--> Evaluating Prospective Resource Estimates for CO2 Utilization and Storage in Unconventional Reservoirs of the Illinois Basin

Eastern Section Meeting

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Evaluating Prospective Resource Estimates for CO2 Utilization and Storage in Unconventional Reservoirs of the Illinois Basin

Abstract

The Advanced Coal Technology Consortium of the joint US-China Clean Energy Research Center is evaluating the potential to mitigate CO2 emissions from large point sources by implementing carbon capture, utilization and storage (CCUS) technology. One aspect of this research involves assessing CO2 storage and utilization resource potential for each of the four classes of geological reservoirs commonly considered for CCUS: deep saline formations, oil and gas reservoirs, unmineable coal seams, and organic-rich shales. Despite the potential economic benefits of CO2 utilization for enhanced recovery of hydrocarbons in unconventional reservoirs, CCUS resource assessments of coal seams and shale formations remain highly uncertain with relatively little field testing compared to conventional reservoirs.

This study focuses on evaluating CO2 utilization and storage resource estimates in the New Albany Shale (Devonian and Mississippian) and various unmineable coal seams in the Illinois Basin. The estimates are derived from varying approaches using multiple types of reservoir analyses and production data to refine the volumetric-based resource estimates, and to address the challenge of reducing uncertainty inherent in these estimates. A hierarchical approach is employed to help evaluate parameter uncertainty and sensitivity of resource estimates. Results are based on a case study analysis of fifteen different storage locations surrounding Duke Energy's Gibson Station power plant in southwest Indiana. Reservoir simulation is shown to be a key tool for gaining process-level insight and comparison to results from regional-scale analyses allowed for more refined resource estimates that underpin the development of a new integrated system-level model for optimizing CCUS deployment.