The deposition of asphaltenes in porous media is one of the most difficult problems during CO 2 flooding. In this paper, the adsorption of asphaltenes onto Al 2 O 3 nanoparticles was studied through two methods: (i) by adding a certain mass of nanoparticles in a fixed volume of solution with different initial concentrations of asphaltenes and (ii) by exposing a certain amount of asphaltenes in a fixed volume of solution to different nanoparticles additions. Then, the impact of Al 2 O 3 nanoparticles on the asphaltene precipitation was investigated using the IFT behavior of the oil–CO 2 system. Coreflood tests were conducted to study the potential of nanoparticles for inhibition of asphaltenes damage during CO 2 flooding, and the effect of injection parameters of nanofluid. It is found that the solid–liquid equilibrium (SLE) can well describe the isotherms of asphaltene adsorption. The trend of the IFT–pressure curve is affected by asphaltene accumulation at the oil–CO 2 interface. The slopes in the high pressure region can be used to examine the intensity of asphaltene precipitation. As the addition of nanoparticles increases, the IFT slope in the high pressure range decreases. This is because the asphaltenes are absorbed at the surface of nanoparticles. As a result, the nanoparticles prevent asphaltene precipitation from accumulating at the CO 2 –oil interface, hence causing asphaltene to remain within the bulk of oil. The higher the mass fraction of Al 2 O 3 nanoparticles is, the lower the intensity of the asphaltene precipitation would be. The coreflood results show that the injection of Al 2 O 3 nanofluid can lessen the oil permeability reduction because the nanoparticles can inhibit the deposition of asphaltenes onto the sand surfaces in the porous media. The 0.5 wt % nanoparticles and the 0.1 nanofluid/CO 2 slug volume ratio are considered as the optimum for inhibiting asphaltenes damage during CO 2 flooding. Continuous CO 2 and nanofluid injection could be more effective compared with the cyclic injection pattern.
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Lu et al. (2016) studied this question.
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