5 datasets found
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Federal
Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions - September 2023 Report
Department of Energy —
This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum... -
Federal
Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement - Workshop Presentation
Department of Energy —
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... -
Federal
Utah FORGE 2-2404: Determination of Reservoir-Scale Stress State Presentation Slides
Department of Energy —
This PowerPoint summarizes the integration of multiple approaches and data to constrain wellbore stress models at Utah FORGE. This stress determination used faulting... -
Federal
Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions
Department of Energy —
This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning... -
Federal
Utah FORGE 2-2446: Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity - 2025 Workshop Presentation
Department of Energy —
This is a presentation on the Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity project by Lawrence Livermore National Laboratory,...
5 datasets found