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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
1 Resources
0 Stars
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1 Resources
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Curated
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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3 Resources
0 Stars
Curated
Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence
This dataset provides Python scripts supporting both subsurface and surface geothermal exploration AI models developed for the project "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning." It includes two main components: (1) scripts fo...
Demir, E. and Duzgun, S. Colorado School of Mines
Sep 01, 2023
2 Resources
1 Stars
Curated
2 Resources
1 Stars
Curated
EGS Collab: Modeling and Simulation Working Group Teleconference Series (1-98)
This submission contains the presentation slides and recordings from the first 98 EGS Collab Modeling and Simulation Working Group teleconferences. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and resul...
White, M. et al Pacific Northwest National Laboratory
Feb 04, 2020
100 Resources
0 Stars
Publicly accessible
100 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
Curated
3 Resources
0 Stars
Curated
Altona, NY EGS Field Site Radon Study
Data include 222Rn activities and complimentary geochemical data for multiple field experiments as part of an EGS project
Brown, S. and Christensen, J. Lawrence Berkeley National Laboratory
Sep 02, 2016
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
Processed Lab Data for Neural Network-Based Shear Stress Level Prediction
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...
Marone, C. et al Pennsylvania State University
May 14, 2021
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
Utah FORGE: 2023 Phase 3B Year 1 Annual Report
This report discusses the objectives, goals, accomplishments and results of Phase 3B Year 1 of the Utah FORGE project. The report includes infrastructure, seismic monitoring, modelling, external R&D, communications and outreach, data produced, lessons learned, conclusions, and pla...
Moore, J. et al Energy and Geoscience Institute at the University of Utah
Aug 09, 2023
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Machine Learning Model Geotiffs Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Ma...
Faulds, J. et al Nevada Bureau of Mines and Geology
Jun 01, 2021
1 Resources
0 Stars
Publicly accessible
1 Resources
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Publicly accessible
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
Publicly accessible
3 Resources
0 Stars
Publicly accessible
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
4 Resources
0 Stars
Publicly accessible
GIS Resource Compilation Map Package Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups incl...
Brown, S. et al Nevada Bureau of Mines and Geology
Jun 01, 2021
1 Resources
0 Stars
Publicly accessible
1 Resources
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EGS Collab Experiment 1: Common Discrete Fracture Network
This package includes data and models that support hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF). A paper by Schwering et al. (2020) describes the deterministic basis for developing a "common" discrete fracture...
Schwering, P. et al Sandia National Laboratories
Sep 18, 2019
4 Resources
0 Stars
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4 Resources
0 Stars
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Improvements in 2016 to Natural Reservoir Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin
*These files add to and replace same-named files found within Submission 559 (hover over file display names to see actual file names in bottom-left corner of screen)*
The files included in this submission contain all data pertinent to the methods and results of a cohesive multi-st...
Camp, E. Cornell University
Aug 18, 2016
16 Resources
0 Stars
Publicly accessible
16 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
16 Resources
0 Stars
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Deep Direct-Use Feasibility Study Reservoir Productivity Uncertainty Analysis for the Tuscarora Sandstone, Morgantown, WV
This dataset contains figures that summarize the Tuscarora Sandstone core permeability data collected from the Preston 119 well in Preston County, WV, and summary results of a stochastic analysis that was used to estimate reservoir productivity for the currently unexplored Tuscaro...
Smith, J. West Virginia University
Dec 19, 2019
4 Resources
0 Stars
Publicly accessible
4 Resources
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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
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1 Resources
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Fallon FORGE: Distinct Element Reservoir Modeling
Archive containing input/output data for distinct element reservoir modeling for Fallon FORGE. Models created using 3DEC, InSite, and in-house Python algorithms (ITASCA). List of archived files follows; please see 'Modeling Metadata.pdf' (included as a resource below) for addition...
Blankenship, D. et al Sandia National Laboratories
Mar 12, 2018
2 Resources
0 Stars
Publicly accessible
2 Resources
0 Stars
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Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations
The purpose of phase 1 is to determine the feasibility of integrating geologic CO2 storage (GCS) with geothermal energy production. Phase 1 includes reservoir analyses to determine injector/producer well schemes that balance the generation of economically useful flow rates at the ...
A., T. Lawrence Livermore National Laboratory
Jan 01, 2012
9 Resources
0 Stars
Publicly accessible
9 Resources
0 Stars
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Geologic Reservoir Content Model from Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin
This dataset contains the known hydrocarbon reservoirs within the study area of the Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) as part of Phase 1, Natural Reservoirs Quality Analysis. The final values for Reservoir Productivity Index (RPI) and uncertainty...
E., T. Cornell University
Sep 30, 2015
2 Resources
0 Stars
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2 Resources
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Appalachian Basin Play Fairway Analysis: Natural Sedimentary Reservoirs Data 2016 Revision
Tier 3 data for Appalachian Basin sectors of New York, Pennsylvania and West Virginia used in a Geothermal Play Fairway Analysis of opportunities for low-temperature direct-use applications of heat. It accompanies data and materials submitted as Geothermal Data Repository Submissi...
Jordan, T. Cornell University
Dec 07, 2016
3 Resources
0 Stars
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3 Resources
0 Stars
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Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations
Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations: An Approach to Improve Energy Recovery and Mitigate Risk : FY1 Final Report
The purpose of phase 1 is to determine the feasibility of integrating geologic CO2 storage (GCS) with geothermal...
A., T. Lawrence Livermore National Laboratory
Jan 01, 2012
7 Resources
0 Stars
Publicly accessible
7 Resources
0 Stars
Publicly accessible
Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations
Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations: An Approach to Improve Energy Recovery and Mitigate Risk : FY1 Final Report
The purpose of phase 1 is to determine the feasibility of integrating geologic CO2 storage (GCS) with geothermal...
A., T. Lawrence Livermore National Laboratory
Jan 01, 2012
7 Resources
0 Stars
Publicly accessible
7 Resources
0 Stars
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Resource Analysis for Deep Direct-Use Feasibility Study in East Texas
The National Renewable Energy Laboratory, Southern Methodist University Geothermal Laboratory, Eastman Chemical, Turbine Air Systems, and the Electric Power Research Institute are evaluating the feasibility of using geothermal heat to improve the efficiency of natural gas power pl...
Richards, M. et al Southern Methodist University
Jun 28, 2018
13 Resources
0 Stars
Publicly accessible
13 Resources
0 Stars
Publicly accessible
Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations
Active Management of Integrated Geothermal-CO2 Storage Reservoirs in Sedimentary Formations: An Approach to Improve Energy Recovery and Mitigate Risk: FY1 Final Report
The purpose of phase 1 is to determine the feasibility of integrating geologic CO2 storage (GCS) with geothermal...
A., T. Lawrence Livermore National Laboratory
Jan 01, 2000
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible