--> Abstract: Uncertainty Analysis of Storage Resource Estimates for CO2 Sequestration in Saline Formations, by Olga H. Popova, Sean T. McCoy, Bobby Karimi, Mitchell Small Small, Andrew C. Thomas, and M. Granger Morgan; #90124 (2011)

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AAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA

Uncertainty Analysis of Storage Resource Estimates for CO2 Sequestration in Saline Formations

Olga H. Popova1; Sean T. McCoy1; Bobby Karimi4; Mitchell Small Small2; Andrew C. Thomas3; M. Granger Morgan1

(1) Department of Engineering and Public Policy, Canegie Mellon University, Pittsburgh, PA.

(2) Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA.

(3) Department of Statistics, Carnegie Mellon University, Pittsburgh, PA.

(4) Department of Geology and Planetary Science, University of Pittsburgh, Pittsburgh, PA.

Consensus on the need to address climate change is emerging, and carbon capture and storage (CCS) is a technology that provides a near-term solution to reduce carbon dioxide (CO2) emissions to the atmosphere and reduce our impact on the climate system.

While, previous research has suggested that the sequestration resource is very large (on the order of hundreds of years of CO2 emissions to be sequestered in the US) there is a large range in recent storage resource estimates for the same regions. As with all natural resources, the amount of the resource that can be used is likely to be far less than the ideal maximum when technical and economic limitations are considered. In addition to this, the uncertainty in resource assessments also contributes to risk associated with making a decision to invest in development of CO2 sequestration infrastructure.

This study focuses on the resource in deep saline-filled formations (DSFs) because this class of CO2 repositories is believed to make up the bulk of the sequestration resource. The purpose of this study is to quantify storage resource for CO2 and associated uncertainty using a geological model of the Pennsylvania part of the Appalachian sedimentary basin. The geologic framework of the model is based on information provided by the Bureau of Topographic & Geologic Survey of the Pennsylvania State.

The results of statistical studies of reservoir and structural properties of the Oriskany sandstone suggest the best fit distributions for three parameters: porosity, thickness, and depth. The regression analysis allows prediction of (1) porosity values for well-sites with only depth and thickness measurements, (2) formation temperature, and (3) formation pressure. The equation of state developed by Span and Wagner is used for calculation of CO2 density as a function of temperature and pressure, which in turn are functions of depth. Next, uncertainty analysis is performed using Monte Carlo simulation for storage resource estimates. Four parameters are treated as uncertain: depth, porosity, pressure and temperature.

The results of the simulation show that there is a large variation in storage resource estimates and that a point estimate using the mean values of input parameters does not result in the same capacity as the mean of the simulated storage resource distribution. Sensitivity analysis indicates that the two parameters that contribute the most to the uncertainty in estimates are porosity and temperature.