A key parameter in a CO2 flooding process is the CO2 solubility; for, it contributes to oil viscosity reduction and oil swelling, which together, in turn, enhance the oil mobility and oil relative permeability. Often CO2 solubility and the other CO2-oil physical properties are established through timeconsuming experimental means or using models or correlations available in the literature. However, one must recognize that such models or correlations to predict CO2-oil physical properties are valid usually for certain data ranges or sitespecific conditions. Furthermore, it is to be noted that there is no reliable model available to predict CO2-live oil physical properties, as most of the available correlations were developed based on dead oil data.
In this study, a genetic algorithm (GA)-based technique has been used to develop more reliable correlations to predict the CO2 solubility, oil swelling factor, CO2-oil density, and CO2- oil viscosity for both dead and live oils. These correlations recognize not only all major variables that affect each physical property but also take into account the effects of the CO2 liquefaction pressure and oil molecular weight (MW). These correlations have been successfully validated with published experimental data and compared against several widely used correlations. The GA-based correlations have yielded more accurate predictions with lower errors than the other correlations tested. Furthermore, unlike these correlations, which are applicable to only limited data ranges and conditions, the GA-based correlations can be applied over a wider data range and conditions. Another important and useful aspect is that the GA-based correlations can also be integrated into any reservoir simulator for CO2 flooding design and simulation.
The knowledge of physical and chemical interactions between CO2 and reservoir oil besides studying of the prospective recovery are very important for any CO2 flooding project. The major parameter that affects CO2 flooding is CO2 solubility in oil because it results in oil viscosity reduction and oil swelling increase, which in turn, enhance the oil mobility, oil relative permeability, and increase the oil recovery efficiency. Therefore, a better understanding of this parameter and its effects on the CO2-oil mixture properties is vital to any successful CO2 flooding project.
The effects of CO2 on oil physical properties are determined by laboratory studies and available modeling or correlations packages. For the laboratory studies, it is very expensive and time consuming to have a laboratory study, which covers a wider range of data. On the other hand, for the available correlations packages, they can be used in certain situations. However, they are not applicable in many situations, as they have many limitations in their applications. Furthermore, most of the available packages were developed based on dead oil data and there is no reliable model to predict the effects of CO2 on live oil physical properties.
The objective of this research is to develop a more reliable model to predict the effects of CO2 on oil properties (CO2 solubility in oil, oil swelling factor, and CO2-oil mixture density and viscosity) for both dead and live oils.