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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
0 Stars
Publicly accessible

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
0 Stars
Publicly accessible

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
0 Resources
0 Stars
In curation

Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and reside...
Ong, S. and Clark, N. National Renewable Energy Laboratory
Nov 25, 2014
22 Resources
0 Stars
Curated