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Scale-up of reactive flow through network flow modeling

Posted on:2009-05-13Degree:Ph.DType:Dissertation
University:State University of New York at Stony BrookCandidate:Kim, DaesangFull Text:PDF
GTID:1442390002990982Subject:Mathematics
Abstract/Summary:
Pore-throat networks of porous rock samples are constructed from analyses of 3D X-ray computed micro-tomographic (CMT) images of three rock core samples taken from the Viking field in the Alberta basin. The networks are extended to network flow models in order to characterize the properties of reactive flows through porous media. New to both the CMT and network flow work is the extraction of four material phases: the void phase; kaolinite; quartz; and "minerals of interest". Thus, the segmented images contain information on mineral abundances and accessibilities of the four phases: cluster sizes; accessible surface areas; size and area distributions. The standard network flow model is extended to include the mineral distribution network for computation of reactive flow.;Reactions are chosen to simulate precipitation and dissolution reactions that may accompany CO2 sequestration. The minerals of interest are assumed to be anorthite. The reactive model includes both kinetic and instantaneous reaction components. The reaction rates for kinetic components are integrated over each time step, and the equilibrium condition for the instantaneous components is satisfied at every time step.;The reactive flow model is applied to the Viking samples. The simulation results show that there are differences from previously reported results in the literature. Small reactive surface areas of anorthite result in a slow change in the kaolinite reaction rate; the time to reach a steady state of the kaolinite reaction is on the order of 103 seconds. The anorthite reaction rate depends only on pH because of the small values of saturation state. Hence, the pore scale variation of anorthite reaction rate at steady state is small. The simulation results indicate there are heterogeneities in the kaolinite reaction rate, which depends on the saturation state. By inspecting the saturation state, the heterogeneities in the kaolinite reaction rate can be predicted.
Keywords/Search Tags:Network, Kaolinite reaction rate, Reactive flow, Saturation state, Model
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