Dynamic Response Modeling in Underground Hydrogen Storage Using a Fourier-Integrated Hybrid Neural Framework
Authors: Omeke J. , Misra S. , Retnanto A. 
Published: February 2025

Abstract
Underground hydrogen storage (UHS) is crucial for balancing renewable energy fluctuations, but modeling its dynamic injection and withdrawal cycles introduces sharp fronts and complex behaviors. Traditional neural networks when modeling an underground hydrogen storage operation struggle with capturing the hyperbolic, sudden, dramatic changes, often smoothing out critical transitions. This study presents an advanced neural network, Fourier-Integrated Hybrid Neural Framework (F-IHNF), tailored to accurately model these sudden and sharp fronts, addressing the computational difficulties associated with high-fidelity UHS simulations.
F-IHNF can model the 3D dynamic responses in field-scale UHS simulations. F-IHNF combines Convolutional LSTM, 3D convolutions, and Fourier Neural Operators (FNO) for detailed spatio-temporal analysis of hydrogen flow during injection and production cycles. The framework efficiently predicts changes in hydrogen plume saturation and pressure. Using a modified Fenton Creek reservoir model (97×18×35 blocks and 61110 active cells) and Latin Hypercube sampling, large reservoir models with varying reservoir, geological and fluid properties and operational conditions were constructed to train a reliable surrogate model.
This study successfully developed the 3D F-IHNF deep learning model to effectively track dynamic responses and complex flow fronts arising from cyclic injection and production in UHS. The architecture's integration of Convolutional LSTM, 3D convolutions, and Fourier Neural Operators (FNO) enables precise modeling of these complex flow behaviors associated with the storage process. Achieving 98% accuracy in blind test validation, the model accurately predicted hydrogen saturation and pressure evolution across 220 epochs. We generated 76 simulations over 48 hours, each representing a 24-month period, averaging 38 minutes per simulation. In contrast, the trained F-IHNF predicted a single 24-month case in 0.5 seconds. Training required 2 hours using 60 training samples and 6 validation samples. Key hyperparameters, including convolutional LSTM channels, 3D convolutional channels, and Fourier modes, were optimized for precision, resulting in mean absolute errors (MAEs) of 0.002 for saturation and 0.004 for pressure. This framework demonstrates the capability for rapid, accurate spatial-temporal predictions, essential for managing the complexities of UHS.
This study introduces the F-IHNF deep learning model, designed for underground hydrogen storage (UHS)—a field still in its early development with significant ongoing research. The model addresses the complexities of dynamic responses during cyclic injection and production, providing rapid predictions that drastically reduce computational costs compared to traditional simulations. This advancement enhances the scalability and efficiency of UHS simulations, offering real-time insights crucial for optimizing storage processes and advancing the practical deployment of UHS in the energy sector.