Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - Final Report
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.
By 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.
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| 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. By 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 |
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"title": "Final Technical Report.pdf",
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"accessURL": "https://gdr.openei.org/files/1806/2_2439%20v2_Utah%20FORGE%20Project%20FINAL%20TECHNICAL%20REPORT_v3.pdf",
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"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"."
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| 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",
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"reservoir characterization",
"sonic log",
"stress anisotropy",
"technical report"
]
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| landingPage | https://gdr.openei.org/submissions/1806 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-12-22T17:42:50Z |
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| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
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| title | Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress - Final Report |
