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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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    References listed on IDEAS

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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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