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Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...

Lu, G. et al University of Pittsburgh

Aug 30, 2024
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Utah FORGE: Stress Logging Data

This spreadsheet consist of data and graphs from deep well 58-32 stress testing from 6900 7500 ft depth. Measured stress data were used to correct logging predictions of in situ stress. Stress plots shows pore pressure (measured during the injection testing), the total vertical in...

McLennan, J. Energy and Geoscience Institute at the University of Utah

Mar 14, 2018
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Utah FORGE 5-2428: Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior Workshop Presentation

This is a presentation on the Fracture Permeability Impact on Seismic Slip Behavior project by Lawrence Livermore National Laboratory, presented by Dr. Kayla A. Kroll. The project's objective is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemic...

Kroll, K. et al Lawrence Livermore National Laboratory

Sep 08, 2023
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Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 May 2025

These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and valida...

Lu, G. et al University of Pittsburgh

Jun 05, 2025
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Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

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 (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...

Kelley, M. et al Battelle Memorial Institute

Jun 19, 2023
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Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...

Mustafa, A. et al Battelle Memorial Institute

Sep 28, 2023
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