Characterization of a fluvial deltaic salt-influenced system for CO2 storage using seismic poststack 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 southeastern 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 17 wells in the southeastern Louisiana Gulf Coast. The area is structurally complex with salt domes and numerous faults. Shelf-edge deltas are the primary sediment input for the target reservoirs, and these fluvial-deltaic sediments provide high porosity and permeable sands. The workflow leverages self-organized maps (SOMs) to identify zones of interest laterally and vertically for carbon storage. SOM can be computed quickly for screening and provides an uplift in resolution for interpretation compared with single attributes. A model-based acoustic impedance inversion produces porosity information and improved resolution that can be used in conjunction 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 ft gross thickness. The research highlights a project sized target within the middle Miocene containing 16, 24, and 52 megatons (P10, P50, P90) of estimated CO2 storage. Using SOM and inversion maps together is an effective way for investigating an area for carbon storage targets.