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Brady Geothermal Field Borehole Pressure Data
This submission supersedes pressure data from March 2017 which can be found as a link in the submission resources. This submission contains 3 .csv files with time series pressure data in 3 observation wells at Brady Geothermal Field as part of the PoroTomo project. These pressure ...
Cardiff, M. and Lim, D. University of Wisconsin
Apr 01, 2016
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
Porotomo: InSAR Data from San Emidio Geothermal Field, Nevada, 1992-2010
This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering San Emidio Geothermal Field in Nevada, USA as part of the porotomo project. Data included within this submission are the following:
> ENVI_T120_GDR.tgz: Tarred direc...
Reinisch, E. and Feigl, K. University of Wisconsin
Jun 25, 2019
7 Resources
0 Stars
Publicly accessible
7 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
1 Resources
0 Stars
Publicly accessible
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
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible
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
10 Resources
0 Stars
Publicly accessible
10 Resources
0 Stars
Publicly accessible