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

Maruti K. Mudunuru (), Bulbul Ahmmed, Elisabeth Rau, Velimir V. Vesselinov and Satish Karra
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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

Energies, 2023, vol. 16, issue 7, 1-11

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.

Keywords: geothermal exploration; geothermal resource signatures; machine learning; play fairway analysis; Tularosa Basin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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