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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
0 Stars
Publicly accessible

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
11 Resources
0 Stars
Publicly accessible

Brady's Geothermal Field March 2016 Vibroseis SEG-Y Files and UTM Locations

PoroTomo March 2016 Updated vibroseis source locations with UTM locations. Supersedes gdr.openei.org/submissions/824. Updated vibroseis source location data for Stages 1-4, PoroTomo March 2016. This revision includes source point locations in UTM format (meters) for all four Stage...
Feigl, K. University of Wisconsin
Mar 31, 2016
10 Resources
0 Stars
Publicly accessible

Analysis of Existing Data from a Distributed Acoustic Sensing Experiment at Garner Valley, California

In September 2013, an experiment using Distributed Acoustic Sensing (DAS) was conducted at Garner Valley, a test site of the University of California Santa Barbara (Lancelle et al., 2014). This submission includes noise cross-correlation functions (NCF) . Each file includes a NCF ...
Zeng, X. University of Wisconsin
Mar 26, 2015
37 Resources
0 Stars
Publicly accessible