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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

PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data

Mar 29, 2016
342.13 TB
Publicly accessible
This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also include...
Authors
Feigl, K. et al University of Wisconsin
geothermalporotomodasfiber opticsurface sensorsseismic arraydistributed acoustic sensingporoeleastic tomographybradys geothermal fieldgeosciencedistributed sensingdownholetrenchedseismicityhydrothermalgeophysicsoediraw datajupyter notebookpythonhdf5hsdsh5pyh5pyd

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

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

Jun 30, 2021
13.6 MB
Publicly accessible
This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...
Authors
Buster, G. et al Upflow
geothermalenergymachine learningoptimizationoperationssynthetic datapower plantbig kahunagoomlgenetic optimizationforecastinputsoutputsconfigurationexamplephygnnphysics guided neural networkssteamfieldsteam fieldwellsflash plantsneural networkdataprocessed datacodepythonsimulationmodel

REopt Lite Geothermal Heat Pump Design Requirements

Mar 08, 2021
277.13 kB
Publicly accessible
This document describes the design requirements for the geothermal heat pump (GHP) module being added to the existing REopt Lite web tool. This document describes the purpose, users, and functional requirements to which the modified web tool shall conform. This document will be re...
Authors
Olis, D. National Renewable Energy Laboratory
geothermalenergyreoptreopt liteghpgeothermal heat pumpmodelmoduleweb toolweb interfacetooltechno-economiceconomics

Soda Lake Geothermal: Raw 3D and 3C Seismic-Reflection Data from 2010 Survey

Sep 01, 2010
172.11 GB
Publicly accessible
This dataset contains seismic-reflection records created in 2010 around the Soda Lake geothermal field near Fallon, Nevada. The data was collected by the power plant operator at the time, Magma Energy (CYRQ Energy in 2024). This was a petroleum-industry-quality three-dimensional ...
Authors
N. Louie, J. et al University of Nevada Reno
geothermalenergysoda lake geothermalseismic dataraw data3dseismic reflectionseg-yfield logsproject reportssoda lakenevadavibroseisfallon3cshot recordsfield recordssurveyseismic surveymagmageophysics

Utah FORGE: Triggered 3-Component Surface Nodal Seismic Waveforms from the April 2022 FOAL 1 Experiment

Jun 30, 2026
736.6 MB
Publicly accessible
This package contains triggered 3-component surface nodal seismic waveforms recorded during the FOAL 1 experiment, or FORGE Observation Array Linear #1, which occurred during the April 2022 hydraulic stimulation of well 16A(78)-32 at the Utah FORGE EGS site. This data was utilized...
Authors
Kim, J. et al Rice University
geothermalenergyegsutah forgeforgemicroseismichydraulic stimulationfoal 1forge observation array linear16a32-32april 2022microseismicityinduced seismicitypassive seismic monitoringsurface nodal array3-component seismic datasmartsolominiseedstationxmlevent catalogwell trajectoryfogmoregeophysicsseismic dataprocessed data

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography

Jun 01, 2020
12.59 MB
Publicly accessible
This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for we...
Authors
Chai, C. et al Oak Ridge National Laboratory
geothermalenergy3d seismic structureinteractivesurfegs collabp-waves-wavemicroseismic catalogseismic tomographyinteractive visualizationegsgeophysicsseismicmicroseismictomographyvelocitymicroearthquakemicro-earthquakemeqe1-pe1-ipassive source sensor3dmodelingvisualizationcatalogprocessed data

Programs and Code for Geothermal Exploration Artificial Intelligence

Apr 27, 2021
710.72 kB
Publicly accessible
The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including...
Authors
Moraga, J. Colorado School of Mines
geothermalenergycodershell scriptsgeothermal aimachine learningself organizing mapk-meanspythonaiartificial intelligencedeep learningexplorationgeothermal explorationremote sensingblindsite detectionlstland surface temperaturenumpyrastertensorflowk meananomaly detectionlandsat adr lstsbatchslurmshell

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Apr 15, 2022
113.79 MB
Publicly accessible
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Authors
Jin, W. et al Idaho National Laboratory
reservoir thermal energy storagestochastic simulationgeotesmachine learningmodelingtesht-rtescharacterizationnumerical modelstochastichydrogeologic formationsimulated datasimulation datahigh-temperaturethermal energy storageoptimizationartificial neural network regressionannneural networkoperation scenariosseasonal-cyclepareto frontsseasonal operationcontinuous operationfalconmoose

Utah FORGE: Laboratory Data for Emergent Pore Pressure Heterogeneity Controlling Slip Timing and Microseismicity

Mar 02, 2026
45.1 MB
Publicly accessible
Included are experimental data recorded from shear experiments that explore the effects of pore pressure heterogeneity on microseismic character and fault slip timing resulting from shear reactivation of laboratory faults. Raw mechanical and acoustic data from 15 experiments are i...
Authors
Elsworth, D. et al Pennsylvania State University
geothermalenergyinduced seismicitypore pressure heterogeneitygeomechanicsshear experimentsinjection rateutah forgeegsfault reactivationmicroseismicityacoustic emissionsfault slip timinglaboratory datatriaxial deformationgranitoid coresfluid injectionraw data

Utah FORGE: Fluid Injection-Rate Controls on Seismic Moment from Laboratory Fault Reactivation Experiments

Sep 16, 2025
5.43 GB
Publicly accessible
This dataset contains experimental and acoustic data from shear reactivation tests that investigate the relationship between fluid-injection rate, pore pressure distribution, and seismic moment during laboratory fault slip. It includes raw mechanical data and acoustic emission rec...
Authors
Elsworth, D. et al Pennsylvania State University
geothermalenergyinduced seismicityegsgeomechanicsutah forgegranitoidlaboratory experimentsfault reactivationshear slipfluid injectioninjection ratepore pressurepore pressure distributionseismic momentacoustic emissionspermeabilitytriaxial testingshear stressaxial displacementpressure vesselmechinical datatime-series dataraw dataprocessed datamatlab
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