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-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.
All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.
Citation Formats
TY - DATA
AB - 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-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.
All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.
AU - Jin, Wencheng
A2 - Atkinson, Trevor A.
A3 - Doughty, Christine
A4 - Neupane, Ghanashyam
A5 - Spycher, Nicolas
A6 - McLing, Travis L.
A7 - Dobson, Patrick F.
A8 - Smith, Robert
A9 - Podgorney, Robert
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1891881
KW - Reservoir Thermal Energy Storage
KW - Stochastic Simulation
KW - GeoTES
KW - Machine Learning
KW - Modeling
KW - TES
KW - HT-RTES
KW - characterization
KW - numerical model
KW - stochastic
KW - hydrogeologic formation
KW - simulated data
KW - simulation data
KW - High-Temperature
KW - Thermal Energy Storage
KW - Optimization
KW - artificial neural network regression
KW - ANN
KW - neural network
KW - operation scenarios
KW - seasonal-cycle
KW - Pareto fronts
KW - seasonal operation
KW - continuous operation
KW - Falcon
KW - MOOSE
LA - English
DA - 2022/04/15
PY - 2022
PB - Idaho National Laboratory
T1 - Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files
UR - https://doi.org/10.15121/1891881
ER -
Jin, Wencheng, et al. Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files. Idaho National Laboratory, 15 April, 2022, GDR. https://doi.org/10.15121/1891881.
Jin, W., Atkinson, T., Doughty, C., Neupane, G., Spycher, N., McLing, T., Dobson, P., Smith, R., & Podgorney, R. (2022). Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files. [Data set]. GDR. Idaho National Laboratory. https://doi.org/10.15121/1891881
Jin, Wencheng, Trevor A. Atkinson, Christine Doughty, Ghanashyam Neupane, Nicolas Spycher, Travis L. McLing, Patrick F. Dobson, Robert Smith, and Robert Podgorney. Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files. Idaho National Laboratory, April, 15, 2022. Distributed by GDR. https://doi.org/10.15121/1891881
@misc{OEDI_Dataset_7522,
title = {Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files},
author = {Jin, Wencheng and Atkinson, Trevor A. and Doughty, Christine and Neupane, Ghanashyam and Spycher, Nicolas and McLing, Travis L. and Dobson, Patrick F. and Smith, Robert and Podgorney, Robert},
abstractNote = {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-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.
All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.},
url = {https://gdr.openei.org/submissions/1412},
year = {2022},
howpublished = {GDR, Idaho National Laboratory, https://doi.org/10.15121/1891881},
note = {Accessed: 2025-05-09},
doi = {10.15121/1891881}
}
https://dx.doi.org/10.15121/1891881
Details
Data from Apr 15, 2022
Last updated Oct 12, 2022
Submitted Sep 1, 2022
Organization
Idaho National Laboratory
Contact
Wencheng Jin
404.906.7832
Authors
Original Source
https://gdr.openei.org/submissions/1412Research Areas
Keywords
Reservoir Thermal Energy Storage, Stochastic Simulation, GeoTES, Machine Learning, Modeling, TES, HT-RTES, characterization, numerical model, stochastic, hydrogeologic formation, simulated data, simulation data, High-Temperature, Thermal Energy Storage, Optimization, artificial neural network regression, ANN, neural network, operation scenarios, seasonal-cycle, Pareto fronts, seasonal operation, continuous operation, Falcon, MOOSEDOE Project Details
Project Name Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage using Planet Earth as a Thermal Battery (GeoTES): Phase II
Project Lead Jeffrey Bowman
Project Number FY22 AOP 2.8.1.1