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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

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
1 Resources
0 Stars
Publicly accessible

Analyzed Boise Data for Oscillatory Hydraulic Tomography

Data here has been "pre-processed" and "analyzed" from the raw data submitted to the GDR previously (raw data files found at http://gdr.openei.org/submissions/479. doi:10.15121/1176944 after 30 September 2017). First, we submit .mat files which are the "pre-processed" data (must h...
Lim, D. University of Wisconsin
Jul 01, 2015
8 Resources
0 Stars
Publicly accessible

Pressure-Temperature Simulation at Brady Hot Springs

These files contain the output of a model calculation to simulate the pressure and temperature of fluid at Brady Hot Springs, Nevada, USA. The calculation couples the hydrologic flow (Darcy's Law) with simple thermodynamics. The epoch of validity is 24 March 2015. Coordinates are ...
Feigl, K. Temple University
Jul 11, 2017
3 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

Analysis of Existing InSAR Data Brady Hot Springs Geothermal Area

Attributes of synthetic aperture radar (SAR) data acquired by TerraSAR-X and TanDEM-X satellite missions and archived at WInSAR facility.
Feigl, K. University of Wisconsin
Dec 26, 2014
1 Resources
0 Stars
Publicly accessible

PoroTomo Project: Brady's Geothermal Field: GPS station deployment and data analysis

Brady's Field PoroTomo project. GPS station deployment and data analysis. Links to data files and website with station and time-series information
Kreemer, C. University of Nevada
Apr 26, 2016
8 Resources
0 Stars
Publicly accessible

Raw Pressure Data from Boise Hydrogeophysical Research Site (BHRS)

Pressure data from a phreatic aquifer was collected in the summer of 2013 during Multi-frequency Oscillatory Hydraulic Tomography pumping tests. All tests were performed at the Boise Hydrogeophysical Research Site. The data will be inverted using a fast steady-periodic adjoint-bas...
Lim, D. University of Wisconsin
Jul 17, 2013
20 Resources
0 Stars
Publicly accessible

Guelph, ON Canada DTS Metadata

Metadata for active distributed temperature survey (DTS) experiments at Guelph, Ontario Canada. This data that this metadata refers to was taken as part of the PoroTomo project. The metadata includes information about status, location, elevation, units, and other metadata.
Coleman, T. University of Wisconsin
Dec 15, 2014
3 Resources
0 Stars
Publicly accessible

PoroTomo Project: Brady's Geothermal Field, Subtask 3.5: GPS Data Analysis

Links to GPS RINEX data not previously reported, plus links to station web pages, which include most up-to-date time-series
Kreemer, C. University of Wisconsin
Jan 04, 2016
7 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

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