--> Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates: Cambrian-Ordovician Units within the MRCSP Region

AAPG Eastern Section Meeting

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Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates: Cambrian-Ordovician Units within the MRCSP Region

Abstract

The Midwest Regional Carbon Sequestration Partnership (MRCSP) aims to study the regional distribution and geologic storage suitability of units within the Cambrian-Ordovician sequences, including the Knox Supergroup, St. Peter Sandstone, Trenton and Lexington Limestones, and equivalent units across the MRCSP region.

To date, we have compiled a comprehensive data set of wireline logs and petrophysical information that include core analysis for porosity and permeability and mercury injection capillary pressure (MICP) analyses. Using these data, carbon storage resource estimates (SRE) are evaluated using a hierarchical approach that addresses uncertainty in the estimates by incorporating different models of formation porosity based on a series of increasingly complex portrayals of the pore system. The simplest analysis follows the USDOE methodology whereby a SRE is calculated using a single value for porosity in the assessed formation. Additional estimates follow the same general methodology but employ increasingly precise spatially variable porosity models based on formation diagenesis (depth-dependent function), reservoir suitability (effective porosity), distinct petrofacies (advanced reservoir characterization), and multiple realizations of porosity using data-driven geostatistical methods.

Results from this hierarchical approach help illuminate the magnitude of uncertainty that should be expected in SREs as a function of data availability and the level of reservoir characterization that is achievable for a given formation. A semi-probabilistic SRE calculation methodology using Monte Carlo simulations to create models for porosity generally tends to underestimate the range of uncertainty in storage resource. Conceivably, the higher the order model, the lower the uncertainty in the SRE. Ongoing research is investigating whether improved precision implicit in higher orders of the hierarchy are generating more accurate estimates of storage volumes.