A Holistic Framework for Optimizing CO 2 Storage: Reviewing Multidimensional Constraints and Application of Automated Hierarchical Spatiotemporal Discretization Algorithm

Ismail Ismail, Sofianos Panagiotis Fotias and Vassilis Gaganis ()
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Ismail Ismail: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15772 Athens, Greece
Sofianos Panagiotis Fotias: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15772 Athens, Greece
Vassilis Gaganis: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15772 Athens, Greece

Energies, 2025, vol. 18, issue 22, 1-41

Abstract: 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 CO 2 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 CO 2 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 CO 2 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.

Keywords: Carbon Capture and Storage; well-control strategies; well injection schedules; multidimensional constraints; reservoir simulation; carbon storage; field optimization; storage site (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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