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

摘要

CO_2 injection into deep saline aquifers is a preferred method for mitigating CO_2 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 CO_2 storage capacity of a deep saline aquifer. In order to create a predictive model, the ranges of variables that affect the CO_2 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 CMC STARS. The simulation period covered a total of 300 years of CO_2 storage. By using a least-squares method, linear and nonlinear predictive models were developed to estimate CO_2 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 CO_2 storage capacity is depth. Most of the (up to 90%) injected gas dissolved into the formation water and a negligible amount of CO_2 reacted with carbonate.