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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

Utah FORGE 5-2557: Final Report and Presentation for the Role of Fluid and Temperature in Fracture Mechanics and Coupled THMC Processes for Enhanced Geothermal Systems

Nov 30, 2025
163.74 MB
Publicly accessible
This contains a final technical report and closeout presentation recording summarizing the results from Utah FORGE Project 5-2557 on the role of fluid pressure and temperature in fracture mechanics and coupled thermo-hydro-mechanical-chemical (THMC) processes relevant to enhanced ...
Authors
Pyrak-Nolte, L. Purdue University
geothermalenergythermo-hydro-mechanical-chemicalthmcutah forgeegstechnical reportlaboratory experimentstheoretical developmentsnumerical simulationscirculation testsfracture slippermeability evolutionseismicaseismicmoose farmfriction theoryaijoint inversion

Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction 2025 Workshop Presentation

Sep 18, 2025
86.94 MB
Publicly accessible
This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This vid...
Authors
Nakata, N. Lawrence Berkeley National Laboratory
geothermalenergyutah forge2025 annual workshopegsinduced seismicitytraffic light systemmachine learningseismicityforecastingground motion predictiongenerative aireservoir engineeringhigh-pressure experimentspresentationpresentation slidespresentation recordingreport

Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events

Jan 21, 2025
971.63 kB
Publicly accessible
This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process ...
Authors
Williams, J. et al Global Technology Connection, Inc.
geothermalenergyutah forgedata processingmachine learninginduced seismicitytechnical reportevent detectionmlartificial intelligenceaireal-timephysics informedrecurrent neural networksborehole seismicseismic datamicroseismicevent catalogmagnitude-frequency distributiongeophysicsegs

Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity October, 2025

Oct 13, 2025
1.62 MB
Publicly accessible
This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included t...
Authors
Williams, J. et al Global Technology Connection, Inc.
geothermalenergydeep learninginduced seismicitypredictivemagnitudeartificial intelligenceaimachine learningmldlphysics-basedmodelingutah forgeegstechnical reportseismic datainjection parametersgeophysical modelsprobabilistic

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Apr 04, 2022
1.04 GB
Publicly accessible
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
Authors
Ahmmed, B. Stanford University
geothermalenergymachine learningartificial intelligenceaiexplorationmodelmodelingprocessed datatraining datatraining datasetremote sensinghidden geothermal resourcesresource detectiondiscoverydevelopmentresourceneural networkprediction

Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2025 Workshop Presentation

Sep 18, 2025
70.48 MB
Publicly accessible
This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Dr. Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to esti...
Authors
Williams, J. GTC Analytics
geothermalenergyutah forgeegs2025 annual workshopinduced seismicitymachine learningrecurrent neural networksprobabilistic modelingseismic response predictionmagnitude-frequency analysisphysics-informed aipresentationpresentation recordingpresentation slidesreport

Utah FORGE: Phase-Picking Based Microseismic Event Catalog April 2024 Stimulation

Feb 19, 2026
1.73 MB
Publicly accessible
This catalog contains microseismic event locations recorded during the April 2024 stimulation at the Utah FORGE site. Events were detected and located using a phase-picking workflow that integrates downhole geophones (wells 56 and 78B) and downhole DAS (well 16B). P and S-wave arr...
Authors
Zhu, W. et al University Of Utah
geothermalenergyapril 2024 stimulationforgeutahmicroseismic event locationsutah forgeuniversity of utahseismicaimachine learningdata-driven decisionscommunity safteyinfrastructure protectionproactive risk mitigationimproved safteyimmediate responsestimulationfracingground motionseismic hazardsegsevent catalogmlprocessed datareservoir characterizationhydraulic fracturinggeomechanicsgeophysics
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