Development of a linear predictive model for carbon dioxide sequestration in deep saline carbonate aquifers

CO₂ injection into deep saline aquifers is a preferred method for mitigating CO₂ emission. Although deep saline aquifers are found in many sedimentary basins and provide very large storage capacities, several numerical simulations are needed before injection to determine the storage capacity of an aquifer. Since numerical simulations are expensive and time-consuming, using a predictive model enables quick estimation of CO₂ storage capacity of a deep saline aquifer. In order to create a predictive model, the ranges of variables that affect the CO₂ storage capacity were determined from published literature data. Correlations found in literature were used for other important parameters such as pore volume compressibility and density of brine. Latin hypercube space filling design was used to construct 100 simulation cases prepared using CMG STARS. The simulation period covered a total of 300 years of CO₂ storage. By using a least-squares method, linear and nonlinear predictive models were developed to estimate CO₂ storage capacity of deep saline carbonate aquifers. Numerical dispersion effects were considered by decreasing the grid dimensions. It was observed that a dimensionless linear predictive model is better than the nonlinear. The sensitivity analyses showed that the most important parameter that affects CO₂ storage capacity is depth. Most of the (up to 90%) injected gas dissolved into the formation water and a negligible amount of CO₂ reacted with carbonate.