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"Machine Learning for in-situ stress"×
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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
2 Resources
0 Stars
Publicly accessible

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
3 Resources
0 Stars
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Hybrid machine learning model to predict 3D in-situ permeability evolution

Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...
Elsworth, D. and Marone, C. Pennsylvania State University
Nov 22, 2022
4 Resources
0 Stars
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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
Ahmmed, B. Stanford University
Apr 04, 2022
3 Resources
0 Stars
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Utah FORGE 2-2446: Connecting In Situ Stress and Wellbore Deviation to Near-Well Fracture Complexity using Phase-Field Simulations

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 ori...
Cusini, M. and Fei, F. Lawrence Livermore National Laboratory
Jan 30, 2025
2 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
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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

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

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
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

EGS Collab Experiment 1: SIMFIP Notch-164 GRL Paper

Characterizing the stimulation mode of a fracture is critical to assess the hydraulic efficiency and the seismic risk related to deep fluid manipulations. We have monitored the three-dimensional displacements of a fluid-driven fracture during water injections in a borehole at ~1.5...
Guglielmi, Y. Lawrence Berkeley National Laboratory
Sep 24, 2020
9 Resources
0 Stars
Publicly accessible

Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) ...
Ayyanar, R. et al Arizona State University
Feb 01, 2025
12 Resources
0 Stars
In curation

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...
Duzgun, H. et al Colorado School of Mines
Jan 08, 2021
12 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2446: Characterizing Stress Roughness Through Simulation of Hydraulic Fracture Growth

This dataset covers work that investigated the apparent toughness anisotropy at Utah FORGE by comparing microseismic data with stress profiles from field measurements. The study analyzes the hydraulic fracture growth of Stage 3 at Well 16A(78)-32 using MEQ data, calibrating a nume...
Cusini, M. and Fei, F. Lawrence Livermore National Laboratory
Jan 30, 2025
3 Resources
0 Stars
Publicly accessible

Newberry EGS Demonstration: Initial Project Report and Induced Seismicity Mitigation Plan, 2011

This is the first project report and induced seismicity mitigation plan for the Newberry Enhanced Geothermal Systems (EGS) Demonstration project. The primary objectives of this first phase were to obtain necessary permits and comply with all regulations, including NEPA, communica...
Cladouhos, T. et al AltaRock Energy Inc
May 05, 2024
4 Resources
0 Stars
Publicly accessible

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-w...
Chai, C. et al Oak Ridge National Laboratory
Apr 20, 2020
7 Resources
0 Stars
Publicly accessible

Utah FORGE 3-2535: Preliminary Report on Development of a Reservoir Seismic Velocity Model

This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larg...
Gritto, R. Array Information Technology
Jan 30, 2023
1 Resources
0 Stars
Publicly accessible

Simulating Complex Fracture Systems in Geothermal Reservoirs Using an Explicitly Coupled Hydro-Geomechanical Model

Low permeability geothermal reservoirs can be stimulated by hydraulic fracturing to create Enhanced (or Engineered) Geothermal Systems (EGS) with higher permeability and improved heat transfer to increase heat production. In this paper, we document our effort to develop a numerica...
Carrigan, C. et al Lawrence Livermore National Laboratory
Jan 01, 2011
2 Resources
0 Stars
Publicly accessible

Utah FORGE 2439: Well 16B(78)-32 Field-Test Data from Mini-Frac Tests

This submittal includes the field-test data collected during stress tests conducted in the Utah FORGE 16B(78)-32 wellbore to measure/characterize the stresses in the geothermal reservoir. The type of stress test performed is referred to as a mini-frac test or a micro-frac test. Th...
Kelley, M. et al Battelle Memorial Institute
Jul 02, 2023
4 Resources
0 Stars
Publicly accessible

OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to t...
Joswig-Jones. . et al National Renewable Energy Laboratory
Oct 26, 2021
12 Resources
0 Stars
Publicly accessible

Utah FORGE: Optimization of a Plug-and-Perf Stimulation (Fervo Energy)

Information around the plug-and-perf treatment design at Utah FORGE by Fervo Energy. Objective and Purpose: Develop a multistage hydraulic stimulation approach designed specifically to target the top three factors that control the technical and commercial viability of an EGS sys...
Norbeck, J. et al Fervo Energy
Feb 08, 2023
3 Resources
0 Stars
Publicly accessible

Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events

This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process ...
Williams, J. et al Global Technology Connection, Inc.
Jan 21, 2025
3 Resources
0 Stars
Publicly accessible

Method to Recover Media Ligand Losses During Sorption of Rare Earth Elements from Simulated Geothermal Brines

This document describes the method and results of an in-situ experiment used to confirm that ligand bleed from a sorptive media can be contained. The experiment focused on maintaining the media's sorption of rare earth elements (REE) obtained from a simulated geothermal brine dop...
Stull, D. Tusaar Corp.
May 24, 2016
2 Resources
0 Stars
Publicly accessible

Snake River Plain FORGE: Site Characterization Data

The site characterization data used to develop the conceptual geologic model for the Snake River Plain site in Idaho, as part of phase 1 of the Frontier Observatory for Research in Geothermal Energy (FORGE) initiative. This collection includes data on seismic events, groundwater,...
Moos, D. and Barton, C. Idaho National Laboratory
Apr 18, 2016
49 Resources
0 Stars
Publicly accessible

Community Geothermal: Final Thermal Conductivity Test Report and Data Logs Carbondale, CO

Provided here are a final thermal conductivity test report, a drilling log, and a heat rejection log from Carbondale, CO. Also attached is a report made before drilling, which contains predictions on the hydrogeologic conditions of the drill site. The forty-eight (48.9) hour in-si...
Ewbank, G. et al Clean Energy Economy for the Region (CLEER)
Oct 20, 2023
4 Resources
0 Stars
Publicly accessible
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