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Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

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Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.

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Predicting Geothermal Favorability in the Western United States by Using Machine Learning - Addressing Challenges and Developing Solutions

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Citation Formats

TY  - DATA
AB  - Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.
AU  - Mordensky, Stanley P.
A2  - Lipor, John J.
A3  - DeAngelo, Jacob
A4  - Burns, Erick R.
A5  - Lindsey, Cary R.
DB  - Open Energy Data Initiative (OEDI)
DP  - Open EI | National Renewable Energy Laboratory
DO  - 
KW  - geothermal
KW  - energy
KW  - PFA
KW  - hydrothermal
KW  - energy assessment
KW  - resource assessment
KW  - retrospective
KW  - machine learning
KW  - geoscience
KW  - western US
KW  - data-driven
KW  - bias reduction
KW  - favorability
KW  - mapping
KW  - EGS
KW  - low temp
KW  - processed data
KW  - resource
KW  - characterization
LA  - English
DA  - 2023/02/07
PY  - 2023
PB  - United States Geological Survey
T1  - Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments
UR  - https://data.openei.org/submissions/7589
ER  -

Export Citation to RIS

Mordensky, Stanley P., et al. Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments. United States Geological Survey, 7 February, 2023, GDR. https://gdr.openei.org/submissions/1498.

Mordensky, S., Lipor, J., DeAngelo, J., Burns, E., & Lindsey, C. (2023). Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments. [Data set]. GDR. United States Geological Survey. https://gdr.openei.org/submissions/1498

Mordensky, Stanley P., John J. Lipor, Jacob DeAngelo, Erick R. Burns, and Cary R. Lindsey. Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments. United States Geological Survey, February, 7, 2023.  Distributed by GDR. https://gdr.openei.org/submissions/1498

@misc{OEDI_Dataset_7589,
title = {Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments},
author = {Mordensky, Stanley P. and Lipor, John J. and DeAngelo, Jacob and Burns, Erick R. and Lindsey, Cary R.},
abstractNote = {Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.



This study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. This submission includes a list of papers, data releases, and presentations produced as part of this work.},
url = {https://gdr.openei.org/submissions/1498},
year = {2023},
howpublished = {GDR, United States Geological Survey, https://gdr.openei.org/submissions/1498},
note = {Accessed: 2025-05-28}
}

Details

Data from Feb 7, 2023

Last updated Jul 25, 2023

Submitted May 1, 2023