A question of capacity assessing CO₂ sequestration potential in Texas offshore lands

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Miller, Erin Noel

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The combustion of fossil fuels results in the release of carbon dioxide to the atmosphere, a known greenhouse gas. Evidence suggests that “most of the observed increase in global average temperatures…is very likely due to the observed increase in anthropogenic greenhouse gas concentrations” (IPCC, 2007). One solution currently being examined is carbon capture and storage (CCS). The advantage of CCS is that it does not require an actual reduction in the amount of carbon dioxide emissions created, but reduces emissions to the atmosphere by storing the greenhouse gases in the subsurface. Fundamentally, CCS works in the reverse of oil and gas production. Instead of extracting fluids from the subsurface, CCS injects carbon dioxide (CO2) into the pore spaces of developed oil and gas reservoirs, saline aquifers, or coal bed seams (Bachu, 2007), where it exists in a dense but low-viscosity phase (Supercritical state).
The Gulf Coast Carbon Center, based at the University of Texas at Austin’s Bureau of Economic Geology, is currently evaluating the State of Texas Offshore Lands (STOL) in the Gulf of Mexico (GOM) in order to evaluate the carbon-storage capacity in the state owned lands. “Capacity is defined as the volume fraction of the subsurface within a stratigraphic interval available for [CO2] sequestration” (Hovorka, 2004). There are a variety of methods currently used to calculate capacity. With so many options, how does a project decide which method to employ in determining capacity? This paper discusses the methods, presents an analysis of the benefits and drawbacks of the various methods, and develops a process for future projects to utilize in determining which methodology to employ. Additionally, storage capacity is calculated using the various methods presented, in order to compare the methods and understand their various advantages and drawbacks. Reservoir specific simulations are expected to predict smaller capacities in comparison to more broad static methods. This will provide end member predictions of capacity, shedding light on what can be expected in best case and worst case scenarios. The lessons learned from this study can be applied to future endeavors and formations all over the world.



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