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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
Publicly accessible

GDR Data Management and Best Practices for Submitters and Curators

Resources for GDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the GDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for develo...
Weers, J. et al National Renewable Energy Laboratory
Mar 31, 2021
3 Resources
1 Stars
Publicly accessible

Community Geothermal: Connecticut Workforce Needs Assessment Report and Data

Included here is a geothermal industry workforce needs assessment report for Connecticut. As part of the DOE-funded Community Geothermal Heating and Cooling Design and Deployment grant, Northeast Energy Efficiency Partnership (NEEP) conducted several online surveys to gain a bett...
Macpherson, C. et al Connecticut Department of Energy and Environmental Protection (CT DEEP)
Jan 22, 2024
1 Resources
0 Stars
Publicly accessible

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
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

DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano

Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested...
Taverna, N. et al National Renewable Energy Laboratory
Jan 24, 2024
14 Resources
0 Stars
Publicly accessible

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
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  • The GDR is the submission point for all data collected from research funded by the U.S. Department of Energy's Geothermal Technologies Office.
  • Content is available under Creative Commons Attribution 4.0 unless otherwise noted.

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