{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Andrew Bunger", "hasEmail": "mailto:bunger@pitt.edu"}, "dataQuality": true, "description": "This comprehensive technical report documents a multi-component approach to in-situ stress characterization at the Utah FORGE EGS site that integrates Machine Learning (ML) methods for predicting near-well principal stresses around geothermal wells with the physics-based finite element model for translating near-field stresses to far-field principal stresses. The ML framework leverages laboratory triaxial ultrasonic velocity (TUV) measurements and field sonic log data to establish velocity-to-stress relationships and estimate the three near-field principal stresses. The physics-based model accounts for thermo-poro-mechanical effects induced by drilling, fluid circulation, and logging operations, as well as stress perturbations associated with the inclined well trajectory.\n\nBy integrating data-driven ML predictions with physics-based thermo-poro-mechanical modeling, this workflow reconciles near-wellbore stress measurements with far-field in-situ stresses in a geothermal reservoir. Application to FORGE wells demonstrates that near-wellbore thermal and poroelastic disturbances can significantly modify local stress states and that the resulting stress anisotropy is strongly dependent on well orientation. The combined approach provides a robust framework for in-situ stress estimation in complex EGS settings and supports improved interpretation of sonic logs and stress-informed geothermal reservoir development.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1806/2_2439%20v2_Utah%20FORGE%20Project%20FINAL%20TECHNICAL%20REPORT_v3.pdf", "description": "Final report for the University of Pittsburgh-led Utah FORGE sponsored research project \"A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE EGS Site: Laboratory, Modeling and Field Measurement\".", "format": "pdf", "mediaType": "application/pdf", "title": "Final Technical Report.pdf"}], "identifier": "https://data.openei.org/submissions/8600", "issued": "2025-12-22T07:00:00Z", "keyword": ["EGS", "In-situ stress estimation", "Machine Learning", "Near-wellbore stress", "TUV", "Thermo-Poro-Elastic Modeling", "Utah FORGE", "Wave Velocity", "energy", "far-field principal stress", "finite element modeling", "geothermal", "physics-based modeling", "reservoir characterization", "sonic log", "stress anisotropy", "technical report"], "landingPage": "https://gdr.openei.org/submissions/1806", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2025-12-22T17:42:50Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "University of Pittsburgh"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-112.916367,38.483935],[-112.879748,38.483935],[-112.879748,38.5148],[-112.916367,38.5148],[-112.916367,38.483935]]]}", "title": "Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - Final Report"}