Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration

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 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.

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

“…In this short paper, we provide a machine learning (ML)-enhanced data-driven concept that quantifies the important attributes for geothermal resource characterization. Specifically, the MLenhanced PFA novelty is that it quantifies the relative importance of each attribute [15,16]. Our ML analysis is based on an open-source framework called GeoThermalCloud https: //github.com/SmartTensors/GeoThermalCloud.jl (accessed on 21 March 2023), which simultaneously analyzes available attributes, finds geothermal prospects, and discovers critical parameters defining prospective locations [17,18].…”

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

“…This approach to improving geothermal development projects' success rate is called Play Fairway Analysis (PFA). The PFA concept borrowed from oil& gas industries allows us to identify potential locations of blind hydrothermal systems and quantify geothermal potential in the regions of interest Holmes and Fournier (2022). Traditional PFA was able to identify various regions of interest for geothermal exploration Faulds et al (2016).…”

Index, Volume 3, 2022

“…This approach to improving geothermal development projects' success rate is called play fairway analysis (PFA). The PFA concept borrowed from the oil and gas industries allows us to identify potential locations of blind hydrothermal systems and quantify geothermal potential in the regions of interest (Holmes and Fournier, 2022). Traditional PFA was able to identify various regions of interest for geothermal exploration (Faulds et al, 2016).…”