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DASH Slow Strain Rates from Brady Hot Springs Geothermal Field during PoroTomo Deployment Period
This submission contains slow strain rates summed to radians over 30 second intervals [rad/s] derived from horizontal distributed acoustic sensing measurements (DASH) of Brady geothermal field during PoroTomo deployment (2016-Mar-14 to 2016-Mar-26). There is one file correspondin...
Reinisch, E. et al University of Wisconsin
Jun 27, 2018
20 Resources
0 Stars
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20 Resources
0 Stars
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Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications
To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group a consortium of several national labs and academic institutions has tabulated time-depe...
Beckers, K. et al Pacific Northwest National Laboratory
Feb 03, 2023
3 Resources
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3 Resources
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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
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13 Resources
0 Stars
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Utah FORGE: Well 16B(78)-32 DTS, RFS DSS Strain, and Absolute Strain Circulation Test Fiber Optic Data August 2024
This dataset contains processed fiber optic measurements collected during the extended cross-well circulation test at the Utah FORGE site in August 2024. The data was acquired from the 16B(78)-32 well using distributed fiber optic sensing (DFOS) technology, including distributed t...
Jurick, D. et al Neubrex Energy Services (US), LLC
Aug 30, 2024
9 Resources
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9 Resources
0 Stars
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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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12 Resources
0 Stars
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Subsurface Geological Information and Models in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy
This purpose of this set of entries is to group together the materials and analytical methods used in the assessment of the natural rock properties within and surrounding two potential reservoirs.
Jordan, T. et al Cornell University
Oct 27, 2019
27 Resources
0 Stars
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27 Resources
0 Stars
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Geothermal Reservoir Simulation Results in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy
This dataset contains input data, code, ReadMe files, output data, and figures that summarize the results of a stochastic analysis of geothermal reservoir production from two potential geothermal reservoirs that were evaluated for the Cornell University Deep Direct-Use project. Th...
Smith, J. and Beckers, K. Cornell University
Oct 29, 2019
10 Resources
0 Stars
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10 Resources
0 Stars
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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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18 Resources
0 Stars
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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
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100 Resources
0 Stars
Publicly accessible