Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation
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 University of Pittsburgh, presented by Dr. Andrew Bunger. The project's objective was to characterize stress in the Utah FORGE EGS reservoir using three methods: a laboratory rock-core stress estimation combined with a Machine Learning approach for estimation of in-situ stress from field sonic-log data, a field based in-situ measurement (min-frac) approach, and a modeling approach. This presentation was featured at the Utah FORGE R&D Annual Workshop on September 8, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2020-1, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.
Citation Formats
TY - DATA
AB - 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 University of Pittsburgh, presented by Dr. Andrew Bunger. The project's objective was to characterize stress in the Utah FORGE EGS reservoir using three methods: a laboratory rock-core stress estimation combined with a Machine Learning approach for estimation of in-situ stress from field sonic-log data, a field based in-situ measurement (min-frac) approach, and a modeling approach. This presentation was featured at the Utah FORGE R&D Annual Workshop on September 8, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2020-1, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.
AU - Bunger, Andrew
DB - Geothermal Data Repository
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - EGS
KW - in-situ stress
KW - characterization
KW - stress modeling
KW - laboratory testing
KW - machine learning
KW - sonic log
KW - analysis
KW - mini-frac testing
KW - 2025 Annual Workshop
KW - presentation
KW - presentation recording
KW - presentation slides
KW - report
LA - English
DA - 2025/09/18
PY - 2025
PB - University of Pittsburgh
T1 - Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation
UR - https://gdr.openei.org/submissions/1780
ER -
Bunger, Andrew. Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation. University of Pittsburgh, 18 September, 2025, Geothermal Data Repository. https://gdr.openei.org/submissions/1780.
Bunger, A. (2025). Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation. [Data set]. Geothermal Data Repository. University of Pittsburgh. https://gdr.openei.org/submissions/1780
Bunger, Andrew. Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation. University of Pittsburgh, September, 18, 2025. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1780
@misc{GDR_Dataset_1780,
title = {Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - 2025 Workshop Presentation},
author = {Bunger, Andrew},
abstractNote = {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 University of Pittsburgh, presented by Dr. Andrew Bunger. The project's objective was to characterize stress in the Utah FORGE EGS reservoir using three methods: a laboratory rock-core stress estimation combined with a Machine Learning approach for estimation of in-situ stress from field sonic-log data, a field based in-situ measurement (min-frac) approach, and a modeling approach. This presentation was featured at the Utah FORGE R\&D Annual Workshop on September 8, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2020-1, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.},
url = {https://gdr.openei.org/submissions/1780},
year = {2025},
howpublished = {Geothermal Data Repository, University of Pittsburgh, https://gdr.openei.org/submissions/1780},
note = {Accessed: 2026-06-15}
}
Details
Data from Sep 18, 2025
Last updated Sep 21, 2025
Submitted Sep 18, 2025
Organization
University of Pittsburgh
Contact
Andrew Bunger
Authors
Keywords
geothermal, energy, Utah FORGE, EGS, in-situ stress, characterization, stress modeling, laboratory testing, machine learning, sonic log, analysis, mini-frac testing, 2025 Annual Workshop, presentation, presentation recording, presentation slides, reportDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
