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Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

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-...
Lu, G. et al University of Pittsburgh
Aug 30, 2024
2 Resources
0 Stars
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Utah FORGE 2439: Report on Minifrac Tests for Stress Characterization

This report describes minifrac tests conducted in the 16B(78)-32 well at the Utah FORGE site to characterize subsurface stresses, including the magnitude and orientation of the minimum and maximum horizontal stresses and the magnitude of the vertical stress. A minifrac test was co...
Kelley, M. et al Battelle Memorial Institute
Feb 22, 2024
1 Resources
0 Stars
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Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

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...
Mustafa, A. et al Battelle Memorial Institute
Sep 28, 2023
3 Resources
0 Stars
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DCIF Westerly Granite AE Stress Effect Test (Task 3-1)

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on rectangular Westerly granite blocks (width=depth=4.0", height=2.0"). Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (A...
Nakagawa, S. and Trzeciak, M. Lawrence Berkeley National Laboratory
Jul 08, 2021
15 Resources
0 Stars
Publicly accessible

Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

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...
Kelley, M. et al Battelle Memorial Institute
Jun 19, 2023
1 Resources
0 Stars
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Utah FORGE 2-2446: Report on Laboratory Block Experiments with Six Different Combinations of Stresses and Rock Fabrics

This report documents a series of block-scale hydraulic fracturing experiments, simulating Utah FORGE conditions to investigate how different combinations of in situ stress regimes, well orientations, and thermal stress conditions influence fracture initiation and propagation. The...
Bunger, A. and Lu, Y. Lawrence Livermore National Laboratory
Jan 30, 2025
1 Resources
0 Stars
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Fallon FORGE: Distinct Element Reservoir Modeling

Archive containing input/output data for distinct element reservoir modeling for Fallon FORGE. Models created using 3DEC, InSite, and in-house Python algorithms (ITASCA). List of archived files follows; please see 'Modeling Metadata.pdf' (included as a resource below) for addition...
Blankenship, D. et al Sandia National Laboratories
Mar 12, 2018
2 Resources
0 Stars
Publicly accessible

Estimate stress for 16B(78)-32 based on sonic logging data and laboratory data, enabled by the validated ML algorithm

Report documenting completion of Milestone 2.3.2 of Utah FORGE project 2439v2: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement Presents: 1) Laboratory Triaxial Ultrasonic Velocity Experiments ...
Mustafa, A. et al University of Pittsburgh Pittsburgh, PA
Jun 05, 2025
1 Resources
0 Stars
Awaiting curation

Utah FORGE 3-2535: Compilation of Geodetic Data and Estimation of Associated Deformation

Report on possible geodetic signature of the 3 stimulations in April 2022 as well as a comparison with existing InSAR data gathered over the site before, during, and after the stimulation. In geothermal production it is important to understand the existing stress field and the cha...
Vasco, D. et al Lawrence Berkeley National Laboratory
Apr 29, 2022
4 Resources
0 Stars
Publicly accessible

Utah FORGE 2439: A Multi-Component Approach to Characterizing In-Situ Stress

Core-based in-situ stress estimation, Triaxial Ultrasonic Velocity (labTUV) data, and Deformation Rate Analysis (DRA) data for Utah FORGE well 16A(78)-32 using triaxial ultrasonic velocity and deformation rate analysis. Report documenting a multi-component approach to characterizi...
Bunger, A. et al Battelle Memorial Institute
Dec 13, 2022
4 Resources
0 Stars
Publicly accessible

Utah FORGE: 2024 Discrete Fracture Network Model Data

The Utah FORGE 2024 Discrete Fracture Network (DFN) Model dataset provides a set of files representing discrete fracture network modeling for the FORGE site near Milford, Utah. The dataset includes four distinct DFN model file sets, each corresponding to different time frames and ...
Finnila, A. and Jones, C. Energy and Geoscience Institute at the University of Utah
Sep 08, 2024
5 Resources
0 Stars
Publicly accessible

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

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...
Kelley, M. et al Battelle Memorial Institute
Jul 02, 2023
4 Resources
0 Stars
Publicly accessible

Thermal-Hydrological-Mechanical Modelling of Stockton University Reservoir Cooling System, Fine Scale Stress Test Modelling

Mesh, properties, initial conditions, injection/withdrawal rates for modelling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, fo...
Smith, J. et al Lawrence Berkeley National Laboratory
Feb 22, 2021
14 Resources
0 Stars
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Thermal-Hydrological-Mechanical Modelling of Stockton University Reservoir Cooling System, Large Scale Grid

Mesh, properties, initial conditions, injection/withdrawal rates for modeling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, on ...
Smith, J. et al Lawrence Berkeley National Laboratory
Feb 26, 2021
15 Resources
0 Stars
Publicly accessible