U.S. Department of EnergyOffice of Scientific and Technical Information
Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files
Abstract
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 weremore »
- Authors:
-
- Idaho National Laboratory
- Publication Date:
- Other Number(s):
- 1412
- Research Org.:
- DOE Geothermal Data Repository; Idaho National Laboratory
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
- Collaborations:
- Idaho National Laboratory
- Subject:
- 15 GEOTHERMAL ENERGY; ANN; Falcon; GeoTES; HT-RTES; High-Temperature; MOOSE; Machine Learning; Modeling; Optimization; Pareto fronts; Reservoir Thermal Energy Storage; Stochastic Simulation; TES; Thermal Energy Storage; artificial neural network regression; characterization; continuous operation; hydrogeologic formation; neural network; numerical model; operation scenarios; seasonal operation; seasonal-cycle; simulated data; simulation data; stochastic
- OSTI Identifier:
- 1891881
- DOI:
- https://doi.org/10.15121/1891881
Citation Formats
Jin, Wencheng, Atkinson, Trevor A., Doughty, Christine, Neupane, Ghanashyam, Spycher, Nicolas, McLing, Travis L., Dobson, Patrick F., Smith, Robert, and Podgorney, Robert. Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files. United States: N. p., 2022.
Web. doi:10.15121/1891881.
Jin, Wencheng, Atkinson, Trevor A., Doughty, Christine, Neupane, Ghanashyam, Spycher, Nicolas, McLing, Travis L., Dobson, Patrick F., Smith, Robert, & Podgorney, Robert. Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files. United States. doi:https://doi.org/10.15121/1891881
Jin, Wencheng, Atkinson, Trevor A., Doughty, Christine, Neupane, Ghanashyam, Spycher, Nicolas, McLing, Travis L., Dobson, Patrick F., Smith, Robert, and Podgorney, Robert. 2022.
"Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files". United States. doi:https://doi.org/10.15121/1891881. https://www.osti.gov/servlets/purl/1891881. Pub date:Fri Apr 15 00:00:00 EDT 2022
@article{osti_1891881,
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.},
doi = {10.15121/1891881},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 15 00:00:00 EDT 2022},
month = {Fri Apr 15 00:00:00 EDT 2022}
}