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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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Triggered MEQ Events on LBNL Permanent Seismic Array, Brady's EGS, March 2016

List of triggered events recorded on LBNL's permanent EGS seismic array at Brady's geothermal field. This submission also includes links to the NCEDC EGS Earthquake Catalog Search page and to the metadata for the seismic array installed at Brady's Geothermal Field.

Robertson, M. University of Wisconsin

Jun 01, 2016
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Brady's Geothermal Field Analysis of Pressure Data

*This submission provides corrections to GDR Submissions 844 and 845* Poroelastic Tomography (PoroTomo) by Adjoint Inverse Modeling of Data from Hydrology. The 3 *csv files containing pressure data are the corrected versions of the pressure dataset found in Submission 844. The ...

Lim, D. University of Wisconsin

Mar 17, 2017
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Active Source 3D Seismic Tomography of Brady Hot Springs Geothermal Field, Nevada

We deployed a dense seismic array to image the shallow structure in the injection area of the Brady Hot Springs geothermal site in Nevada. The array was composed of 238 5 Hz, three-component nodal instruments and 8,700 m of distributed acoustic sensing (DAS) fiber-optic cable inst...

Parker, L. University of Wisconsin

Aug 09, 2017
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Material Properties for Brady Hot Springs Nevada USA from PoroTomo Project

The PoroTomo team has completed inverse modeling of the three data sets (seismology, geodesy, and hydrology) individually, as described previously. The estimated values of the material properties are registered on a three-dimensional grid with a spacing of 25 meters between nodes....

Feigl, K. and PoroTomo Team, . University of Wisconsin

Mar 06, 2019
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