Advanced Deep Learning Networks for CO 2 Trapping Analysis in Geological Reservoirs

Yueqian Cao, Zhikai Liang, Meiqin Che, Jieqiong Luo and Youwen Sun ()
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Yueqian Cao: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Zhikai Liang: School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China
Meiqin Che: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Jieqiong Luo: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Youwen Sun: Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

Sustainability, 2025, vol. 17, issue 16, 1-16

Abstract: As global temperatures continue to rise, surpassing the +2.5 °C threshold under current emissions scenarios, the urgency for sustainable, effective carbon management strategies has intensified. Geological carbon storage (GCS) has been explored as a potential mitigation tool; however, its large-scale feasibility remains highly uncertain due to concerns over storage permanence, leakage risks, and economic viability. This study proposes three advanced deep learning models—DeepDropNet, GateSeqNet, and RecurChainNet—to predict the Residual Trapping Index (RTI) and Solubility Trapping Index (STI) with enhanced accuracy and computational efficiency. Using a dataset of over 2000 high-fidelity simulation records, the models capture complex nonlinear relationships between key reservoir properties. Results indicate that GateSeqNet achieved the highest predictive accuracy, with an R 2 of 0.95 for RTI and 0.93 for STI, outperforming both DeepDropNet and RecurChainNet. Ablation tests reveal that excluding post injection and injection rate significantly reduced model performance, decreasing R 2 by up to 90% in RTI predictions. The proposed models provide a computationally efficient alternative to traditional numerical simulations, which makes them viable for real-time CO 2 sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO 2 storage performance in geological formations and for achieving global sustainability goals.

Keywords: geological carbon storage; deep learning; residual trapping index; solubility trapping index; CO 2 sequestration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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