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Utah FORGE 3-2514: A Strain Sensing Array to Characterize Deformation at the FORGE Site 2023 Annual Workshop Presentation

Sep 08, 2023
66.85 MB
Publicly accessible
This is a presentation on the Strain Sensing Array to Characterize Deformation at the FORGE Site project by Clemson University, presented by Lawrence Murdoch. The project's objective was to evaluate the feasibility of measuring and interpreting tensor strain data to improve the pe...
Authors
Murdoch, L. et al Clemson University
geothermalenergyannual workshop2023utah forgeegsfeasibilitystrain tensor datatensor strainmeterdeformationgeomechanical modelsclosed-form modelnumerical forward modelinversionstimulationwell testingproductionhigh-temperature tensor strainmeterreservoir characterizationmodel calibration

Utah FORGE 3-2417: Fiber-Optic Geophysical Monitoring of Reservoir Evolution 2023 Annual Workshop Presentation

Sep 08, 2023
54.51 MB
Publicly accessible
This is a presentation on the Fiber-Optic Geophysical Monitoring of Reservoir Evolution at the Utah FORGE Milford Site project by Rice University, presented by Prof. Jonathan Ajo-Franklin. The project's objective was to develop an end-to-end fiber-optic sensing approach for EGS to...
Authors
Ajo-Franklin, J. et al Rice University
geothermalenergyannual workshop2023utah forgeegsdistributed fiber optic sensingdfosdasflowing fracturesfracture creationwell pairegs performancemechanically compliant fracturesend-to-endegs fracture model

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
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