A Holistic Framework for Optimizing CO2 Storage: Reviewing Multidimensional Constraints and Application of Automated Hierarchical Spatiotemporal Discretization Algorithm

Climate change mitigation demands scalable, technologically mature solutions capable of addressing emissions from hard-to-abate sectors. Carbon Capture and Storage (CCS) offers one of the few ready pathways for deep decarbonization by capturing CO2 at large point sources and securely storing it in deep geological formations. The long-term viability of CCS depends on well control strategies/injection schedules that maximize storage capacity, maintain containment integrity, ensure commercial deliverability and remain economically viable. However, current practice still relies heavily on manual, heuristic-based well scheduling, which struggles to optimize storage capacity while minimizing by-products such as CO2 recycling within the high-dimensional space of interdependent technical, commercial, operational, economic and regulatory constraints. This study makes two contributions: (1) it systematically reviews, maps and characterizes these multidimensional constraints, framing them as an integrated decision space for CCS operations, and (2) it introduces an industry-ready optimization framework—Automated Optimization of Well control Strategies through Dynamic Time–Space Discretization—which couples reservoir simulation with constraint-embedded, hierarchical refinement in space and time. Using a modified genetic algorithm, injection schedules evolve from coarse to fine resolution, accelerating convergence while preserving robustness. Applied to a heterogeneous saline aquifer model, the method was tested under both engineering and financial objectives. Compared to an industry-standard manual schedule, optimal solutions increased net stored CO2 by 14% and reduced recycling by 22%, raising retention efficiency to over 95%. Under financial objectives, the framework maintained these technical gains while increasing cumulative cash flow by 23%, achieved through leaner, smoother injection profiles that minimize costly by-products. The results confirm that the framework’s robustness, scalability and compatibility with commercial simulators make it a practical pathway to enhance CCS performance and accelerate deployment at scale.