Impact of Grid Resolution on Accuracy of In-Situ CO2 Modeling for Improved CCS Project Design and Implementation
Authors: Ismail I. , Fotias S. P. , Tartaras E. , Stefatos A. , Gaganis V. 
Published: October 2024

Carbon Capture and Storage (CCS) is pivotal in reducing CO2 emissions from hard-to-abate industrial sectors, thereby supporting net-zero targets and advancing the hydrogen-based economy. As the demand for CO2 storage permits rises and CCS projects progress through various design and development stages, accurately predicting subsurface CO2 dynamics—such as plume behavior, field pressure, and trapping mechanisms—becomes critical. The complexity of CCS in saline aquifers is exacerbated by challenges such as sparse data, parameter uncertainties, and intricate subsurface geology. While advancements in data acquisition and interpretation have improved subsurface understanding, sophisticated numerical simulations, including computational fluid dynamics (CFD) that integrate geo-chemical and geo-mechanical effects, are indispensable for guiding project design and implementation. These simulations support crucial decisions for operators and storage developers, including the optimization of injection strategies and facility design, and they are essential for regulatory authorities tasked with evaluating risk assessment plans and monitoring protocols to ensure compliance with environmental and safety standards.

Key outputs influencing CCS project design and implementation include defining CO2 plume extent and conformance, as well as the distribution of pressure, saturation, and trapping mechanisms. Accurate delineation of the CO2 plume and identification of trapping mechanisms—particularly in distinguishing high-risk (e.g., free CO2) from low-risk (e.g., dissolved CO2) states—are vital for establishing the area of review and assessing leakage risks through potential pathways such as abandoned wellbores or faults. These factors directly influence financial security measures and the preparedness of state interventions for remediation and restoration. Additionally, understanding CO2 movement and pressure dynamics is crucial for determining effective storage capacity, developing appropriate injection strategies, and maintaining storage integrity. Precision in reservoir simulation is of paramount importance, as inaccuracies could jeopardize the success and safety of CCS projects. While fine-scale models offer detailed representations of in-situ CO2, they are computationally intensive, particularly in saline aquifers that require multiple realizations and complex tasks like well allocation and scenario development. To expedite simulations, operators often employ upscaling techniques with coarser grid resolutions leading to predictions which might endanger critical predictions such as the reservoir storativity and, most important, the containment risks assessment. Nevertheless, research on the impacts of upscaling on in-situ CO2 geological storage modeling remains limited.

This study investigates the impact of upscaling on in-situ CO2 modeling by comparing several coarse models of varying vertical resolution against a fine-scale model, using an integrative qualitative and quantitative evaluation criterion. Our findings reveal significant variations in CO2 plume dynamics, field pressures, and trapping mechanisms between coarse and fine-scale models. The results highlight the critical trade-off between computational efficiency and predictive accuracy, demonstrating that the grid resolution required for precise CO2 plume simulation is finer than typically employed, especially when the scenarios evaluated are on the storativity limits. Furthermore, the study underscores the risks associated with overly coarse resolutions, which can lead to erroneous predictions and compromise project integrity. By advocating for finer grid resolutions, this work provides essential insights for site developers, operators, and regulators, enhancing CCS operational safety and efficiency, and contributing to more reliable in-situ CO2 modeling strategies.