{"@type": "dcat:Dataset", "DOI": "10.15121/2001502", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Sean Lattis", "hasEmail": "mailto:slattis@egi.utah.edu"}, "dataQuality": true, "description": "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.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1536/Utah%20FORGE%202-2439-Battelle%202023%20Annual%20Workshop%20Presentation.mp4", "description": "The presentation followed a peer review format comprised of two parts, a formal presentation roughly lasting 20 minutes followed by a Q&A discussion lasting about 25 minutes.", "format": "mp4", "mediaType": "application/octet-stream", "title": "Presentation Recording.mp4"}], "identifier": "https://data.openei.org/submissions/7623", "issued": "2023-09-08T06:00:00Z", "keyword": ["2023", "EGS", "Machine Learning", "Utah FORGE", "annual workshop", "boundary element method", "deformation rate analysis", "energy", "far-field", "geothermal", "in-situ stress", "laboratory experiments", "mini-frac", "modeling", "near-field", "rock-core stress estimation", "sleeve frac packer", "sonic-log data", "stress characterization"], "landingPage": "https://gdr.openei.org/submissions/1536", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2026-07-28T18:12:24Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "Battelle Memorial Institute"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-113.6300828125,37.00277522913662],[-109.7739828125,37.00277522913662],[-109.7739828125,41.04145697239045],[-113.6300828125,41.04145697239045],[-113.6300828125,37.00277522913662]]]}", "title": "Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - 2023 Annual Workshop Presentation"}