Rapid Forecasting of Underground Hydrogen Storage Using Fourier-Integrated Hybrid Neural Framework

Abstract Deep learning can significantly accelerate the simulation of the injection, storage and production processes in an underground hydrogen storage (UHS). By understanding complex system interactions, deep learning offers accelerated simulations, enabling the creation of an fast visualization, forecasting and optimization framework. Our research presents a novel data-driven approach leveraging deep learning to mitigate the computational challenges of high-fidelity underground hydrogen-storage simulations. In this study, an innovative Fourier-Integrated Hybrid Neural Framework (F-IHNF) is used to create deep-learning-based surrogate models for field-scale hydrogen storage simulation. This framework combines Convolutional LSTM, 3D convolutions, and Fourier Neural Operators (FNO) for precise spatio-temporal analysis, focusing on hydrogen flow dynamics influenced by production and injection cycles. The deep-learning-based accelerated simulation workflow is developed and deployed on Fenton Creek gas reservoir model, segmented into 97×18×35 grid blocks with 61,110 active cells and a grid size of 121ft×136ft×2.8ft. The newly developed 3D Fourier-Integrated Hybrid Neural Framework (F-IHNF) achieves an impressive 98% accuracy in blind test validation for both pressure and hydrogen saturation forecasting over a period of 2 years involving 3 production-injection cycles. In the data generation phase, 76 simulations of hydrogen storage in various realizations of Fenton Creek geomodel under various engineering parameters were generated over 48 hours, with each 24-month hydrogen-storage simulation averaging 38 minutes. In comparison, the F-IHNF takes 0.5 seconds to forecast the spatiotemporal pressure and saturation evolutions over 24 months. This marks a 5000-time speedup in the forecasting. It took approximately 2 hours to train the F-IHNF model using 60 training samples and 6 validation samples. By finely tuning key hyperparameters—including convolutional LSTM hidden channels, 3D convolutional channels, and the selection of Fourier modes in x, y, and z directions—the precision of F-IHNF was impoved. The use of a weighted mean absolute error for saturation and a normal mean absolute error for pressure resulted in validation MAEs of 0.002 and 0.004, respectively. This study integrates a Fourier-Integrated Hybrid Neural Framework with deep learning for accelerated simulation of underground hydrogen storage. This approach will support faster analysis, visualization, forecasting, monitoring, and robust optimization, potentially aiding the adoption of underground hydrogen storage in the renewable energy sector for a sustainable future.