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Stimulation and Microseismicity×

Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2026 Workshop Presentation

Sep 30, 2026
73.58 MB
Curated
This is a presentation on Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics (Global Technology Connection), presented by Dr. Jesse Williams. The project is developing machine-learning and physics-informed models to impro...
Authors
Williams, J. GTC Analytics
geothermalenergyutah forgeegs2026 annual workshopmachine-learningneural networksphysics basedphysics-informedseismic responseinduced seismicityforecastingstimulation-induced seismicitytraffic lightgeothermal reservoirreservoir managementpresentationreportdecision support

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 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

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 5-2419: Final Report and Presentation on Seismicity Permeability Relationships Probed via Nonlinear Acoustic Imaging

Sep 30, 2025
254.88 MB
Publicly accessible
This submission contains the final technical report and closeout presentation for Utah FORGE Project 5-2419, which investigates the coupled evolution of permeability and induced seismicity in enhanced geothermal systems using laboratory experiments, field observations, and nonline...
Authors
Elsworth, D. Pennsylvania State University
geothermalenergyutah forgeegspermeabilityinduced seismicitylaboratory experimentsfield observationsnonlinear acoustic imagingfault reactivationreservoir rockmicroearthquakestimulationphysics informed modelingmachine learningseismic momentstress statepermeability creationseismic hazardtechnical reportgeomechanicsgeophysicsmicroseismicity

Utah FORGE 6-3712: Curated and Fused 2022 and 2024 Stimulation Injection Datasets and Processing Report February 2026

Feb 25, 2026
155 MB
Publicly accessible
This submission contains curated injection parameter datasets from the 2022 and 2024 stimulation experiments conducted at the Utah FORGE site, along with the report documenting the data processing workflow. The datasets were developed as part of Project 6-3712: Probabilistic Estim...
Authors
Williams, J. et al Global Technology Connection, Inc.
geothermalenergyinduced seismicityinjection parametersdatasetstimulationutah forgeegsstimulation experimentinjection datatreating pressureslurry rateclean ratecumulative injected volumeinflow rateflowbacktime-series dataprocessed datahydraulic stimulationfeomechanics

Utah FORGE 5-2615: Laboratory Data for Insights on Hydraulic Fracture Closure and Stress Measurement

Jun 12, 2024
4.82 MB
Publicly accessible
This dataset includes data from injection/fall-off experiments conducted in controlled laboratory settings. The aim is to investigate the physics governing fracture closure and the associated stress measurements during hydraulic fracturing. These time series data include flow rat...
Authors
Ye, Z. and Ghassemi, A. University of Oklahoma
hydraulic fracturinginjection/fall-off testsin-situ stress measurementsfracture closuregeothermalegsutah forgeminifracstressflow ratepressurevolumelaboratoryenergyinjectionfall-offwater injectionoil injectionsierra white granitecrab orchard sandstonescioto sandstonekismetgeomechanics

Hybrid machine learning model to predict 3D in-situ permeability evolution

Nov 22, 2022
5.58 MB
Publicly accessible
Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...
Authors
Elsworth, D. and Marone, C. Pennsylvania State University
geothermalenergyegsnewberryhydraulicstimulationprocessed datamachine learningpermeability evolutionhydraulic fracturinginduced seismicityegs collabseismic data analysiswellhead pressureflow ratefracture permeabilitymicroearthquakeenhanced geothermal systems
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