Permeability Reduction of Berea Cores Owing to Nanoparticle Adsorption onto the Pore Surface: Mechanistic Modeling and Experimental Work

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• 作者：Bin Yuan ; Wendong WangRouzbeh G. Moghanloo ; Yuliang Su ; Kai Wang ; Miaolun Jiang
• 刊名：Energy & Fuels
• 出版年：2017
• 出版时间：January 19, 2017
• 年：2017
• 卷：31
• 期：1
• 页码：795-804
• 全文大小：633K
• ISSN：1520-5029

文摘

This paper examines an integrated approach to study the permeability alteration resulting from nanofluid flow through porous media. Hydrophilic nanostructure particles (NSPs) are dispersed in the brine stream at 0.05, 0.2, and 0.5 wt % concentrations and injected into several oil-wet Berea sandstones. The pressure drops across the cores and the effluent nanoparticle concentrations are monitored. To quantify the nanoparticle adsorption/detachment and straining behavior and associated effects on formation permeability, analytical mechanistic models are derived using the method of characteristics. The interplay between nanoparticles and rocks is described by the classical particle filtration theory coupled with the maximum adsorption concentration model. All of the necessary parameters, e.g., the maximum adsorption concentrations, reversible or detachment adsorption concentrations, nanoparticle adsorption and straining rates, and corresponding formation damage coefficients, are characterized. The experimental results indicate that nanoparticle adsorption and straining (i.e., the maximum adsorption concentration and nanoparticle adsorption straining rates) are enhanced along with the increase of the nanoparticle injection concentration. As a result, the breakthrough of injected nanoparticles is delayed, the steady-state effluent concentration decreases, and the pressure drop increases more rapidly. The nanoparticle adsorption consists of reversible and irreversible adsorption. During post-flush, the reversible nanoparticle concentrations are enhanced by the increase of nanoparticle concentrations. In practice, this paper contributes to the following applications: (1) Lab experiments are applied to highlight the effects of nanoparticle adsorption, straining, and detachment behaviors on the formation damage. (2) The analytical mechanistic model provides physical insights to quantify nanofluid flow performance and can be extended to optimize the treatment of nanofluid application (e.g., injection concentrations) while considering both the loss of nanoparticles and their induced formation damage.