Integrated Geochemical Modeling of Trapping Mechanisms and Sensitivity Analysis for Optimized CO(2) Sequestration in Saline Aquifers

综合地球化学模型研究捕集机制及敏感性分析,以优化盐水层中二氧化碳的封存

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Abstract

Geological carbon sequestration (GCS) in deep saline aquifers is a critical climate mitigation strategy, but its long-term effectiveness depends on the combined impact of structural, residual, solubility, and mineral trapping mechanisms. Despite substantial research done on individual trapping processes, integrated evaluations under realistic reservoir conditions remain limited, reducing the predictive reliability of CO(2) storage performance. This study develops a high-resolution, 3D compositional model of the Strawn formation in northeast King County, Texas, using CMG-GEM software to investigate coupled physical and geochemical trapping processes. Unlike the previous studies, this model couples all four trapping mechanisms in a single framework, providing a quantitative and mechanistic understanding of CO(2) fate in the subsurface. The validation of the model grid was performed using grid sensitivity analysis on selected performance parameters. A comparative analysis is presented between two-dimensional and three-dimensional modeling approaches to highlight the importance of spatial dimensionality in accurately capturing CO(2) plume dynamics and assessing storage efficiency. Results show that solubility trapping, when effectively combined with structural and residual mechanisms, yields the highest amount of CO(2) sequestered (43%), followed by mineral trapping (40%). A comprehensive sensitivity analysis, conducted on factors affecting such solubility trapping, demonstrates that the efficiency of the integrated trapping increases with higher injection rates, prolonged injection years, higher porosity, elevated pressures, lower temperatures, and reduced vertical and horizontal permeabilities. These findings offer crucial insights for optimizing reservoir and operational parameters to improve containment and long-term storage. The integrated modeling approach presented here offers a robust and transferable framework for evaluating and designing site-specific carbon storage systems on a scale.

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