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Utah FORGE 2-2439v2: A Multi-Component Approach to Characterizing In-Situ Stress Final Report

This comprehensive technical report documents a multi-component approach to in-situ stress characterization at the Utah FORGE EGS site that integrates Machine Learning (ML) methods for predicting near-well principal stresses around geothermal wells with the physics-based finite el...

Bunger, A. et al University of Pittsburgh

Dec 22, 2025
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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
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Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 May 2025

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 valida...

Lu, G. et al University of Pittsburgh

Jun 05, 2025
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Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity October, 2025

This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included t...

Williams, J. et al Global Technology Connection, Inc.

Oct 13, 2025
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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2025 Workshop Presentation

This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Dr. Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to esti...

Williams, J. GTC Analytics

Sep 18, 2025
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TEAMER: OpenFAST Modeling and Simulation of the Aquantis AQ10 Marine Hydrokinetic Turbine

This dataset was developed under TEAMER technical support (CRD-21-17763-0) to model the Aquantis AQ10, a spar buoy-based marine hydrokinetic turbine, using the OpenFAST simulation framework. The project transitioned modeling from the proprietary Tidal Bladed tool to OpenFAST to en...

Swales, H. and Tran, T. Aquantis, Inc.

Jul 31, 2025
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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
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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
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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
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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
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AeroDyn V15.04: Design Tool for Wind and MHK Turbines

AeroDyn is a time-domain wind and MHK turbine aerodynamics module that can be coupled into the FAST version 8 multi-physics engineering tool to enable aero-elastic simulation of horizontal-axis wind turbines. AeroDyn V15.04 has been updated to include a cavitation check for MHK tu...

Murray, R. et al National Renewable Energy Laboratory

Apr 28, 2017
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Two-Phase Flow of Ferrofluids in Porous Media: Plateau-Rayleigh-like Instability and Suppression of Fingering

Includes six mp4 and one PDF files. The mp4 files show the percolation of ferrofluid in porous media under a magnetic field. The names of the authors are available in the PDF file. The data are from an experimental study performed at the Michigan Technological University. The expe...

Askari, R. and Khurana, M. Michigan Tech

Feb 27, 2021
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Simulation Tools for Modeling Thermal Spallation Drilling on Multiple Scales

Widespread adoption of geothermal energy will require access to deeply buried resources in granitic basement rocks at high temperatures and pressures. Exploiting these resources necessitates novel methods for drilling, stimulation, and maintenance, under operating conditions that ...

Walsh, S. et al Lawrence Livermore National Laboratory

Jan 01, 2012
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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
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Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States....

Aljubran, M. and Horne, R. Stanford University

Mar 14, 2024
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WHOLESCALE: Coordinates of wells at San Emidio, Nevada

This dataset includes position coordinates and elevation information for wells at the WHOLESCALE San Emidio project location. Well positions in the attached file are characterized by UTM coordinates (Easting, Northing) in meters, and WHOLESCALE coordinates (Easting, Northing) rel...

Cardiff, M. et al University of Wisconsin Madison

Sep 25, 2023
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Occupant Behavior in Commercial Buildings: Synthetic Population, Co-Simulation with EnergyPlus and Agent Based Modeling

This technical manual serves as an outline of three separate research projects related to building energy performance modeling of occupant behavior, conducted at the Rutgers Center for Green Building (RCGB): a Synthetic Population project, a Co-Simulation of Synthetic Population w...

Andrews, C. Rutgers Center for Green Building

Apr 27, 2016
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WHOLESCALE: Mass Flux Rates for Wells at San Emidio in December 2016

This dataset provides mass flux rates in kg/s from six (production and injection) wells at San Emidio at minute intervals from December 1, 2016 December 15, 2016. Files for injection wells are named with "IW", for instance "WellIW42-21SI.csv", and include negative flux rates. Fil...

Cardiff, M. et al University of Wisconsin Madison

Dec 01, 2016
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Seismic Survey 2016 Metadata at San Emidio, Nevada

1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in o...

Lord, N. et al University of Wisconsin

Dec 05, 2016
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Utah FORGE: Roosevelt Hot Springs Analytical Well-Based Temperature Model Data

This submission contains a cumulative record of one-dimensional temperature modeling based off of well data in the vicinity of the Utah FORGE site. Temperature log data from wells used, and in some cases were extrapolated below the bottom of a number of wells. The data were corre...

Podgorney, R. and Allis, R. Idaho National Laboratory

Dec 07, 2018
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GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information...

Vesselinov, V. Los Alamos National Laboratory

Mar 29, 2021
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Northwest National Marine Renewable Energy Center, OR Final Technical Report & Appendices

In 2008, the US Department of Energy (DOE) Wind and Water Power Program issued a funding opportunity announcement to establish university-led National Marine Renewable Energy Centers. Oregon State University and the University of Washington combined their capabilities in wave and ...

Hellin, D. Northwest National Marine Renewable Energy Center

Jun 30, 2016
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Impact of uncoordinated plug-in electric vehicle charging on residential power demand supplementary data

This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," [1]. These files include electricity demand profiles for 200 households randomly selected among the ones available in the 2009 R...

MuratoriNational Renewable Energy Laboratory

Jun 13, 2017
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Community Geothermal: Borefield Design, Thermal Conductivity, and Subsurface Modeling Data Chicago, IL

This dataset encompasses the development of a geothermal energy system for the West Woodlawn neighborhood in Chicago, Illinois. This project is part of a broader initiative to design and deploy geothermal heating and cooling systems at a community scale. The dataset includes therm...

Baser, T. et al Saint Louis University

Dec 04, 2023
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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
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