geoPFA is an open-source Python library for conducting Play Fairway Analysis (PFA) in 2D and 3D, designed to reduce exploration risk by integrating surface and subsurface considerations into a single, transparent workflow. Built around NREL’s Geothermal PFA Best Practices and aligned with FAIR software principles, geoPFA provides modular, extensible tools for cleaning, processing, weighting, and combining diverse datasets into quantitative favorability maps. These datasets can include geological, geophysical, geochemical, and thermo-hydro-mechanical-chemical simulation results, as well as surface-level factors such as energy demand, transmission access, and natural hazard exposure.
The framework is fully customizable, enabling users to define criteria, components, and indicators for any geothermal resource type—from low-temperature and conventional hydrothermal to superhot systems—and to extend the methodology to other subsurface applications if desired. geoPFA supports multiple data processing approaches, including interpolation, density mapping, distance-based scoring, extrapolation, and thermal modeling, while allowing integration of expert-derived weightings or analytical hierarchy methods.
geoPFA has been successfully demonstrated in diverse contexts: a 3D PFA for the Nesjavellir field in Iceland, where results aligned with known subsurface conditions and guided scenario-based development strategies (Taverna et al., 2025); and 2D PFAs of the Denver Basin and Alaska for lower-enthalpy geothermal with greater emphasis on surface constraints (Davalos-Elizondo et al., 2024; in work). By making advanced exploration workflows reproducible, transparent, and openly accessible, geoPFA enables research teams, developers, and agencies to make better-informed decisions through reducing time required for developing workflows, allowing more time to be spent on feature engineering and interpretation of results.
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