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"16A78-32 Stimulation Tracer Test"×
Brady's, NV×

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Feb 18, 2021
4.49 MB
Publicly accessible
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...
Authors
Beckers, K. et al National Renewable Energy Laboratory
geothermalenergymachine learningsubsurfacecharacterizationbrady hot springspredictionreservoir modelingtime seriespcaprincipal component analysisreservoir managementbradys hot springsporotomoreservoirdual-porositystimulationinjection testmodeltemperatureflowpressuresimulationsingle-fracturedoubletheatmapheat maptensorflow

Brady 1D Seismic Velocity Model Ambient Noise Prelim

Oct 25, 2013
0.97 kB
Publicly accessible
Preliminary 1D seismic velocity model derived from ambient noise correlation. 28 Green's functions filtered between 4-10 Hz for Vp, Vs, and Qs were calculated. 1D model estimated for each path. The final model is a median of the individual models. Resolution is best for the top 1 ...
Authors
J., R. Lawrence Livermore National Laboratory
seismicvelocity modelbradysseismic velocitygeothermalnevada

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

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

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