Dynamic Behavior of Miscible Binary Fluid Mixtures in Nanopores : Implications for Co2-Enhanced Oil Flow in Shale Reservoirs
Dynamic behavior of miscible CO2-oil mixtures in nanopores is the heart of CO2-EOR and CO2 storage in shale reservoirs. However, the underlying physics resulting from scale effects remains unclear due to the lack of in-depth mechanisms analysis and powerful theoretical models. In this work, a novel approach on the basis of molecular kinetic theory is developed to model the miscible mixtures flow in nanopores with involving changes in activation free energy induced by intermolecular interactions and interfacial interactions, where the former is correlated with Helmholtz free energy and determined by a modified Soave-Redlich-Kwong equation of state, while the latter is determined by a solid-fluid potential model with assuming a miscible mixture as a single pseudo-fluid. Subsequently, CO2-enhanced oil flow in nanopores and its deviation from bulk results are demonstrated. Moreover, the role of microscopic interactions, pore size and CO2 content are clarified. It is found that CO2 can simultaneously weaken the fluid-fluid interactions and fluid-wall interactions, jointly leading to a reduction in local fluid viscosity. Besides, a decrease in pore space also suppresses the activation free energy induced by the fluid-fluid interactions, benefiting the movement of molecules from one location to another. Combining all these mechanisms, the CO2-enhanced effect in organic nanopores is found to be greater than that of bulk results, and their difference is noticeable and sensitive as the pore size is smaller than 20nm. Besides, the increase of wall-oil affinity and CO2 content can further strengthen the enhancement effect. Our work will not only lay a theoretical foundation for reservoir simulation of CO2-EOR and CO2 storage in shale reservoirs but also apply to other miscible mixtures transport in nonporous materials
Year of publication: |
[2022]
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Authors: | FENG, DONG ; Chen, Zhangxin ; Wu, Keliu ; Li, Jing ; Gao, Yanling ; Bi, Jianfei ; Zhang, Shengting ; Peng, Fei |
Publisher: |
[S.l.] : SSRN |
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freely available
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