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EGS Collab Experiment 1: Tracer data tests
This file contains the first set of tracer data for the EGS Collab testbed. The first set of tracer tests were conducted during October-November, 2018. We have included tracer data for C-dots, chloride, fluorescein, and rhodamine-B. The details about the tracer test can be found i...
Neupane, G. et al Idaho National Laboratory
Apr 16, 2019
3 Resources
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3 Resources
0 Stars
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Utah FORGE Phase 3 Native State Model: 2022 Update
This is the Phase 3 native state model update. The Phase 3 numerical model represents a significant subsurface volume below the FORGE site footprint. The model domain of 4.0 km x 4.0 km x 4.2 km is located approximately between depths of 4000 to 4200 meters below land surface. Thi...
Podgorney, R. and Liu, R. Idaho National Laboratory
Jul 29, 2022
1 Resources
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1 Resources
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Utah FORGE Phase Native State FALCON Model Files
The submission includes FALCON input file and mesh for the an initial pressure-temperature simulation, and a second set for pressure-temperature-displacement simulation. All simulations are steady state. Data and input for the FORGE Phase 2 native state model were compiled from hi...
Podgorney, R. Idaho National Laboratory
Jun 06, 2019
2 Resources
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2 Resources
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EGS Collab Experiment 1: Second Set Tracer Test Results
The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. The first multi-well experimental site was established at the 4850 level in the Homestake Mine in Lead,...
Neupane, G. et al Idaho National Laboratory
Dec 19, 2019
4 Resources
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4 Resources
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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
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4 Resources
0 Stars
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