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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
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Understanding Constraints on Geothermal Sustainability Through Reservoir Characterization at Brady Geothermal Field, Nevada
The vast supply of geothermal energy stored throughout the Earth and the exceedingly long time required to dissipate that energy makes the world's geothermal energy supply nearly limitless. As such, this resource holds the potential to provide a large supply of the world's energy ...
Patterson, J. University of Wisconsin
Jul 11, 2018
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Brady's Geothermal Field DAS Vibroseis Data
The submitted data correspond to the monitored vibrations caused by a vibroseis seismically exciting the ground in the vertical direction and captured by the DAS horizontal and vertical arrays during the PoroTomo Experiment. The data also include a file with the acceleration recor...
Feigl, K. University of Wisconsin
Mar 25, 2016
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11 Resources
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Publicly accessible