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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
0 Stars
Publicly accessible
12 Resources
0 Stars
Publicly accessible
Cape EGS: Frisco 2-P Well Stimulation Microseismic Data
This dataset contains microseismic data acquired during the Frisco 2-P well stimulation project led by Fervo Energy, conducted between June 1 and June 11, 2024, near the Utah FORGE geothermal site. The microseismic data was collected from various Utah FORGE wells: via Distributed ...
Dadi, S. and Titov, A. Fervo Energy
Sep 19, 2024
26 Resources
0 Stars
Curated
26 Resources
0 Stars
Curated
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
0 Stars
Publicly accessible
18 Resources
0 Stars
Publicly accessible
Newberry EGS Demonstration: Well 55-29 Stimulation Data 2014
The Newberry Volcano EGS Demonstration in central Oregon, a 5 year project begun in 2010, tests recent technological advances designed to reduce the cost of power generated by EGS in a hot, dry well (NWG 55-29) drilled in 2008. First, the stimulation pumps used were designed to ru...
Cladhouhos, T. et al AltaRock Energy Inc
Sep 03, 2015
54 Resources
0 Stars
Curated
54 Resources
0 Stars
Curated
EGS Collab Experiment 1: Microseismic Monitoring
The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first expe...
Schoenball, M. et al Lawrence Berkeley National Laboratory
Jul 29, 2019
46 Resources
0 Stars
Curated
46 Resources
0 Stars
Curated
Utah FORGE: Discrete Fracture Network (DFN) Data
The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 1...
Finnila, A. and Podgorney, R. Golder Associates Inc.
Jun 24, 2020
66 Resources
0 Stars
Publicly accessible
66 Resources
0 Stars
Publicly accessible