Transient pressure prediction in large-scale underground natural gas storage: A deep learning approach and case study

Large-scale underground natural gas storage (UNGS) facilities typically allocate only one month annually for well shut-in to facilitate pressure build-up. This limited well shut-in period affects the subsequent pressure measurements and evaluation of gas storage capacity. This study proposes a novel workflow to directly predict the transient pressure behavior of UNGS during pressure build-up without requiring additional well shut-in operations. The proposed workflow uses the gas injection-withdrawal rate as a dynamic input feature for a deep learning model, with reservoir pressure as the output feature. An enhanced WA-BiLSTM model integrates multiple physical mechanisms and advanced optimization algorithms. The model achieves mean squared error (MSE) of 2 × 10-3, which is less than 5 % of traditional model's results. Field data from the largest Hutubi UNGS exhibit a prediction accuracy of 99.0 % during the well shut-in phase. High-precision prediction results with low MSE ensure the reliability of pressure derivative data. By analyzing the predicted pressure data, the actual gas storage volume is 104.75 × 108 m3, accounting for 97.9 % of the facility's designated storage capacity.