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"Machine Learning for in-situ stress"×

Utah FORGE 2-2446: Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity 2025 Workshop Presentation

This is a presentation on the Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity project by Lawrence Livermore National Laboratory, presented by Dr. Fan (Frank) Fei. The project's objective was to employ a combination of high-fidelity simulations and tr...
Kroll, K. and Fei, F. Lawrence Livermore National Laboratory
Sep 18, 2025
3 Resources
0 Stars
Publicly accessible

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
0 Stars
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Python Codebase and Jupyter Notebooks Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, a...
Brown, S. and Smith, C. Nevada Bureau of Mines and Geology
Jun 30, 2022
4 Resources
0 Stars
Publicly accessible

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...
Zhu, T. Pennsylvania State University
May 05, 2021
4 Resources
0 Stars
Publicly accessible

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: brady_som_output.gri, brady_som_output.grd, brady_som_output.* desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Moraga, J. et al Colorado School of Mines
Sep 01, 2020
16 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2404: Determination of Reservoir-Scale Stress State Presentation Slides

This PowerPoint summarizes the integration of multiple approaches and data to constrain wellbore stress models at Utah FORGE. This stress determination used faulting theory, breakouts, and drilling-induced cracks detected in image logs. Wellbore stress profiles were established f...
Ghassemi, A. et al The University of Oklahoma
Jul 31, 2022
1 Resources
0 Stars
Publicly accessible

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...
Buster, G. et al Upflow
Jun 30, 2021
11 Resources
0 Stars
Publicly accessible

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation 2024 Annual Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, disc...
Dvory, N. Energy and Geoscience Institute at the University of Utah
Sep 15, 2024
1 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2446: Report on Laboratory Block Experiments with Six Different Combinations of Stresses and Rock Fabrics

This report documents a series of block-scale hydraulic fracturing experiments, simulating Utah FORGE conditions to investigate how different combinations of in situ stress regimes, well orientations, and thermal stress conditions influence fracture initiation and propagation. The...
Bunger, A. and Lu, Y. Lawrence Livermore National Laboratory
Jan 30, 2025
1 Resources
0 Stars
Publicly accessible

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
0 Stars
Publicly accessible

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Jin, W. et al Idaho National Laboratory
Apr 15, 2022
4 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2404: Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE 2023 Annual Workshop Presentation

This is a presentation on the Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE project by the University of Oklahoma, presented by Dr. Ahmad Ghassemi, McCasland Chair Professor. The project's objective was to develop a methodology ...
Ghassemi, A. University of Oklahoma
Sep 08, 2023
1 Resources
0 Stars
Publicly accessible

Utah FORGE 2439: Report on Minifrac Tests for Stress Characterization

This report describes minifrac tests conducted in the 16B(78)-32 well at the Utah FORGE site to characterize subsurface stresses, including the magnitude and orientation of the minimum and maximum horizontal stresses and the magnitude of the vertical stress. A minifrac test was co...
Kelley, M. et al Battelle Memorial Institute
Feb 22, 2024
1 Resources
0 Stars
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Active Source Seismic (Ultrasonic) Data from Double-Direct Shear Lab Experiments

Active source ultrasonic data from lab experiments p5270 and p5271 including raw waveforms (WF) and mechanical data (mat). From the PSU team working on the "Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties" project. The fric...
Marone, C. Pennsylvania State University
May 05, 2021
1 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2404: Application of Advanced Techniques for Determination of Reservoir-Scale Stress State 2024 Annual Workshop Presentation

This is a presentation on the Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at FORGE by the University of Oklahoma, presented by Ahmad Ghassemi. This video discusses how magnitude and orientation of natural in-situ principal stresses at dept...
Ghassemi, A. et al Energy and Geoscience Institute at the University of Utah
Aug 28, 2024
1 Resources
0 Stars
Publicly accessible

STRESSINVERSE Software for Stress Inversion

The STRESSINVERSE code uses an iterative method to find the nodal planes most consistent with the stress field given fault frictional properties. STRESINVERSE inverts the strike, rake and dip from moment tensor solutions for the in-situ state of stress. The code iteratively solves...
Gritto, R. Array Information Technology
Oct 31, 2018
1 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2446: Report on Phase Field Modelling of Near-Wellbore Hydraulic Fracture Nucleation and Propagation

This is a report that describes the modelling of fracture nucleation and propagation in the near-wellbore region to understand the relationship between in situ stress and fracture patterns. A novel phase field formulation is described here, which represents fractures as a diffuse ...
Cusini, M. and Fei, F. Lawrence Livermore National Laboratory
Dec 31, 2023
1 Resources
0 Stars
Publicly accessible

Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies

This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system c...
BushNational Renewable Energy Laboratory
Aug 01, 2020
2 Resources
0 Stars
Publicly accessible

BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...
Tripp, C. et al National Renewable Energy Laboratory
May 20, 2022
4 Resources
0 Stars
Publicly accessible

Utah FORGE: Native State Numerical Model 2025 Update

This dataset contains a three-dimensional native-state numerical model of the Utah FORGE site, updated in 2025, representing thermal, hydraulic, and stress conditions within the granitic reservoir and overlying sedimentary formations. The model domain extends 6 km by 6 km laterall...
Vazic, B. et al Idaho National Laboratory
Jan 30, 2026
1 Resources
0 Stars
Publicly accessible

Fuel Cell Inverter Transition Between Modes of Operation (Grid-Forming and Grid-Following)

This dataset shows the operation of the fuel cell inverter under grid-forming mode of operation, grid-following mode of operation and transition between the two modes.
Nemsow. . et al National Renewable Energy Laboratory
Dec 23, 2024
2 Resources
0 Stars
Publicly accessible

BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...
Tripp, C. et al National Renewable Energy Laboratory
Dec 30, 2022
9 Resources
1 Stars
Publicly accessible

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation 2025 Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by Dr. No'am Zach Dvory. This video slide presentation, by the University of Utah, d...
Dvory, N. University of Utah
Sep 18, 2025
3 Resources
0 Stars
Publicly accessible

Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction 2025 Workshop Presentation

This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This vid...
Nakata, N. Lawrence Berkeley National Laboratory
Sep 18, 2025
3 Resources
0 Stars
Publicly accessible

Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2024 Annual Workshop Presentation

This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate...
Williams, J. Energy and Geoscience Institute at the University of Utah
Sep 17, 2024
1 Resources
0 Stars
Publicly accessible
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