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The Geothermal Artificial Intelligence for geothermal exploration

Author

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  • Moraga, J.
  • Duzgun, H.S.
  • Cavur, M.
  • Soydan, H.

Abstract

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). We processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92–95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal/non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72–76% accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:192:y:2022:i:c:p:134-149
    DOI: 10.1016/j.renene.2022.04.113
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    1. Sadeghi, Behnam & Khalajmasoumi, Masoumeh, 2015. "A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 818-831.
    2. Barry Boots & Michael Tiefelsdorf, 2000. "Global and local spatial autocorrelation in bounded regular tessellations," Journal of Geographical Systems, Springer, vol. 2(4), pages 319-348, December.
    3. 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).
    4. Grant Buster & Paul Siratovich & Nicole Taverna & Michael Rossol & Jon Weers & Andrea Blair & Jay Huggins & Christine Siega & Warren Mannington & Alex Urgel & Jonathan Cen & Jaime Quinao & Robbie Watt, 2021. "A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)," Energies, MDPI, vol. 14(20), pages 1-20, October.
    5. Galina Alova & Philipp A. Trotter & Alex Money, 2021. "A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success," Nature Energy, Nature, vol. 6(2), pages 158-166, February.
    6. Abdel Zaher, Mohamed & Saibi, Hakim & Mansour, Khamis & Khalil, Ahmed & Soliman, Mamdouh, 2018. "Geothermal exploration using airborne gravity and magnetic data at Siwa Oasis, Western Desert, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3824-3832.
    7. Clauser, Christoph & Ewert, Markus, 2018. "The renewables cost challenge: Levelized cost of geothermal electric energy compared to other sources of primary energy – Review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3683-3693.
    8. Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.
    9. Gaucher, Emmanuel & Schoenball, Martin & Heidbach, Oliver & Zang, Arno & Fokker, Peter A. & van Wees, Jan-Diederik & Kohl, Thomas, 2015. "Induced seismicity in geothermal reservoirs: A review of forecasting approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1473-1490.
    10. Dmitry Duplyakin & Koenraad F. Beckers & Drew L. Siler & Michael J. Martin & Henry E. Johnston, 2022. "Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning," Energies, MDPI, vol. 15(3), pages 1-20, January.
    11. Xia, Liangyu & Zhang, Yabo, 2019. "An overview of world geothermal power generation and a case study on China—The resource and market perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 411-423.
    12. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
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    1. Faisal Alqahtani & Muhsan Ehsan & Murad Abdulfarraj & Essam Aboud & Zohaib Naseer & Nabil N. El-Masry & Mohamed F. Abdelwahed, 2023. "Machine Learning Techniques in Predicting Bottom Hole Temperature and Remote Sensing for Assessment of Geothermal Potential in the Kingdom of Saudi Arabia," Sustainability, MDPI, vol. 15(17), pages 1-36, August.
    2. Tomasz Topór & Małgorzata Słota-Valim & Rafał Kudrewicz, 2023. "Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools," Energies, MDPI, vol. 16(13), pages 1-19, July.

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