3D grid based screening process for large-scale CO2 geological storage in Gunsan Basin, Yellow Sea, Korea

Abstract This study presents an approach to screen sedimentary basins for their CO2 geological storage potential based on a 3D grid with geological data. The 3D grid-based screening was applied to the Gunsan Basin, offshore Korea, for selecting potential sub-basins. Six sub-basins were recognized and prioritized using a set of quantifiable criteria, reflecting storage capacity, geological risk, and socio-economic aspects. Nine criteria were defined and weighed to reflect local priority and geological characteristics. Every grid cell was populated with geological and geometrical properties, scored and ranked for each criterion. Typically, the storage capacity is used for evaluating the storage potential of a basin, which, however, was not estimated here due to the low quantity of available data. Instead, the capacity potential was quantified by combining the pore volume and Gravitational Number for each grid cell. Mean score values for each sub-basin indicate that the East Sub-basin is the most promising region, containing a suitable aquifer with an estimated storage capacity of a few hundreds of MtCO2. Therefore, we suggest that the Gunsan Basin is suitable for implementing a CCS program with an injection rate of 4 MtCO2/year for 30 years. Moreover, we suggest that the 3D grid-based screening process could be used to quickly screen different sub-basins or potential aquifers by depth.