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

Jin, Wencheng; Atkinson, Trevor A.; Doughty, Christine; Neupane, Ghanashyam; Spycher, Nicolas; McLing, Travis L.; Dobson, Patrick F.; Smith, Robert; Podgorney, Robert

doi:10.15121/1891881

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

Dataset
Other Related Research

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 »« less

Authors:: Jin, Wencheng; Atkinson, Trevor A.; Doughty, Christine; Neupane, Ghanashyam; Spycher, Nicolas; McLing, Travis L.; Dobson, Patrick F.; Smith, Robert; Podgorney, Robert

Publication Date:: Fri Apr 15 00:00:00 EDT 2022

Other Number(s):: 1412

DOE Contract Number:: FY22 AOP 2.8.1.1

Research Org.:: USDOE Geothermal Data Repository (United States); Idaho National Lab. (INL), Idaho Falls, ID (United States)

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

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; MOOSE

Geolocation:: 83.0,180.0|-83.0,180.0|-83.0,-180.0|83.0,-180.0|83.0,180.0

OSTI Identifier:: 1891881

DOI:: https://doi.org/10.15121/1891881

Project Location:

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.

Copy to clipboard


                    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

Copy to clipboard


                    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

Copy to clipboard


                    
@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}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.15121/1891881

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Journal Article Jin, Wencheng ; Atkinson, Trevor A. ; Doughty, Christine ; ... - Renewable Energy

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to identify 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.more »« less
Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Journal Article Jin, Wencheng ; Atkinson, Trevor A. ; Doughty, Christine ; ... - Renewable Energy

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to identify 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.more »« less
Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Journal Article Jin, Wencheng ; Atkinson, Trevor A. ; Doughty, Christine ; ... - Renewable Energy
Reactive Transport Simulations of High-Tempertature Geologic Thermal Energy Storage (GeoTES) in Deep Saline Formations - I/O Files

Dataset Spycher, Nicolas ; Doughty, Christine

Simulation input and output files, post-processed figures and excel tables, and tecplot layout files for generating figures. These simulations were run with TOUGHREACT V4.12 by Lawrence Berkeley National Laboratory in 2021. This work was completed as part of the geologic thermal energy storage (GeoTES) research project reported in the final report for Phase I of this work, which is linked below.
Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

Dataset Samarakoon, Anjana Malinge ; Tennant, Alan ; Batista, Cristian D ; ...

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data.more »« less

Similar Records