Surrogate Modelling for Optimization of Underground Hydrogen Storage based on Convolutional LSTM-Attention Network

Summary To deepen insights into the hydrogen recovery in UHS projects, conducting reservoir simulation and optimization to pinpoint optimal operating parameters becomes essential. However, this process is typically time-consuming. This work introduces a comprehensive framework designed for reservoir simulation and optimization in UHS, integrating a CNN-LSTM-Attention network and StoSAG. Important mechanisms, including compositional flow, cushion gas dynamics, and micro-bio reactions, are thoroughly incorporated in the UHS simulation. Results show a remarkable speedup of approximately 1000 times compared to optimization conducted solely with reservoir simulation, all while maintaining equivalent accuracy. This framework serves as an important guideline, offering crucial insights into accelerating the optimization of the UHS process and related projects.