Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements
This 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.
Citation Formats
Energy and Geoscience Institute at the University of Utah. (2024). Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements [data set]. Retrieved from https://gdr.openei.org/submissions/1640.
Bunger, Andrew. Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements . United States: N.p., 04 Sep, 2024. Web. https://gdr.openei.org/submissions/1640.
Bunger, Andrew. Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements . United States. https://gdr.openei.org/submissions/1640
Bunger, Andrew. 2024. "Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements ". United States. https://gdr.openei.org/submissions/1640.
@div{oedi_1640, title = {Utah FORGE 2024 Annual R&D Workshop: University of Pittsburgh - A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements }, author = {Bunger, Andrew.}, abstractNote = {This 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.}, doi = {}, url = {https://gdr.openei.org/submissions/1640}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {09}}
Details
Data from Sep 4, 2024
Last updated Sep 4, 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 estimationDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080