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

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. W...

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Bibliographic Details
Published in:Energies (Basel) 2023-04, Vol.16 (7), p.3098
Main Authors: Mudunuru, Maruti K., Ahmmed, Bulbul, Rau, Elisabeth, Vesselinov, Velimir V., Karra, Satish
Format: Article
Language:English
Subjects:
Analysis
Basins
Basins (Geology)
Clustering
Decomposition
Electric power demand
Electricity
Equipment and supplies
Exploration
Federal agencies
Geological surveys
Geothermal energy
geothermal exploration
Geothermal power
geothermal resource signatures
Geothermal resources
Green technology
Heat
Learning algorithms
Machine learning
Mountains
New Mexico
play fairway analysis
Renewable resources
Resource development
Resource exploration
Teaching
Tularosa Basin
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16073098