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Utah FORGE: Seismic Velocity Models, February 2021

This dataset contains a map, showing the Utah FORGE seismic stations, and seismic velocity model data. There are 61 1-D velocity models which are in a compressed TAR file. A paper is referenced at the end of this description which discusses the use of these data in 3D modelling. T...

Pankow, K. Energy and Geoscience Institute at the University of Utah

Feb 28, 2021
3 Resources
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Utah FORGE: Well 56-32 Drilling Data and Logs

This dataset consists of drilling data (Pason data spreadsheets, daily reports, days v. depth, mud logs), Schlumberger logs (FMI, shear anisotropy analysis, memory, sonic, array induction/spectral density/dual spaced neutron/gamma ray/caliper, spectral GR/temperature, and Gardner ...

Bristol, J. et al Energy and Geoscience Institute at the University of Utah

Mar 19, 2021
18 Resources
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Utah FORGE: Well 16A(78)-32: Summary of Drilling Activities

This is a 74 page detailed summary of the drilling activities of Utah FORGE 16A(78)-32, a highly deviated well completed on February 2nd, 2021, as part of the Utah FORGE project. This well was deviated at 65 degrees from vertical after reaching 6000 feet in depth had a total measu...

Winkler, D. et al Energy and Geoscience Institute at the University of Utah

Mar 20, 2021
1 Resources
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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
4 Resources
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GEOPHIRES files for DDU techno-economic simulations

During 2017-2019, the U.S. Department of Energy funded six geothermal deep direct-use (DDU) projects to investigate feasibility of DDU for heating, cooling and thermal storage in the United States. In a follow-on study conducted at the National Renewable Energy Laboratory (NREL), ...

Beckers, K. and Kolker, A. National Renewable Energy Laboratory

Mar 31, 2021
1 Resources
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Utah FORGE: Well 58-32 (MU-ESW1) FMI Log Fracture Results

A Schlumberger Fullbore Formation Micro-Imager (FMI) log was run from 7390 feet to 7527 feet depth in well 58-32, originally known as well MU-ESW1. Well 58-32 was completed in 2017 as part of Utah FORGE Phase 2. It reached a depth of 7536 feet and recorded a bottom hole temperatur...

BLOCHOWICZ, A. Energy and Geoscience Institute at the University of Utah

Mar 06, 2021
1 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
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EGS Collab Experiment 1: Circulation Testing Processed data

This submission includes processed and reduced data for circulation testing that was conducted at the 164' fracture on the 4850 ft level of the Sanford Underground Research Facility. The circulation tests were done to test the flow through the 164' fracture in the EGS Collab Exper...

Fu, P. et al Lawrence Livermore National Laboratory

Apr 01, 2021
7 Resources
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Directional Cooling-Induced Fracturing Westerly Granite Test Results

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on a short, cylindrical sample of Westerly granite (diameter = 4 inches, height ~ 2 inches). Liquid nitrogen was poured in a copper cup attached to the top of the sample, and the resulting acoustic emissions ...

Nakagawa, S. Lawrence Berkeley National Laboratory

Dec 18, 2020
5 Resources
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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
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