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Utah FORGE 3-2535: Preliminary Report on Development of a Reservoir Seismic Velocity Model

Jan 30, 2023
5.8 MB
Publicly accessible
This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larg...
Authors
Gritto, R. Array Information Technology
geothermalenergy3d seismic velocity modelseismic resolutionseismicgeophysicsreservoirmodelvelocityforgeutah forgeegscharacterizationreportpreliminarymilfordphasenetneural networkingmachine learningdeep learning

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 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 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 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 May 2025

Jun 05, 2025
20.34 MB
Publicly accessible
These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and valida...
Authors
Lu, G. et al University of Pittsburgh
geothermalenergyutah16b78-32in-situ stressultrasonic velocityutah forgeegs16bmachine learningtrue triaxial testingsonic logsstress predictionfar-field stressthermo-poro-mechanicalmodelingdeep learningfinite element modelgeothermal reservoirstress profilingstress anisotropytechnical report

Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Feb 07, 2023
Size unavailable
Publicly accessible
Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Authors
Mordensky, S. et al United States Geological Survey
geothermalenergypfahydrothermalenergy assessmentresource assessmentretrospectivemachine learninggeosciencewestern usdata-drivenbias reductionfavorabilitymappingegslow tempprocessed dataresourcecharacterization

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

May 05, 2021
11.59 GB
Publicly accessible
The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...
Authors
Zhu, T. Pennsylvania State University
geothermalenergycodedeep learningmachine learningaiartificial intelligenceegsenhanced geothermal systemsengineered geothermal systemsnewberryoregonnewberry volcanomlraw dataprocessed datamicroseismicitynumpywaveformpreprocessedpythonnewberry volcanic sitemicroearthquakemeqseismicgeophysicsgeophysical

U.S. Geothermal Heat Pump Siting Regulation and Zoning Ordinances (2026)

Sep 21, 2026
95.6 MB
Curated
This is a collection of documented state and local ordinances governing geothermal heat pumps (GHPs), ground-source heat pumps, and related geothermal exchange wells and loop systems throughout the United States. The data were compiled using the Infrastructure Continuous Ordinance...
Authors
Pinchuk, P. et al National Laboratory of the Rockies
geothermalheatpumpexchangegeo-exchangeheatingcoolingwellclosed loopopen loophorizontal loopvertical loopordinanceregulationsetbacknoisepermittingaillmcompassinfra-compassmachine readablegeothermal heat pumpghpartificial intelligenceheating and cooling

Sentinel-1 Input Data for PSInSAR Analysis

Apr 29, 2021
305.43 GB
Publicly accessible
Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a l...
Authors
Moraga, J. Colorado School of Mines
geothermalenergyinsarpsinsarsentinel-1synthetic aperture radarsatellite imagingaiartificial intelligencedeep learningmachine learningaerial photographyremote sensingsite detectionanomaly detectioninput datageologydisplacementsubsoilphenomenaprocessed dataradarimaginginterferometricanalysissoil deformationgeospatial data
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