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

Hongyang Chu, Liang Zhang, Huimin Lu, Danyang Chen, Jianping Wang, Weiyao Zhu and W. John Lee

Energy, 2024, vol. 311, issue C

Abstract: 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.

Keywords: Underground natural gas storage; Transient pressure behavior; Case study; BiLSTM; Whale optimization algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031876

DOI: 10.1016/j.energy.2024.133411

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