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"2-2446"×
Brady's, NV×

Brady's Geothermal Field DAS Earthquake Data

Mar 21, 2016
4.08 GB
Publicly accessible
The submitted data correspond to the vibration caused by a 3.4 M earthquake and captured by the DAS horizontal and vertical arrays during the PoroTomo Experiment. Data is available in the original .sgy formats, as well as in standardized .h5 formats. Earthquake information : M ...
Authors
Feigl, K. University of Wisconsin
geothermalbrady hot springsporotomoseismicdistributed acoustic sensingearthquakedashorizontalverticalgeophysicsmonitoringseismicityarraypassive sourcenveqinduced seismicityseg-yhdf5

Brady Geothermal Field Borehole Pressure Data

Apr 01, 2016
4.64 MB
Publicly accessible
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 ...
Authors
Cardiff, M. and Lim, D. University of Wisconsin
geothermalenergyenhanced geothermal systemegshydrologyhydrogeologyboreholeobservation wellpressure databrady geothermal fieldporotomoporoelastic tomographywellwell databradybrady hot springsnevada

Brady's Geothermal Field Analysis of Pressure Data

Mar 17, 2017
10.13 MB
Publicly accessible
*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 ...
Authors
Lim, D. University of Wisconsin
geothermalenergyproduction flow rate datainjection flow rate datapressure datahydrologyhydrogeologybrady hot springsporotomoporoelastic tomographyegsenhanced geothermal systemsengineered geothermal systemsdeployment databradybradys geothermal fieldpressureflow rateboreholedownholewell datapressure sensorobservation wellspumping testwater pressureobservation well

Raw Pressure Data from Observation Wells at Brady's Hot Springs

Mar 13, 2016
5.15 MB
Publicly accessible
This .csv files contain the raw water pressure data from three observation wells during pumping tests performed in the Spring of 2016. Included is a "read me" file explaining the details of where and how the data were collected.
Authors
Lim, D. University of Wisconsin
geothermalpressure sensorpressurebrady hot springsobservation wellspumping testspressure datawater pressureread me fileobservation wellporotomobradys geothermal area

PoroTomo: BRAD and BRDY GPS Station RINEX Files March 26, 2015

Jan 01, 2015
49.48 kB
Publicly accessible
This dataset provides links to daily RINEX files for two GPS stations at Brady's Hot Springs as of March 26, 2015. The data is formatted as compressed GNSS RINEX observation files, accessible through formatted CSV files, as well as in links to the complete daily time series data. ...
Authors
Kreemer, C. University of Wisconsin
geothermalgeodesygpsbrady hot springbrady hot springsrinexftp locationporotomobradyfieldnevadabradbrdybradys hot springshydrothermal

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Oct 20, 2021
6.41 MB
Publicly accessible
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...
Authors
Beckers, K. et al National Renewable Energy Laboratory
geothermalenergymachine learningmlsubsurfacecharacterizationbrady hot springsbhspredictionreservoir modelingtime seriespcaprincipal component analysisreservoir managementreservoirdual-porositystimulationinjection testpdetemperatureflowpressuresimulationsingle-fracturedoubletheat maptensorflowcnnlstmmlphydrothermalopen source reservoirosrnevada

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

Sep 01, 2020
109.94 MB
Publicly accessible
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: brady_som_output.gri, brady_som_output.grd, brady_som_output.* desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Authors
Moraga, J. et al Colorado School of Mines
geothermalenergygeothermal explorationhydrothermal mineral alterationsland surface temperaturefault densitypsinsarsubsidenceupliftbrady hot springsdesert peaknevadaconvolutional neural networkfallonmachine learningmodelhydrothermalmineraltemperaturerastergeospatial datageotifftraining datatraining dataset

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

Oct 01, 2021
7.12 MB
Publicly accessible
In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydr...
Authors
Siler, D. and Pepin, J. United States Geological Survey
geothermalenergypca3d geologic modelgeologic modelgeologycharacterizationmachine learningmlbhsbrady hot springsprincipal component analysisproductionstressfaultsrbradyhydrothermalgeologic structureunsupervised3d well datacodegeothermicgeophysics

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

Oct 01, 2021
5.68 MB
Publicly accessible
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Authors
Siler, D. et al United States Geological Survey
geothermalenergynmfkbrady hot springsmachine learningmlbhsnonnegative matrix factorization k-meanshydrothermalbradyk-meansclusteringnonnegative matrix factorizationmatrix factorizationgeothermalcloudsmarttensorsunsupervised3d well data3d geologic mapgeologic structurefaultsstressgeologycharacterizationgeologic modelproductioncode
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