OpenEI: Energy Information
  • Geothermal Data Repository
  • My User
    • Sign Up
    • Login
 
  • Data
    • View All Submissions
    • Data Lakes
    • Data Standards
    • Submit Data
  • Help
    • Frequently Asked Questions
    • Data Submission Best Practices
    • Data Submission Tutorial Videos
    • Contact GDR Help
  • About
  • Search

Search GDR Data

Showing results 1 - 12 of 12.
Show results per page.
Order by:
Available Now:
Filters Clear All Filters ×
Topic
Technologies
Demonstration Sites
Data Type
"major element"×
Utah FORGE, UT×

Utah FORGE: Phase 3 Magnetotelluric (MT) Data

Oct 01, 2020
91.86 MB
Publicly accessible
New high-quality tensor MT data at 122 sites, including the vertical magnetic field and utilizing ultra-remote referencing, have been acquired over the Utah FORGE project area. The results will be used to delineate the densities of faults and fractures in crystalline basement rock...
Authors
Wannamaker, P. and Maris, V. Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forge mtmt datamagnetotelluric datautah forgeforgeutah geothermalgeophysical datafinite elementfeafemgeophysicsgravitymagneticsmagnetotelluricsseismicgeodesyegsroosevelt hot springsmilfordsite characterization

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: EGS Reservoir Produced Fluids Trace Elements 2024

May 21, 2025
11.36 kB
Publicly accessible
This dataset contains trace element concentrations from fluid samples collected during the August-September 2024 circulation test at Utah FORGE. Samples were taken from injection well 16A(78)-32, production well 16B(78)-32, and monitoring well 58-32 on four dates between August 16...
Authors
Simmons, S. Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forge16a78-3216b78-3258-32circulation testgeochemistryproduced fluidstrace elementsegsfluid chemistrywell samplinginjection wellproduction wellmonitoring wellunfiltered sampleselemental analysisgeothermal reservoirreservoir characterizationraw data

Utah FORGE: Southwestern Utah Magnetotelluric (MT) Data

Jan 22, 2024
234.04 MB
Publicly accessible
This comprehensive magnetotellurics (MT) dataset, which covers southwestern Utah, integrates 600 sites from various surveys, including those from the Utah FORGE, SubTER, and Play Fairway projects, all of which are linked below. The core of this dataset is the use of a 3D finite el...
Authors
Wannamaker, P. and Marris, V. Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgemtmagnetotelluricresistivitysouthwestern utahplay fairwaygeophysical datafinite elementfeafemgravitymagneticsseismicgeodesyegsroosevelt hot springsmilfordsite characterizationforge mtplay fairway analysisforgesubter

Utah FORGE 2439: Well 16B(78)-32 Field-Test Data from Mini-Frac Tests

Jul 02, 2023
2.73 GB
Publicly accessible
This submittal includes the field-test data collected during stress tests conducted in the Utah FORGE 16B(78)-32 wellbore to measure/characterize the stresses in the geothermal reservoir. The type of stress test performed is referred to as a mini-frac test or a micro-frac test. Th...
Authors
Kelley, M. et al Battelle Memorial Institute
geothermalenergygeomechanicsutahforgeminifracstressin-situ stressegsmini-fracstress testgeophysical logimage logacoustic logsonic logfracturinghydraulic fracturingreservoir stressinjectioncaliper logstemperature logsxmac16b78-32baker hughespacker element pressureflowbackpacker element

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 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

Utah FORGE 2-2446: Report on Phase Field Modelling of Near-Wellbore Hydraulic Fracture Nucleation and Propagation

Dec 31, 2023
5.37 MB
Publicly accessible
This is a report that describes the modelling of fracture nucleation and propagation in the near-wellbore region to understand the relationship between in situ stress and fracture patterns. A novel phase field formulation is described here, which represents fractures as a diffuse ...
Authors
Cusini, M. and Fei, F. Lawrence Livermore National Laboratory
geothermalenergyutah forgephase fieldegsmodeltechnical reportfracture nucleationfracture propagationnear-wellborephase field modelnumerical modelgeosfracturingnumerical resultsin situ stress

