A Review of Intelligent Decision-Making Strategy for Geological CO2 Storage: Insights from Reservoir Engineering

Abstract

In a world characterized by a heavy reliance on fossil fuels, it becomes imperative to strike a harmonious balance between energy demands and carbon mitigation. This article delves into the practice of injecting carbon dioxide (CO2) into subsurface formations as a potent strategy for mitigating climate change. It underscores the critical role of dynamic modeling in addressing the challenges related to CO2 leakage throughout the life cycle of Geological CO2 Storage (GCS) projects, spanning pre-operational, operational, and post-operational phases. Barriers to implementing GCS are discussed, including challenges in high-fidelity modeling, multi-scale simulation, and economic justifications. State-of-the-art techniques with regard to numerical simulation, Data-Driven Modeling (DDM), and multi-objective optimization are comprehensively reviewed. Moreover, an intelligent modeling-optimization paradigm using artificial intelligence and machine learning (AI&ML) is proposed to formulate the optimal development plan during field-scale GCS/GCSU (Geological CO2 Storage and Utilization) projects. Successful case studies from the literature are surveyed, providing insights into the execution of the paradigm in real-world circumstances. Lastly, the paper concludes by outlining the existing challenges, emerging opportunities, and future directions for integrating intelligent modeling-optimization techniques into the decision-making processes of GCS/GCSU and conveying its potential application to the marching towards sustainable energy transition.