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Utah FORGE Well 16A(78)-32 Stimulation Data (April, 2022)
This is a set of data related to the stimulation program at Utah FORGE well 16A(78)-32 during April, 2022. This includes daily reports, 1 second Pason data, tracer data, and shear stimulation data and information including a report of an evolving prognosis for the stimulation oper...
McLennan, J. Energy and Geoscience Institute at the University of Utah
May 18, 2022
11 Resources
0 Stars
Curated
11 Resources
0 Stars
Curated
Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites
The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...
Zhu, T. Pennsylvania State University
May 05, 2021
4 Resources
0 Stars
Curated
4 Resources
0 Stars
Curated
WHOLESCALE: Seismic Survey Data from San Emidio Nevada 2021
This dataset includes raw and processed seismic data from the 2021 seismic survey at the San Emidio geothermal field in Nevada. In April and May 2021, 37 tri-axial short period seismographs were deployed in a 1.8km diameter cluster centered on 40.367278 N, 119.409019 W. The first...
Lord, N. et al University of Wisconsin-Madison
Apr 06, 2021
6 Resources
0 Stars
In curation
6 Resources
0 Stars
In curation
DCIF (Directional Cooling-Induced Fracturing) Westerly Granite AE Borehole Damage Effect Test (Task 3-0)
Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on three rectangular Westerly granite blocks (width=depth=4.0", height=2.0") which were preheated to 200, 400, and 600 degree C to induce damage (microcracks) with varying degrees. Liquid nitrogen was poured...
Nakagawa, S. Lawrence Berkeley National Laboratory
Jan 27, 2022
13 Resources
0 Stars
Curated
13 Resources
0 Stars
Curated
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
Curated
12 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
45 Resources
0 Stars
Curated
45 Resources
0 Stars
Curated