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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

Utah FORGE 5-2428: Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip 2024 Annual Workshop Presentation

Aug 27, 2024
99.9 MB
Publicly accessible
This is a presentation on the Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior by Lawrence Livermore National Laboratory, presented by Kayla Kroll. This presentation addresses testing and modelling related to the dependency of EG...
Authors
Kroll, K. et al Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgeegsgeomechanicsfracturesvelocity step testfluid pressureshear fracture strengthcore flood testspermeabilitylaser profilometryreservoir modellinglawrence livermore national laboratoryllnlvideopresentation

Utah FORGE 3-2417: Simulation Meshes and Results from Extended Circulation and Pulse Interference Testing

Sep 05, 2025
6.17 MB
Publicly accessible
This dataset contains numerical simulation meshes and modeling results from extended circulation and pulse interference tests conducted between Utah FORGE wells 16A(78)-32 and 16B(78)-32. The simulations were performed as part of the FOGMORE Utah FORGE project (Fiber Optic MOnitor...
Authors
Lee, S. and Ghassemi, A. Rice University
geothermalenergyutah forgeegsfogmoresimulationmeshreservoir modelingextended circulation testpulse interference testnumerical simulationmesh datapressure distributiontemperature distributionpermeability modelingstress-dependent permeabilitydiscrete fracture networkdfnfiber optic monitoring16a78-3216b78-32paraviewmodelmodel filesmodel results

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

Utah FORGE: Groundwater Data

Jul 20, 2016
621.85 kB
Publicly accessible
This submission includes two modeled drawdown scenarios with new supply well locations, a total dissolved solids (TDS) concentration grid (raster dataset representing the spatial distribution of TDS), and an excel spreadsheet containing well data.
Authors
Moore, J. Energy and Geoscience Institute at the University of Utah
geothermalmilfordutahegsforgegroundwaterdrawdownwellsupplytdsconcentrationgeochemistrywellsconcentrationsmodelaquifertesttransducerdataflow ratescenariowater tablemodelledpredictedpotentialgeospatial dataarcgisgisshapefileshape fileutah forgelocationwell locationreservoirsupply wellroosevelt hot springsresourcecharacterizationwell data