Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - 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 Battelle [Columbus, OH], presented by Mark Kelley. 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 in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.
Citation Formats
Battelle Memorial Institute. (2023). Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation [data set]. Retrieved from https://dx.doi.org/10.15121/2001502.
Kelley, Mark, Bunger, Andrew. Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation. United States: N.p., 08 Sep, 2023. Web. doi: 10.15121/2001502.
Kelley, Mark, Bunger, Andrew. Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation. United States. https://dx.doi.org/10.15121/2001502
Kelley, Mark, Bunger, Andrew. 2023. "Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation". United States. https://dx.doi.org/10.15121/2001502. https://gdr.openei.org/submissions/1536.
@div{oedi_6017, title = {Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation}, author = {Kelley, Mark, 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 Battelle [Columbus, OH], presented by Mark Kelley. 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 in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.}, doi = {10.15121/2001502}, url = {https://gdr.openei.org/submissions/1536}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {09}}
https://dx.doi.org/10.15121/2001502
Details
Data from Sep 8, 2023
Last updated Sep 25, 2023
Submitted Sep 15, 2023
Organization
Battelle Memorial Institute
Contact
Sean Lattis
Authors
Original Source
https://gdr.openei.org/submissions/1536Research Areas
Keywords
geothermal, energy, annual workshop, 2023, Utah FORGE, EGS, Machine Learning, in-situ stress, stress characterization, mini-frac, rock-core stress estimation, sonic-log data, modeling, laboratory experiments, deformation rate analysis, boundary element method, sleeve frac packer, far-field, near-fieldDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080