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Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation 2024 Annual Workshop Presentation

Sep 15, 2024
41.44 MB
Publicly accessible
This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, disc...
Authors
Dvory, N. Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgeuniversity of utahseismicaimachine learningdata-driven decisionscommunity safteyinfrastructure protectionproactive risk mitigationimproved safteyimmediate responsestimulationfracingground motionseismic hazardsegsvideopresentationdecision support

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation 2025 Workshop Presentation

Sep 18, 2025
84.04 MB
Publicly accessible
This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by Dr. No'am Zach Dvory. This video slide presentation, by the University of Utah, d...
Authors
Dvory, N. University of Utah
geothermalenergyutah forgeegs2025 annual workshopreal-time decision makingmachine learningseismic monitoringstimulation risk managementgeomechanicsseismologypresentationpresentation recordingpresentation slidesreport

Stanford Thermal Earth Model for the Conterminous United States

Mar 14, 2024
21.57 GB
Publicly accessible
Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States....
Authors
Aljubran, M. and Horne, R. Stanford University
thermal earth modeltemperaturegeothermalenergystanfordtemperature-at-depthheat flowrock thermal conductivityinterpignnphysics-informedgraph neural networksmachine learningmodeltemperature modelarcgisapimodel inputsmodel outputsdata-drivenspatial interpolationalgorithmheat conductionbottomhole temperature

Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress Final Report

Dec 22, 2025
2.43 MB
Publicly accessible
This comprehensive technical report documents a multi-component approach to in-situ stress characterization at the Utah FORGE EGS site that integrates Machine Learning (ML) methods for predicting near-well principal stresses around geothermal wells with the physics-based finite el...
Authors
Bunger, A. et al University of Pittsburgh
geothermalenergyin-situ stress estimationutah forgewave velocitythermo-poro-elastic modelingmachine learningegsnear-wellbore stressfar-field principal stressstress anisotropyphysics-based modelingfinite element modelingsonic logtuvtechnical reportreservoir characterization

Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

Sep 28, 2023
3.69 MB
Publicly accessible
This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...
Authors
Mustafa, A. et al Battelle Memorial Institute
geothermalenergyutahforgegeomechanicsmachine learningffnnartificial neural networkannin-situ stressstress characterizationlabtuvtriaxialstressfeed forward artificial neural networkmlegsmodellingexploratory data analysiseda

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

Appendices for Geothermal Exploration Artificial Intelligence Report

Jan 08, 2021
2.76 GB
Publicly accessible
The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...
Authors
Duzgun, H. et al Colorado School of Mines
geothermalenergyartificial intelligencehydrothermally altered mineralsmineral markerssvmgeodatabasewellfaultseismicaiborderbradydesert peaksalton sealand surface temperaturedeformationgeophysicalgeophysicssupport vector machinehyperspectralhyperspectral imagingcalifornianevadaegsblindblind systemdeep learningmachine learningexplorationgeospatial datashort wavelength infraredswirdatabaseanomaly detectionsite detectionradarhydrothermalmodelconceptual modelzoteroraw datapreproccessedprocessed dataenhanced geothermal systemengineered geothermal systemremote sensingarcgisgisinsarmorphologymorphologicalmorphological featurestirvnirvisible near infraredthermal infraredcodepython

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

INGENIOUS Great Basin Regional Dataset Compilation

Jun 30, 2022
116.98 MB
Publicly accessible
This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while re...
Authors
Ayling, B. et al GBCGE, NBMG, UNR
geothermalenergy2-meter probetemperaturegeochemistryquaternary falutsquaternary volcanicsgeodeticsseismicitypaleogeothermalwellsspringsslip and dilationingeniousplay fairway analysisgreat basinnevadautahidahocaliforniaoregonthermal conductivitymagnetotelluricsgravitymagneticsheat flowshapefilesgridssinterslipdilationearthquakesshearconductivityconductancegeotiffgeospatialcompilationdataregionaltufavolcanicsexplorationplay fairwaymodelingundiscovered systemsdiscoveryfavorabilityfaultsmachine learningelevation trenddetrended elevationseismic dataraw datasoda lake
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