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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2024 Annual Workshop Presentation

Sep 17, 2024
52.7 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 Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate...
Authors
Williams, J. Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgemachine learningmulti frequencystimulation-induced seismicityseismicityseismicity predictorstimulationpredictive systemsdeep learningdlmagnitude-frequency distributionseismicegsvideopresentation

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 5-2557: Fluid and Temperature in Fracture Mechanics and Coupled THMC Processes 2023 Annual Workshop Presentation

Sep 08, 2023
64.42 MB
Publicly accessible
This is a presentation on the Role of Fluid and Temperature in Fracture Mechanics and Coupled Thermo-Hydro-Mechanical-Chemical (THMC) Processes for Enhanced Geothermal Systems project by Purdue University, presented by Distinguished Professor of Physics & Astronomy, Laura J. Pyrak...
Authors
Pyrak-Nolte, L. Purdue University
geothermalenergyannual workshop2023utah forgeegsrupture alogorithmwave motion algorithmmachine learninggeophysicsfalconthmcufalconsimulatorjoint inversioninjectionfracturingshearingpermeability evolutionpore pressure diffusiondynamic crack evolutionslipwave generationpresentation

Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

Aug 30, 2024
973.4 kB
Publicly accessible
This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...
Authors
Lu, G. et al University of Pittsburgh
geothermalenergyutah forgein-situ stress estimationphysics-based modelingfinite element methodmachine learning modelthermo-poro-mechanical effectwell loggingvelocity-to-stress relationshipmachine learningfemreporttechnical report16a78-32mlegs2-2439v2principal stressstress predictionfar-fieldpre-cooling

Machine Learning Model Geotiffs Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Jun 01, 2021
590.94 MB
Publicly accessible
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Ma...
Authors
Faulds, J. et al Nevada Bureau of Mines and Geology
geothermalenergyneural networkbayesianannelmbnnprincipal componentpcanmfmachine learningalgorithmplay fairwaynevadapfagreat basingeotiffexplorationcharacterizationinputsoutputsrasterfeature settraining sites
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