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Keywords = play fairway analysis

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11 pages, 4658 KB  
Communication
Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico
by Maruti K. Mudunuru, Bulbul Ahmmed, Elisabeth Rau, Velimir V. Vesselinov and Satish Karra
Energies 2023, 16(7), 3098; https://doi.org/10.3390/en16073098 - 29 Mar 2023
Cited by 11 | Viewed by 4224
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)
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56 pages, 64533 KB  
Article
Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration
by R. Chadwick Holmes and Aimé Fournier
Energies 2022, 15(5), 1929; https://doi.org/10.3390/en15051929 - 7 Mar 2022
Cited by 10 | Viewed by 5365
Abstract
Geothermal exploration has traditionally relied on geological, geochemical, or geophysical surveys for evidence of adequate enthalpy, fluids, and permeability in the subsurface prior to drilling. The recent adoption of play fairway analysis (PFA), a method used in oil and gas exploration, has progressed [...] Read more.
Geothermal exploration has traditionally relied on geological, geochemical, or geophysical surveys for evidence of adequate enthalpy, fluids, and permeability in the subsurface prior to drilling. The recent adoption of play fairway analysis (PFA), a method used in oil and gas exploration, has progressed to include machine learning (ML) for predicting geothermal drill site favorability. This study introduces a novel approach that extends ML PFA predictions with uncertainty characterization. Four ML algorithms—logistic regression, a decision tree, a gradient-boosted forest, and a neural network—are used to evaluate the subsurface enthalpy resource potential for conventional or EGS prospecting. Normalized Shannon entropy is calculated to assess three spatially variable sources of uncertainty in the analysis: model representation, model parameterization, and feature interpolation. When applied to southwest New Mexico, this approach reveals consistent enthalpy trends embedded in a high-dimensional feature set and detected by multiple algorithms. The uncertainty analysis highlights spatial regions where ML models disagree, highly parameterized models are poorly constrained, and predictions show sensitivity to errors in important features. Rapid insights from this analysis enable exploration teams to optimize allocation decisions of limited financial and human resources during the early stages of a geothermal exploration campaign. Full article
(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)
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