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National Renewable Energy Laboratory×

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
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POROTOMO Subtask 6.2 Deploy and Operate DAS DTS

Metadata for the surface Distributed Acoustic (DAS) array deployed the the POROTOMO's Natural Laboratory in Brady Hot Spring, Nevada during the March 2016 testing.
Fratta, D. National Renewable Energy Laboratory
Jun 30, 2016
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