Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods:
Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data;
Method 2: Complete field based in-situ measurement (mini-frac); and
Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore.
This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.
Citation Formats
TY - DATA
AB - This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods:
Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data;
Method 2: Complete field based in-situ measurement (mini-frac); and
Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore.
This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.
AU - Bunger, Andrew
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/2439748
KW - geothermal
KW - energy
KW - Utah FORGE
KW - in-situ stress
KW - Machine Learning
KW - Machine Learning for in-situ stress
KW - sonic logs
KW - mini-frac
KW - rock mechanics
KW - rock stress
KW - stress
KW - stress estimation
KW - video
KW - presentation
LA - English
DA - 2024/09/04
PY - 2024
PB - Energy and Geoscience Institute at the University of Utah
T1 - Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation
UR - https://doi.org/10.15121/2439748
ER -
Bunger, Andrew. Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation. Energy and Geoscience Institute at the University of Utah, 4 September, 2024, Geothermal Data Repository. https://doi.org/10.15121/2439748.
Bunger, A. (2024). Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation. [Data set]. Geothermal Data Repository. Energy and Geoscience Institute at the University of Utah. https://doi.org/10.15121/2439748
Bunger, Andrew. Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation. Energy and Geoscience Institute at the University of Utah, September, 4, 2024. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2439748
@misc{GDR_Dataset_1640,
title = {Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation},
author = {Bunger, Andrew},
abstractNote = {This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods:
Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data;
Method 2: Complete field based in-situ measurement (mini-frac); and
Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore.
This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024. },
url = {https://gdr.openei.org/submissions/1640},
year = {2024},
howpublished = {Geothermal Data Repository, Energy and Geoscience Institute at the University of Utah, https://doi.org/10.15121/2439748},
note = {Accessed: 2025-04-25},
doi = {10.15121/2439748}
}
https://dx.doi.org/10.15121/2439748
Details
Data from Sep 4, 2024
Last updated Sep 6, 2024
Submitted Sep 4, 2024
Organization
Energy and Geoscience Institute at the University of Utah
Contact
Sean Lattice
801.581.3547
Authors
Keywords
geothermal, energy, Utah FORGE, in-situ stress, Machine Learning, Machine Learning for in-situ stress, sonic logs, mini-frac, rock mechanics, rock stress, stress, stress estimation, video, presentationDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
