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 validating stress prediction models using ultrasonic velocity experiments on core samples and applying those models to sonic log data. The other report uses those near-field predictions as input to a thermo-poro-mechanical model to estimate far-field stress profiles under various thermal and pore pressure conditions.

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.

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 work was documented in a previous task report, which is linked below as "December 2022 Report". This 2023 task competition follows the accomplishments outlined the June 2023 report (also linked below), which elaborated the ML model development and validation strategy comprehensively. At this stage, prediction performances of ML models are further improved and implemented carefully for the estimation of in-situ stresses (i.e., Sv, SHmin, and SHmax over the depth ranging from 5000 to 6000 feet in the well 16A(78)-32). A comparison between ML-based and field-based stresses reflected the excellent harmony in terms of nominal errors at the sampling depths.

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 stresses in the well. The ML models were trained using laboratory-based triaxial ultrasonic wave velocity (labTUV) data wherein wave velocities were measured with various combinations of true triaxial applied stress. The ultrasonic velocities data include compressional, fast shear, and slow shear velocities in each of three directions for a total of nine velocities for each stress combination. However, because the ultimate goal is to deploy the trained model for interpretation of field sonic log data where only the vertically propagating waves are measured, the work here focuses on just the wave velocities with vertical (z-direction) propagation. Also, because vertical (overburden) is often well constrained, one approach explored here is to take the vertical stress also as known and train the model to predict the two horizontal stresses. This work was done as part of Utah FORGE project 2439: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement.

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 stresses in the well. The ML models were trained using laboratory-based triaxial ultrasonic wave velocity (labTUV) data wherein wave velocities were measured with various combinations of true triaxial applied stress. The ultrasonic velocities data include compressional, fast shear, and slow shear velocities in each of three directions for a total of nine velocities for each stress combination. However, because the ultimate goal is to deploy the trained model for interpretation of field sonic log data where only the vertically propagating waves are measured, the work here focuses on just the wave velocities with vertical (z-direction) propagation. Also, because vertical (overburden) is often well constrained, one approach explored here is to take the vertical stress also as known and train the model to predict the two horizontal stresses. This work was done as part of Utah FORGE project 2439: A Multi-Component Approach to Characterizing In-Situ Stress at the U.S. DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement.

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods: Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data; Method 2: Complete field based in-situ measurement (mini-frac); and Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods: Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data; Method 2: Complete field based in-situ measurement (mini-frac); and Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.

This report presents a series of numerical experiments investigating the relationships among near-well fracture complexity, in situ stress conditions, and wellbore deviation. Using a phase-field modeling approach, the study explores how factors such as stress regimes, wellbore orientation, and thermal cooling influence fracture propagation. The dataset includes a technical report detailing the modeling approach and findings, along with a repository of GEOS modeling input files. This work was conducted as part of Utah FORGE Project 2-2446, "Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity."

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 University of Pittsburgh, presented by Dr. Andrew Bunger. 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 at the Utah FORGE R&D Annual Workshop on September 8, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2020-1, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.

Core-based in-situ stress estimation, Triaxial Ultrasonic Velocity (labTUV) data, and Deformation Rate Analysis (DRA) data for Utah FORGE well 16A(78)-32 using triaxial ultrasonic velocity and deformation rate analysis. Report documenting a multi-component approach to characterizing in-situ stress at the U.S. DOE FORGE EGS site: laboratory, modeling and field measurement. Core-based methods for in-situ stress estimation were applied using samples from 5 intervals within the Utah FORGE 16A(78)-32 well. At three of these locations, Triaxial Ultrasonic Velocity (labTUV) tests were performed, resulting in experimentally-determined relationships between wave velocities and stresses. Non-monotonic increase in the velocity-stress relationships are inferred provide evidence of stress history and are therefore used to estimate in-situ stress magnitudes. Additionally, Deformation Rate Analysis (DRA) tests were run on core plugs from various orientations at each of the 5 sampling locations. These, too, provide evidence of stress history based on stress-strain behavior. A novel Weight of Evidence (WoE) method was developed as a means of synthesizing in-situ stress evidence from these two types of tests. Results indicate the minimum horizontal stress gradient ranges from 0.58 psi/ft to 0.69 psi/ft, with 4 of the 5 values between 0.66 psi/ft and 0.69 psi/ft. The vertical stress gradient ranges from 1.05 psi/ft to 1.12 psi/ft, with 4 of the 5 zones given results between 1.09 psi/ft and 1.12 psi/ft. The maximum horizontal stress gradient ranges from 0.98 psi/ft to 1.34 psi/ft, with 4 of the 5 zones falling between 0.98 psi/ft and 1.24 psi/ft. The stress regime thus appears to be on the edge between normal faulting and strike-slip faulting, potentially flipping back and forth between the two regimes due to variability of rock properties, structures such as faults, and/or thermal anomalies.