Year: 2025
Type: article
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 R2 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 R2 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 CO2 sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO2 storage performance in geological formations and for achieving global sustainability goals.
Source: Sustainability
DOI: 10.3390/su17167359
Authors: Yueqian Cao, Zhikai Liang, Meiqin Che, Jieqiong Luo, Youwen Sun
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7087570788