Abstract

In this paper, a generalized methodology has been developed to determine the diffusion coefficient of supercritical CO2 in cores that are saturated with different oil samples, under reservoir conditions. In theory, a mathematical model that combines Fick’s diffusion equation and the Peng-Robinson equation of state has been established to describe the mass transfer process. In experiments, the pressure decay method has been employed, and the CO2 diffusion coefficient can be determined once the experimental data match the computational result of the theoretical model. Six oil samples with different compositions (oil samples A to F) are introduced in this study, and the results show that the supercritical CO2 diffusion coefficient decreases gradually from oil samples A to F. The changing properties of oil can account for the decrease in the CO2 diffusion coefficient in two aspects. First, the increasing viscosity of oil slows down the speed of the mass transfer process. Second, the increase in the proportion of heavy components in oil enlarges the mass transfer resistance. According to the results of this work, a lower viscosity and lighter components of oil can facilitate the mass transfer process.

Highlights

  • Insufficient oil and gas supplies and global warming have aroused interest in enhanced oil recovery (EOR) with CO2 and geological CO2 storage [1,2,3]

  • The changing properties of oil can account for the decrease in the CO2 diffusion coefficient in two aspects

  • Six groups of oil samples, which were prepared by mixing kerosene and crude oil under different volume ratios, are used in the diffusion experiments to study the influence of oil properties on the CO2 diffusion coefficient

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Summary

Introduction

Insufficient oil and gas supplies and global warming have aroused interest in enhanced oil recovery (EOR) with CO2 and geological CO2 storage [1,2,3]. Energies 2018, 11, 1495 methods for the quantitative determination of the gas (N2 , CO2 , CH4 [28], C2 H6 , C3 H8 , or a mixture gas) diffusion coefficient in crude oil. These methods include the pressure decay method, X-ray computer-assisted tomography (CAT) method, magnetic resonance imaging (MRI) method [29,30], dynamic drop volume analysis (DPDVA), and pore-scale network modeling method. Taheri et al [50] predicted the solvent diffusion coefficient in heavy oil and bitumen with the sub-pore-scale modeling method and drew the conclusion that it produced similar results to classic experimental measurements. Addition, coefficient the influence oil properties on the CO diffusion coefficient has been analyzed

Materials
Apparatus
Experimental Procedures
Diffusion
Determination of the Diffusion Coefficients
Characterization of the Oil Samples
Viscosity-temperature
Theoftendencies and values of theinteraction data in Table
Solution of the Diffusion Model in the Oil-Saturated Cores
Section 3.1
Effect
D Oil E while
15.3 MPa andthe
Conclusions
Full Text
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