Metadata-Version: 2.4
Name: geoPFA
Version: 0.0.11
Summary: Geothermal Play Fairway Analysis
Author-email: Nicole Taverna <Nicole.Taverna@nrel.gov>, Scott Mello <Scott.Mello@nrel.gov>, Guilherme Castelao <gpimenta@nrel.gov>, Dylan Hettinger <Dylan.Hettinger@nrel.gov>, Karthik Menon <Karthik.Menon@nrel.gov>, Emily Holt <Emily.Holt@nrel.gov>
Maintainer-email: Nicole Taverna <Nicole.Taverna@nrel.gov>
License-Expression: BSD-3-Clause
Project-URL: homepage, https://github.com/NREL/geoPFA
Project-URL: documentation, https://github.com/NREL/geoPFA
Project-URL: repository, https://github.com/NREL/geoPFA
Keywords: geothermal,PFA
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
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# Geothermal PFA

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.

# NOTICE

Copyright © 2023 Alliance for Sustainable Energy, LLC

These data were produced by the Alliance for Sustainable Energy, LLC
(Contractor) under Contract No. DE-AC36-08GO28308 with the U.S. Department of
Energy (DOE). During the period of commercialization or such other time period
specified by the DOE, the Government is granted for itself and others acting on
its behalf a nonexclusive, paid-up, irrevocable worldwide license in this data
to reproduce, prepare derivative works, and perform publicly and display
publicly, by or on behalf of the Government. Subsequent to that period the
Government is granted for itself and others acting on its behalf a
nonexclusive, paid-up, irrevocable worldwide license in this data to reproduce,
prepare derivative works, distribute copies to the public, perform publicly and
display publicly, and to permit others to do so. The specific term of the
license can be identified by inquiry made to the Contractor or DOE. NEITHER
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ANY OF THEIR EMPLOYEES, MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY
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USE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS.
