A novel robust optimization framework based on surrogate modeling for underground hydrogen storage in depleted natural gas reservoirs

Underground hydrogen storage (UHS) plays a vital role in global net-zero energy systems, enabling the storage of excess renewable energy for future use. However, physical reservoir model-based optimization for UHS system design and operation is computationally expensive due to complex geological properties and well-operational controls. This study developed a novel, efficient framework for UHS stochastic optimization to address this challenge, integrating advanced compositional reservoir simulation, accurate surrogate modeling, and stochastic optimization techniques. First, a base reservoir simulation model was developed to capture compositional fluid flow, hydrogen methanation reactions, gravity segregation, hysteresis, and capillary effects. To rapidly evaluate various well controls and reservoir configurations, convolutional neural network (CNN)-bi-directional long short-term memory (BiLSTM)-Attention models were trained as surrogate models using a comprehensive dataset generated from reservoir simulations. The CNN transforms three-dimensional (3D) geological fields into one-dimensional (1D) vectors, effectively capturing spatial features. The BiLSTM network learns the temporal evolution of the input features over time by processing them in both forward and backward directions. Subsequently, the attention mechanism enhances prediction accuracy by identifying and emphasizing the most significant features at critical time steps. The well-trained surrogate models were seamlessly integrated into the stochastic optimization framework based on the genetic algorithm, aiming to maximize the net present value (NPV) from UHS projects. The results demonstrate that the surrogate model exhibits satisfactory performance in the context of prediction accuracy, computational efficiency, and scalability. Notably, the newly developed framework based on surrogate models achieves an approximate 4878-fold speedup compared to an approach relying solely on reservoir simulation, while maintaining comparable accuracy. Overall, the proposed framework offers a promising solution for UHS optimization, providing valuable insights for the design and management of sustainable energy infrastructure.