Utah FORGE: EGS Reservoir Produced Fluids Geochemistry 2022-2024

Feb 26, 2025
1.38 MB
Publicly accessible
This dataset contains geochemical analyses of produced fluids from the Utah FORGE site, specifically from wells 16A(78)-32, 16B(78)-32, and 58-32, collected during various stimulation, flowback, and circulation tests conducted between 2022 and 2024. The data contains element conce...
Authors
Simmons, S. et al Energy and Geoscience Institute at the University of Utah
geothermalenergyutah forgegeochemistryproduced fluids16a78-3258-32circulation testsstimulationflowback waterflowback watersstimulation testsegsreservoir monitoringdissolved gasesmineral dissolutionfluid chemistrybrine compositionchemical geothermometry16b78-32

Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement 2023 Annual Workshop Presentation

Sep 08, 2023
67.35 MB
Publicly accessible
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH], presented by Mark Kelley. The project's objective was to characterize stress in the U...
Authors
Kelley, M. and Bunger, A. Battelle Memorial Institute
geothermalenergyannual workshop2023utah forgeegsmachine learningin-situ stressstress characterizationmini-fracrock-core stress estimationsonic-log datamodelinglaboratory experimentsdeformation rate analysisboundary element methodsleeve frac packerfar-fieldnear-field

Utah FORGE: Groundwater Data

Jul 20, 2016
621.85 kB
Publicly accessible
This submission includes two modeled drawdown scenarios with new supply well locations, a total dissolved solids (TDS) concentration grid (raster dataset representing the spatial distribution of TDS), and an excel spreadsheet containing well data.
Authors
Moore, J. Energy and Geoscience Institute at the University of Utah
geothermalmilfordutahegsforgegroundwaterdrawdownwellsupplytdsconcentrationgeochemistrywellsconcentrationsmodelaquifertesttransducerdataflow ratescenariowater tablemodelledpredictedpotentialgeospatial dataarcgisgisshapefileshape fileutah forgelocationwell locationreservoirsupply wellroosevelt hot springsresourcecharacterizationwell data

Utah FORGE: Report and Associated Data from Measuring and Modeling Deformation 2018 through 2024

Aug 16, 2018
30.41 MB
Publicly accessible
The report provided here describes research activities between August 16th, 2018 and July 30th, 2024. The goals of the research activities are to conduct an Interferometric Synthetic Aperture Radar (InSAR) analysis and Ground Surface Deformation Modeling at the Utah FORGE site. In...
Authors
Feigl, K. and Batzli, S. University of Wisconsin Madison
geothermalenergyutah forgeforgeutah geothermalreservoirengineeringhydraulicfracturingegsinsarinterferometric synthetic aperture radarmodelingreportprocessed dataground deformationhydromechanical modelingfinite-elementgpssurface displacement
Google Map
  • About the GDR
  • Partners & Sponsors
  • Disclaimers
  • Developer Services
  • The GDR provides free access to data generated from projects funded by the U.S. Department of Energy's Office of Geothermal.
  • Content is available under Creative Commons Attribution 4.0 unless otherwise noted.

Privacy Policy Notification

This site uses cookies to store and share user preferences with other OpenEI sites, and uses Google Analytics to collect anonymous user information such as which pages are visited, for how often, and what searches or other webpages may have led users here. You can prevent Google Analytics from recognizing you on return visits to this site by disabling cookies on your browser or by installing a Google Analytics Opt-out Browser Add-on. By clicking "Accept" you agree this site can store cookies on your device and disclose information to OpenEI and Google Analytics in accordance with our privacy policy.

OpenEI Privacy Policy Google Analytics Terms of Service