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A HIERARCHICAL MODELING FRAMEWORK FOR GEOLOGICAL STORAGE OF CARBON DIOXIDE
Impact/Purpose:
We seek to develop practical, physically-based models of carbon dioxide movement in the subsurface, associated with injection of captured CO2 as part of a Carbon Capture and Storage (CCS) strategy to reduce CO2 emissions to the atmosphere. These models will capture the important features of the system while providing computational tractability and allowing probabilistic risk assessment calculations associated with possible leakage of CO2 and displaced brine into drinking water aquifers.
We seek to develop practical, physically-based models of carbon dioxide movement in the subsurface, associated with injection of captured CO2 as part of a Carbon Capture and Storage (CCS) strategy to reduce CO2 emissions to the atmosphere. These models will capture the important features of the system while providing computational tractability and allowing probabilistic risk assessment calculations associated with possible leakage of CO2 and displaced brine into drinking water aquifers.
Description:
Carbon Capture and Storage, or CCS, is likely to be an important technology in a carbonconstrained world. CCS will involve subsurface injection of massive amounts of captured CO2, on a scale that has not previously been approached. The unprecedented scale of this operation leads to very large spatial perturbations of the subsurface involving both the plume of injected CO2 and the associated pressure perturbation. Protection of drinking water supplies requires understanding of the overall evolution of the this flow and transport system, with a focus on leakage pathways that could allow either CO2 or displaced brine to migrate to shallow drinking water formations. Leakage pathways may involve relatively small features whose properties are highly uncertain – an example is the many millions of old oil and gas wells in the parts of North America that are most suitable for CO2 storage. When the range of scales is considered, in combination with the uncertainties associated with both the critical leakage pathways and the standard geological parameters, meaningful risk assessment calculations become a severe computational challenge. Traditional models of multi-component, multi-phase flow and transport are poorly suited to solve these problems because they are unable to provide sufficient computational efficiency to allow a probabilistic risk assessment to be performed. We have developed a set of analytical and semi-analytical solutions to model CO2 injection and potential leakage associated with injection into deep subsurface formations where multiple potentially leaky wells may exist. While these models provide important insights into system behavior, they are constrained by fairly strong assumptions about the system. If we consider the entire spectrum of modeling approaches, the traditional multicomponent multi-phase models tend to reside at the 'most complex' end of the spectrum, while our analytical solutions are close to the 'most simplified' models. In the work proposed herein, we seek to extend the analytical and semi-analytical models as far into the 'more complex' range of the spectrum as is possible, and then to develop a set of models of intermediate complexity that will provide a much richer set of options for CO2 modeling. These intermediate models will be based on numerical, rather than analytical, solutions, but instead of solving the full set of traditional multi-component multi-phase equations we will apply a set of successively more strict assumptions to allow for computational tractability. In addition, we will propose and test new numerical algorithms that will allow for 'hybrid' models that combine the most attractive attributes of the numerical and analytical solutions. The overall result will be a 'hierarchical modeling framework' in which a range of models of different complexity can be used to produce a realistic analysis of a proposed injection. All of this will be done with probabilistic risk assessment as a driving motivation, with outputs that include probability estimates for leakage and assessment of leakage patterns into critical regions like drinking water aquifers. This provides the input necessary to analyze impacts to shallow drinking water zones and to design effective monitoring technologies and strategies. To assess the impacts of different simplifications, we will compare our model results to results from more traditional reservoir simulations.