Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration
نویسندگان
چکیده
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 play fairway analysis (PFA), a method used oil gas exploration, progressed include machine learning (ML) predicting geothermal drill site favorability. This study introduces novel approach that extends ML PFA predictions with uncertainty characterization. Four algorithms—logistic regression, decision tree, gradient-boosted forest, neural network—are evaluate enthalpy resource potential conventional EGS prospecting. Normalized Shannon entropy is calculated assess three spatially variable sources analysis: model representation, parameterization, feature interpolation. When applied southwest New Mexico, this reveals consistent trends embedded high-dimensional set detected by multiple algorithms. highlights spatial regions where models disagree, highly parameterized are poorly constrained, show sensitivity errors important features. Rapid insights from enable teams optimize allocation decisions limited financial human resources during early stages campaign.
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ژورنال
عنوان ژورنال: Energies
سال: 2022
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en15051929