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Idaho National Laboratory×

EGS Collab Experiment 2: Earth Model Datasets

The EGS Collab Project performed a series of tests to increase the understanding the response of crystalline rock mass to stimulations and fluid circulation to efficiently implement enhanced geothermal systems (EGS) technologies. The EGS Collab team created two underground testbed...
Neupane, G. et al Idaho National Laboratory
May 29, 2022
6 Resources
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Utah FORGE: Data for 3-D Model Development Lithology, Temperature, Pressure, and Stress

This submission comprises two downloadable .zip archives. Each archive contains spreadsheets with 3-D data and a .txt document describing the data. The Mesh Files .zip download contains data regarding the lithologic contacts of the granitoid and overlying sedimentary basin fill. T...
Podgorney, R. Idaho National Laboratory
Mar 17, 2020
2 Resources
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EGS Collab Experiment 2: Laser Scanned 4100 L Drift Map

The EGS Collab project is evaluating a site for Experiment 2 (hydraulic fracturing/shearing) at a depth of 1.25 km in the Sanford Underground Research Facility (SURF) on the 4100 Level. Two early test holes were drilled in an alcove (formerly known as Battery Charging Station) nea...
Neupane, G. et al Idaho National Laboratory
Dec 19, 2019
4 Resources
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CAES 2014 Chemical Analyses of Thermal Wells and Springs in Southeastern Idaho

This dataset contains chemical analyses for thermal wells and springs in Southeastern Idaho. Data includes all major cations, major anions, pH, collection temperature, and some trace metals. These samples were collected in 2014 by the Center for Advanced Energy Studies (CAES), an...
Mattson, E. Idaho National Laboratory
Mar 10, 2014
7 Resources
0 Stars
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Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Jin, W. et al Idaho National Laboratory
Apr 15, 2022
4 Resources
0 Stars
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