We constructed a molecular model (digital oil model) for heavy crude oil based on analytical data and our newly developed method. Crude oil was separated into four fractions: saturates, aromatics, resins, and asphlatenes (SARA). Although it is classified as a heavy crude oil, the asphaltenes turned out to be at very low weight concentration (~0.4 wt. %), and were ignored in our study. The digital oil was constructed as a mixture of representative molecules of four fractions: saturates, aromatics, resins, and lost components (which resulted from our SARA analysis). Representative molecules were generated by quantitative molecular representation (QMR), a technique that provides a set of molecules consistent with analytical data, such as elemental composition, average molecular mass, and the proportions of structural types of hydrogen and carbon atoms, as revealed by 1 H and 13 C nuclear magnetic resonance. To enable the QMR method to be applicable to saturates, we made two developments: the first was the generation of non-aromatic molecules by a new algorithm that can generate a more branched structure by separating the chain bonding into main and subsidiary processes; the second was that the molecular mass distribution of the model could be fitted to that obtained from experiments. To validate the digital oil thus obtained, we first confirmed the validity of the model for each fraction in terms of plots of double-bond equivalent as a function of carbon number. We then calculated its density and viscosity by molecular dynamics simulations. The calculated density was in good agreement with experimental data for crude oil. The calculated viscosity was higher than experimental values; however, the error appeared systematic, being a factor of ~1.5 higher than that of experiments. Moreover, the calculated viscosity as a function of temperature was well described by the Vogel-Fulcher-Tammann equation. Digital oil will be a powerful tool to analyze both macroscopic properties and microscopic phenomena of crude oil under any thermodynamic conditions.
To investigate enhanced oil recovery processes, we constructed a molecular model of a live heavy crude oil (digital oil) and studied the crude oil properties at the reservoir temperature and a wide range of pressures. We identified the liquid phase components of the digital oil by flash calculation and calculated the density and viscosity by molecular dynamics simulations. The calculated density and viscosity were in good agreement with experimental data. To evaluate the effectiveness of various solvents to enhance oil recovery, we calculated the oil property changes when different solvents were added to the digital oil. First, we compared methane and carbon dioxide (CO 2 ). The results indicated that CO 2 was more effective in terms of oil-viscosity reduction, oil swelling, and diffusion in the oil. Second, we evaluated the effectiveness of 11 different solvents: nitrogen, CO 2 , methane, ethane, propane, n-heptane, n-octane, toluene, and three xylene isomers (o-xylene, m-xylene, and pxylene). Ethane had the greatest effect on oil-viscosity reduction and oil swelling, and CO 2 had the highest diffusion coefficient. From these results, ethane and CO 2 are appropriate solvents for this crude oil. In addition, it is interesting to note that the decreases of the viscosity among the three xylene isomers were different, but there were no differences in the swelling factors and diffusion coefficients. The different rotation motion characteristics of the xylene isomers can account for the viscosity differences. Such information will be helpful for further development of digital oil models.
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