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Showing results 1 - 13 of 13.
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Utah FORGE, UT×

Utah FORGE: Laboratory Data for Emergent Pore Pressure Heterogeneity Controlling Slip Timing and Microseismicity

Mar 02, 2026
45.1 MB
Publicly accessible
Included are experimental data recorded from shear experiments that explore the effects of pore pressure heterogeneity on microseismic character and fault slip timing resulting from shear reactivation of laboratory faults. Raw mechanical and acoustic data from 15 experiments are i...
Authors
Elsworth, D. et al Pennsylvania State University
geothermalenergyinduced seismicitypore pressure heterogeneitygeomechanicsshear experimentsinjection rateutah forgeegsfault reactivationmicroseismicityacoustic emissionsfault slip timinglaboratory datatriaxial deformationgranitoid coresfluid injectionraw data

Utah FORGE: Fluid Injection-Rate Controls on Seismic Moment from Laboratory Fault Reactivation Experiments

Sep 16, 2025
5.43 GB
Publicly accessible
This dataset contains experimental and acoustic data from shear reactivation tests that investigate the relationship between fluid-injection rate, pore pressure distribution, and seismic moment during laboratory fault slip. It includes raw mechanical data and acoustic emission rec...
Authors
Elsworth, D. et al Pennsylvania State University
geothermalenergyinduced seismicityegsgeomechanicsutah forgegranitoidlaboratory experimentsfault reactivationshear slipfluid injectioninjection ratepore pressurepore pressure distributionseismic momentacoustic emissionspermeabilitytriaxial testingshear stressaxial displacementpressure vesselmechinical datatime-series dataraw dataprocessed datamatlab

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

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

Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Nov 07, 2023
12.98 MB
Publicly accessible
Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding...
Authors
Roseboom, M. et al Pennsylvania State University
geothermalenergyinduced seismicityinjection rateegsseismic momentgeomechanicsgeophysicsutah forgeshearmatlabshear experimentsraw datacodecore experimenttemcoconfining pressurepore pressureaxial pressureshear displacementfracturealong-fault pressureconstant shear stress

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

Jun 19, 2023
2.3 MB
Publicly accessible
This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...
Authors
Kelley, M. et al Battelle Memorial Institute
geothermalenergymachine learningin-situ stressstress characterizationutah forgegeophysicsseismictriaxialstress predictionartificial neural networkmodelfeed forward artificial neural network

Utah FORGE: Friction-Permeability-Seismicity Laboratory Experiments with Non-Linear Acoustics

Jul 08, 2022
7.68 MB
Publicly accessible
Laboratory experimental data on saw-cut interface of Westerly Granite and Utah Forge granitoid rocks. Experiments include velocity-stepping and fluid pressure stepping experiments. Mechanical data from 3 ISCO pumps connected to a Temco pressure vessel measure axial, confining and ...
Authors
Eijsink, A. and Elsworth, D. Pennsylvania State University
geothermalutah forgefriction experimentsfault permeabilityutahacousticsnonlinear acousticsacoustic dataraw datapressurevelocitystep testtestfailuretesting

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: Source Imaging DAS-Based Seismic Event Catalog April 2024 Stimulation

Aug 26, 2025
140.41 kB
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 DAS-specific source imaging workflow that avoids conventional phase picking. Instead, STA/LTA-transformed DAS and downhole...
Authors
Dvory, N. et al The University Of Utah
geothermalenergyforgeutahmicroseismic event locationsutah forgeuniversity of utahseismicaimachine learningdata-driven decisionscommunity safteyinfrastructure protectionproactive risk mitigationimproved safteyimmediate responsestimulationfracingground motionseismic hazardsegsevent catalogmlprocessed datareservoir characterizationapril 2024 stimulation

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

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: Fault Reactivation Through Fluid Injection Induced Seismicity Laboratory Experiments

Jul 01, 2023
119.91 MB
Publicly accessible
Included are results from shear reactivation experiments on laboratory faults pre-loaded close to failure and reactivated by the injection of fluid into the fault. The sample comprises a single-inclined-fracture (SIF) transecting a cylindrical sample of Westerly granite. All expe...
Authors
Yu, J. et al Pennsylvania State University
energyinduced seismicityegsutah forgewesterly graniteraw datacodematlabfault activationinjection testpressureflow ratereactivationshearfluid
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