong, Xülong & Yang, Lei, 2020. "Actively heated fiber optics based thermal response test: A field demonstration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Maximilian Frick & Stefan Kranz & Ben Norden & David Bruhn & Sven Fuchs, 2022. "Geothermal Resources and ATES Potential of Mesozoic Reservoirs in the North German Basin," Energies, MDPI, vol. 15(6), pages 1-26, March.
    5. Duggal, R. & Rayudu, R. & Hinkley, J. & Burnell, J. & Wieland, C. & Keim, M., 2022. "A comprehensive review of energy extraction from low-temperature geothermal resources in hydrocarbon fields," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Chen, Heng & Wang, Yihan & Li, Jiarui & Xu, Gang & Lei, Jing & Liu, Tong, 2022. "Thermodynamic analysis and economic assessment of an improved geothermal power system integrated with a biomass-fired cogeneration plant," Energy, Elsevier, vol. 240(C).
    7. Riccardo Basosi & Roberto Bonciani & Dario Frosali & Giampaolo Manfrida & Maria Laura Parisi & Franco Sansone, 2020. "Life Cycle Analysis of a Geothermal Power Plant: Comparison of the Environmental Performance with Other Renewable Energy Systems," Sustainability, MDPI, vol. 12(7), pages 1-29, April.
    8. Tut Haklıdır, Füsun S., 2020. "The importance of long-term well management in geothermal power systems using fuzzy control: A Western Anatolia (Turkey) case study," Energy, Elsevier, vol. 213(C).
    9. Moraga, J. & Duzgun, H.S. & Cavur, M. & Soydan, H., 2022. "The Geothermal Artificial Intelligence for geothermal exploration," Renewable Energy, Elsevier, vol. 192(C), pages 134-149.

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