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

Dataset · Fri Apr 15 04:00:00 EDT 2022

DOI:https://doi.org/10.15121/1891881· OSTI ID:1891881

Jin, Wencheng ^[1]; Atkinson, Trevor A.; Doughty, Christine; Neupane, Ghanashyam; Spycher, Nicolas; McLing, Travis L.; Dobson, Patrick F.; Smith, Robert; Podgorney, Robert

---

1. Idaho National Laboratory

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.

• Research Organization: DOE Geothermal Data Repository; Idaho National Laboratory
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
• Contributing Organization: Idaho National Laboratory
• OSTI ID: 1891881
• Report Number(s): 1412
• Availability: [e-mail]
• Country of Publication: United States
• Language: English

Related Subjects

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