CHARACTERIZATION OF A FLUVIAL DELTAIC SALT-INFLUENCED SYSTEM FOR CO2 STORAGE USING SEISMIC POST-STACK INVERSION AND SELF-ORGANIZING MAPS IN THE UPPER MIOCENE, ONSHORE LOUISIANA

Abstract

Basins that have historically been explored for hydrocarbons are now seen through a new perspective for carbon capture and storage. Legacy hydrocarbon reservoirs may now have the potential to become carbon storage targets. The Miocene Sandstone in south Louisiana is a stratigraphic interval that contains favorable zones for carbon storage, and seismic interpretation is a critical tool for characterizing these sands and estimating the amount of possible carbon storage. New workflows leveraging machine learning and seismic inversion can be applied to legacy fields to generate a new estimate for pore space and carbon storage capacity. The study area contains a 3D seismic survey covering 310 mi2 (803 km2) and 16 wells. Using these data, the workflow leverages self-organized maps (SOM) to identify zones of interest both laterally and vertically for carbon storage. SOM provides an uplift in resolution for interpretation compared to single attributes. A model-based acoustic impedance inversion produces porosity information that is used in tandem with SOM outputs. The acoustic impedance results strongly correlate to porosity within the target sands, and the SOM and porosity maps delineate specific sand lobes for potential storage. Wellbore data within the sand lobe align with SOM and inversion results and show rock quality with 26% average porosity and 90 feet gross thickness. The research highlights a project sized target within the middle Miocene containing 10.3 Megatons of estimated CO2 storage. Using SOM and inversion maps in tandem is an effective way for screening an area for carbon storage targets.