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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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Utah FORGE: Laboratory Data for Emergent Pore Pressure Heterogeneity Controlling Slip Timing and Microseismicity

Included are experimental data recorded from shear experiments that explore the effects of pore pressure heterogeneity on microseismic character and fault slip timing resulting from shear reactivation of laboratory faults. Raw mechanical and acoustic data from 15 experiments are i...

Elsworth, D. et al Pennsylvania State University

Mar 02, 2026
32 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
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Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Utah FORGE: Laboratory Data for Emergent Pore Pressure Heterogeneity Controlling Slip Timing and Microseismicity

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results