As a rapidly evolving technology, carbon capture and storage (CCS) can potentially lower the levels of greenhouse gas emissions from the oil and gas industry. This paper provides a comprehensive review of different aspects of CCS technology, including its key components, the methods and stages of carbon storage, implied environmental effects, and its pros and cons. This paper also investigates the utilization of CCS as an alternative method to water injection into oil reservoirs. It also probes the technical and operational challenges of implementing CCS technology in the oil and gas industry. Additionally, this paper examines the regulatory and policy issues associated with CCS, including incentives and frameworks for promoting the deployment of CCS technology. Finally, in this paper the potential benefits of CCS are discussed, including reducing the carbon footprint of the oil and gas industry, enhancing energy security, and supporting the transition to a low-carbon economy.

This study focuses on predicting CO₂ solubility in imidazolium-based ionic liquids using deep learning models with input parameters of critical pressure, critical temperature, molecular weight, and acentric factor. The models evaluated include Bayesian Neural Networks (BNN), Deep Neural Networks (DNN), Gradient Boosting Neural Networks (GrowNet), Tabular Neural Networks (TabNet), Random Forest (RF), and Support Vector Regression (SVR). The results were compared with two PC-SAFT models, namely cQC-PC-SAFT-MSA (1) and cQC-PC-SAFT-MSA (2), where deep learning models performed better than SAFT models. Graphical and statistical analyses revealed that the GrowNet model, with a root mean square error of 0.0073 and a coefficient of determination of 0.9962, exhibited the lowest error compared to other models. In addition, Pearson correlation coefficient (PCC) and Shapley additive description (SHAP) analyses highlighted pressure (P) as a key parameter determining CO₂ solubility in imidazolium-based ionic liquids, significantly contributing to model performance.