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Utah FORGE: Fault Reactivation Through Fluid Injection Induced Seismicity Laboratory Experiments
Included are results from shear reactivation experiments on laboratory faults pre-loaded close to failure and reactivated by the injection of fluid into the fault. The sample comprises a single-inclined-fracture (SIF) transecting a cylindrical sample of Westerly granite.
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Yu, J. et al Penn State University
Jul 01, 2023
27 Resources
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27 Resources
0 Stars
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Curated Modeled Fault Data Set
The curated fault model simulation data set consists of tagged and fully-described time series representing simulated faults for the AFDD test building (ORNLs Flexible Research Platform (FRP)), including baseline performance, faulty performance, and corresponding energy impact. A ...
Kim, J. and Leach, M. National Renewable Energy Laboratory
Jun 24, 2019
7 Resources
0 Stars
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7 Resources
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Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment
Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding...
Roseboom, M. et al Pennsylvania State University
Nov 07, 2023
6 Resources
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6 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
0 Stars
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6 Resources
0 Stars
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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
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6 Resources
0 Stars
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