This work introduces a novel application of the Algebraic Dynamic Multilevel (ADM) method for simulating CO2 storage in deep saline aquifers. By integrating a fully implicit coupling strategy, fully compositional thermodynamics, and adaptive mesh refinement, the ADM fr
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This work introduces a novel application of the Algebraic Dynamic Multilevel (ADM) method for simulating CO2 storage in deep saline aquifers. By integrating a fully implicit coupling strategy, fully compositional thermodynamics, and adaptive mesh refinement, the ADM framework effectively models phenomena such as buoyancy-driven migration, convective dissolution, and phase partitioning under various subsurface conditions. The method starts with the construction of a hierarchy of multilevel grids and the generation of localized multiscale basis functions, which account for heterogeneities at each coarse level. During the simulation, the ADM method dynamically refines areas with significant overall CO2 mass fraction gradients while coarsening smooth regions, thus optimizing computational resources without compromising the accuracy required to capture essential flow and transport characteristics. This dynamic grid adjustment is facilitated by algebraic prolongation and restriction operators, which map the fine-scale system onto a coarser grid suited to the evolving distribution of the CO2 plume. This feature allows the ADM to navigate various coarsening thresholds efficiently, striking a trade-off between computational economy and detailed simulation accuracy. Even at relatively higher thresholds, key trapping mechanisms are captured with sufficient detail for quantification. These capabilities make the ADM framework well suited for long-term CO2 sequestration in highly heterogeneous reservoirs, where large-scale models may otherwise become impractically expensive, offering a practical balance between the need for detailed simulations and manageable computational requirements. Overall, the ADM framework proves to be a robust tool for designing, monitoring, and analyzing large-scale CO2 storage operations, supporting reliable and cost-effective solutions in carbon management.