A coupling alternative to reactive transport simulations for long-term prediction of chemical reactions in heterogeneous CO₂ storage systems

Abstract. Fully coupled, multi-phase reactive transport simulations of CO₂ storage systems can be approximated by a simplified one-way coupling of hydrodynamics and reactive chemistry. The main characteristics of such systems, and hypotheses underlying the proposed alternative coupling, are (i) that the presence of CO₂ is the only driving force for chemical reactions and (ii) that its migration in the reservoir is only marginally affected by immobilisation due to chemical reactions. In the simplified coupling, the exposure time to CO₂ of each element of the hydrodynamic grid is estimated by non-reactive simulations and the reaction path of one single batch geochemical model is applied to each grid element during its exposure time. In heterogeneous settings, analytical scaling relationships provide the dependency of velocity and amount of reactions to porosity and gas saturation. The analysis of TOUGHREACT fully coupled reactive transport simulations of CO₂ injection in saline aquifer, inspired to the Ketzin pilot site (Germany), both in homogeneous and heterogeneous settings, confirms that the reaction paths predicted by fully coupled simulations in every element of the grid show a high degree of self-similarity. A threshold value for the minimum concentration of dissolved CO₂ considered chemically active is shown to mitigate the effects of the discrepancy between dissolved CO₂ migration in non-reactive and fully coupled simulations. In real life, the optimal threshold value is unknown and has to be estimated, e.g. by means of 1-D or 2-D simulations, resulting in an uncertainty ultimately due to the process de-coupling. However, such uncertainty is more than acceptable given that the alternative coupling enables using grids of the order of millions of elements, profiting from much better description of heterogeneous reservoirs at a fraction of the calculation time of fully coupled models.