The growing severity of global climate change has highlighted the importance of CO₂ sequestration as a key strategy for reducing CO₂ emissions and mitigating global warming. To this end, sedimentary basins worldwide contain extensive yet underexplored saline aquifers with substantial sequestration potential for long-term CO₂ sequestration. In this study, the suitability and mechanical responses of CO₂ sequestration in a representative half-graben saline aquifer were systematically unraveled through integrated theoretical analysis and multi-physics-coupled numerical simulations. Key factors, such as temperature, pressure, reservoir properties, and caprock distribution, were evaluated based on well logging and mud logging data. Taking the evaluation results as a basis, optimal reservoir-caprock combinations were identified and classified into three types according to their spatial distribution: Single caprock-reservoir, lower interlayer-caprock-reservoir, and upper interlayer-caprock-reservoir. To simulate the mechanical responses during CO₂ injection and sequestration, corresponding conceptual models were developed. The results indicate that Type III reservoir-caprock combinations, featuring upper mudstone interlayers, exhibit the lowest caprock stress, reduced leakage risk and enhanced sequestration security, which should be prioritized in sequestration site selection. Our findings provide valuable insights for selecting safe and effective CO₂ sequestration sites in saline aquifers across regional sedimentary basins. Document Type: Original article Cited as: Wu, S., Chen, Y., Zhou, X., Chen, Q., Zhu, L., Wang, L. Suitability evaluation of CO₂ sequestration in saline aquifers: Insights from regional basin studies. Advances in Geo-Energy Research, 2026, 20(1): 43-55. https://doi.org/10.46690/ager.2026.04.04

In ultra-deep drilling environments, downhole measurement tools often fail or cannot be deployed due to extreme high-temperature and high-pressure (HPHT) conditions. Consequently, mud-logging data become one of the few reliable real-time information sources for evaluating drilling performance and identifying abnormal conditions. This study proposes a data-driven framework for automatic identification of drilling operation statuses using machine learning, with a particular focus on ultra-deep and HPHT wells. A support vector machine (SVM)-based classification workflow was established to recognize nine representative drilling operation statuses from mud-logging data. Through systematic model optimization, the proposed method achieved a classification accuracy of 91.33%. By incorporating a sliding window-based time-series optimization strategy, the overall accuracy was further improved to 95.22%, while the recognition accuracy of HPHT-related operations increased from 77.67% to 89.33%. These results demonstrate that the optimized model possesses strong adaptability and stability under extreme HPHT conditions. This study specifically targets HPHT environments with limited downhole data and incorporates time-series optimization to enhance model robustness. The proposed framework provides a reliable approach with potential for generalization for intelligent operation recognition in ultra-deep drilling, supporting real-time decision-making and improving operational safety and efficiency in challenging environments.