Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

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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 - Geothermal Data Repository 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 -
Export Citation to RIS
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, Geothermal Data Repository. 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]. Geothermal Data Repository. 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 Geothermal Data Repository. https://doi.org/10.15121/1891881
@misc{GDR_Dataset_1412, 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 = {Geothermal Data Repository, Idaho National Laboratory, https://doi.org/10.15121/1891881}, note = {Accessed: 2025-04-23}, 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

Wencheng Jin

Idaho National Laboratory

Trevor A. Atkinson

Idaho National Laboratory

Christine Doughty

Lawrence Berkeley National Laboratory

Ghanashyam Neupane

Idaho National Laboratory

Nicolas Spycher

Lawrence Berkeley National Laboratory

Travis L. McLing

Idaho National Laboratory

Patrick F. Dobson

Lawrence Berkeley National Laboratory

Robert Smith

University of Idaho

Robert Podgorney

Idaho National Laboratory

DOE 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

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