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Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Author

Listed:
  • Maruti K. Mudunuru

    (Earth System Science Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA)

  • Bulbul Ahmmed

    (Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA)

  • Elisabeth Rau

    (Matador Resources Company, Dallas, TX 75240, USA)

  • Velimir V. Vesselinov

    (EnviTrace LLC, Santa Fe, NM 87501, USA)

  • Satish Karra

    (Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA)

Abstract

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k -means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

Suggested Citation

  • Maruti K. Mudunuru & Bulbul Ahmmed & Elisabeth Rau & Velimir V. Vesselinov & Satish Karra, 2023. "Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico," Energies, MDPI, vol. 16(7), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3098-:d:1110173
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. R. Chadwick Holmes & Aimé Fournier, 2022. "Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration," Energies, MDPI, vol. 15(5), pages 1-56, March.
    3. Ahmmed, Bulbul & Vesselinov, Velimir V., 2022. "Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA," Renewable Energy, Elsevier, vol. 197(C), pages 1034-1048.
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