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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Wencheng Jin",
"@type": "vcard:Contact",
"hasEmail": "mailto:wencheng.jin@inl.gov"
}
|
| dataQuality |
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| description | 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Machine-Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization",
"format": "118",
"accessURL": "https://doi.org/10.1016/j.renene.2022.07.118",
"mediaType": "application/octet-stream",
"description": "Link to journal article published in Renewable Energy detailing this work."
},
{
"@type": "dcat:Distribution",
"title": "Numerical Simulation - Models Inputs and Outputs.zip",
"format": "zip",
"accessURL": "https://gdr.openei.org/files/1412/Jin2022renewable.zip",
"mediaType": "application/zip",
"description": "This data set includes the numerical modeling input files (.e and .i) and output files (.csv) used to synthesize data, and the reduced-order machine learning models (.pkl format) trained from the synthesized data for reservoir thermal energy storage site identification. The input files include mesh files with fixed caprock and bedrock, and a varying reservoir thickness and two different scenarios - one with a seasonal operation case and one with a continuous operation case. See the readme file in the archive for more information."
},
{
"@type": "dcat:Distribution",
"title": "Dynamic Earth Energy Storage Terawatt-year Grid-scale Energy Storage Using Planet Earth as a Thermal Battery GeoTES Phase I Project Final Report",
"format": "HTML",
"accessURL": "https://gdr.openei.org/submissions/1416",
"mediaType": "text/html",
"description": "Link to other GDR submission containing final report for the DOE GTO funded research on geologic thermal energy storage, or commonly known as reservoir thermal energy storage."
},
{
"@type": "dcat:Distribution",
"title": "MOOSE-Based Falcon Code used in Simulations",
"format": "HTML",
"accessURL": "https://github.com/idaholab/falcon",
"mediaType": "text/html",
"description": "The MOOSE-based FALCON code used in this study is open-sourced and can be used to replicate the simulation cases. FALCON is a finite-element geothermal reservoir simulation and analysis code for coupled and fully implicit Thermo-Hydro-Mechanical-Chemical (THMC) geosystems based on the MOOSE framework mainly developed by Idaho National Laboratory. It solves the coupled governing equations for fluid flow, heat transfer, rock deformation and fracturing, and chemical reactions in geological porous media. "
}
]
|
| DOI | 10.15121/1891881 |
| identifier | https://data.openei.org/submissions/7522 |
| issued | 2022-04-15T06:00:00Z |
| keyword |
[
"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"
]
|
| landingPage | https://gdr.openei.org/submissions/1412 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2022-10-12T16:32:38Z |
| programCode |
[
"019:006"
]
|
| projectLead | Jeffrey Bowman |
| projectNumber | FY22 AOP 2.8.1.1 |
| projectTitle | Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage using Planet Earth as a Thermal Battery (GeoTES): Phase II |
| publisher |
{
"name": "Idaho National Laboratory",
"@type": "org:Organization"
}
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| title | Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files |
