To mitigate dangerous climate change effects, the 195 countries that signed the 2015 Paris Agreement agreed to “keep the increase in average global surface temperature below 2 °C and limit the increase to 1.5 °C” by reducing carbon emissions. One promising option for reducing carbon emissions is the deployment of carbon capture, utilization, and storage technologies (CCUS) to achieve climate goals. However, for large-scale deployment of underground carbon storage, it is essential to develop technically sound, safe, and cost-effective CO₂ injection and well control strategies. This involves sophisticated balancing of various factors such as subsurface engineering policies, technical constraints, and economic trade-offs. Optimization techniques are the best tools to manage this complexity and ensure that CCUS projects are economically viable while maintaining safety and environmental standards. This work reviews thoroughly and critically carbon storage studies, along with the optimization of CO₂ injection and well control strategies in saline aquifers. The result of this review provides the foundation for carbon storage by outlining the key subsurface policies and the application of these policies in carbon storage development plans. It also focusses on examining applied optimization techniques to develop CO₂ injection and well control strategies in saline aquifers, providing insights for future work and commercial CCUS applications.

Carbon Capture and Storage (CCS) is a critical strategy for reducing CO₂ emissions from hard-to-abate sectors. Reliable and efficient reservoir simulation tools are essential for supporting the safe and effective deployment of CCS projects. This study presents a twofold contribution to CCS modeling in saline aquifers: (1) the validation of the Black Oil Model (BoM) as a computationally efficient alternative to compositional simulators, and (2) a systematic assessment of the impact of grid resolution on plume prediction accuracy. The BoM was benchmarked against three commercial compositional simulators—Eclipse E300, CMG-GEM, and TNavigator. The comparison focused on key aspects of CO₂ storage operations, including plume evolution to assess containment and storage security, as well as injection safety and efficiency through pressure and saturation profile analysis, evaluated across both the injection and the post-closure monitoring phases. The BoM successfully reproduced plume extent and CO₂ saturation distributions, with mean deviations of 3% during injection, 5% during post-closure, and an overall average of 4% across the entire project duration. Additionally, simulation times were reduced by a factor of four compared to compositional models. These results confirm the BoM’s practical utility as a robust and efficient tool for CO₂ storage simulation. In parallel, the study investigated the influence of vertical and lateral grid resolutions/coarsening on the accuracy of CO₂ modeling. Seven models were developed and evaluated using a hybrid qualitative–quantitative framework, consistent with the BoM validation methodology. Vertical resolution was found to be particularly critical during the monitoring phase. While a 5 m resolution proved adequate during injection, deviations in plume shape and magnitude during post-injection increased to an average of 15% compared to a fine 2 m vertical resolution model, highlighting the necessity of fine vertical discretization (≤2 m) to capture gravity-driven plume dynamics during the monitoring phase. Conversely, lateral grid resolution had a stronger effect during the injection phase. A lateral cell size of 150 m was required for accurate plume prediction, with 200 m remaining moderately acceptable for early-phase assessment and prospect ranking, whereas coarser lateral grids led to significant underestimation of plume spread and dissolution extent. These findings demonstrate that the BoM, when combined with informed grid resolution strategies, enables accurate and computationally efficient simulation of CO₂ storage in saline aquifers. The study provides practical guidelines for fluid model selection and spatial discretization, offering critical input to subsurface experts involved in CCS project development, monitoring design, and regulatory compliance.

Carbon capture and storage technologies play a crucial role in mitigating climate change by capturing and storing carbon dioxide emissions underground. Saline aquifers, among other geological formations, hold promise for long-term CO₂ storage. However, accurately assessing their storage capacity and CO₂ behavior underground necessitates advanced numerical simulation and modeling techniques. In this study, we introduce an approach based on a solubility thermodynamic model that leverages cubic equations of state offline from the simulator. This approach enables the precise prediction of CO₂–brine equilibrium properties and facilitates the conversion of compositional data into black oil PVT data suitable for black oil simulations. By incorporating industry-scale saline aquifer properties, we simulate a carbon storage scheme using the black oil model technique, significantly reducing computation time by at least four times while preserving the essential physical phenomena observed in underground carbon storage operations. A comparative analysis between black oil and compositional simulations reveals consistent results for reservoir pressure, CO₂ saturation distributions, and mass fraction of trapping mechanisms, with differences of less than 4%. This validation underscores the reliability and efficiency of integrating the black oil model technique into carbon storage simulations in saline aquifer formations, offering tangible benefits to industry operators and regulators by striking a balance between accuracy and efficiency. The capability of this approach to extend to temperatures of up to 300 °C and pressures of up to 600 bars broadens its applicability beyond conventional CCS applications, serving as a valuable tool for optimizing decision-making processes in CCS projects, particularly in scenarios where profitability may be marginal.

The accurate prediction of pressure and saturation distribution during the simulation of CO₂ injection into saline aquifers is essential for the successful implementation of carbon sequestration projects. Traditional numerical simulations, while reliable, are computationally expensive. Machine learning (ML) has emerged as a promising tool to accelerate these simulations; however, challenges remain in effectively capturing complex reservoir dynamics, particularly in regions experiencing rapid changes in pressure and saturation. This article addresses the challenges by introducing a fully automated, data-driven ML classifier that distinguishes between regions of fast and slow variation within the reservoir. Firstly, we demonstrate the variability in pressure across different reservoir grid blocks using a simple brine injection and production scenario, highlighting the limitations of conventional acceleration approaches. Subsequently, the proposed methodology leverages ML proxies to rapidly and accurately predict the behavior of slow-varying regions in CO₂ injection simulations, while traditional iterative methods are reserved for fast-varying areas. The results show that this hybrid approach significantly reduces the computational load without compromising on accuracy. This provides a more efficient and scalable solution for modeling CO₂ storage in saline aquifers.

To combat the detrimental impacts of climate change and meet the obligations outlined in the 2015 Paris Agreement, Carbon Capture, Utilization, and Storage (CCUS) has emerged as a crucial technology with significant potential for achieving climate targets. CCUS involves the capture, storage, and utilization of carbon dioxide (CO₂) emissions from existing energy infrastructure, hard-to-abate industries, or directly from the atmosphere, presenting a promising solution for emission reduction. However, fully harnessing the benefits of carbon storage requires the development of technically robust, safe, and cost-effective well control strategies that align with fundamental subsurface policies. This study aims to offer a comprehensive reference guide for carbon storage applications by reviewing relevant research in the field and establishing key subsurface storage policies for carbon storage in saline aquifer formation along with their practical implementation in carbon storage development plans. Additionally, we explore the utilization of optimization techniques employed thus far in the development of effective well control strategies in saline aquifers, presenting mathematical tools utilized and the achieved results.

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.