A Genetic Algorithm-Based Model to Predict CO-Oil Physical Properties for Dead and Live Oil

Abstract

Abstract A key parameter in a CO2 flooding process is the CO2 solubility as it contributes to oil viscosity reduction and oil swelling which together, in turn, enhance the oil mobility and oil relative permeability. Often CO2-oil solubility parameters are established through time-consuming 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 site-specific 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 are 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 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 parameters that affect each physical property, but also take into account the effects of 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 other correlations tested. Furthermore, unlike these correlations, which are applicable to only limited data ranges and conditions, GA-based correlations can be applied over a wider range and conditions. Another important and useful aspect is that GA-based correlations can also be integrated into any reservoir simulator for CO2 flooding design and simulation. Introduction Knowledge of the physical and chemical interactions between CO2 and reservoir oil, other than for study 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, enhances the oil mobility, the oil relative permeability and increases oil recovery efficiency. Therefore, a better understanding of this parameter is vital to any successful CO2 flooding project. The effects of CO2 on oil physical properties are determined by laboratory studies and available modelling or correlation packages. It is very expensive and time consuming to conduct a laboratory study, which covers a wider range of data. On the other hand, for the available correlation packages, they can only be used in certain situations. They are not applicable in many situations as they have many limitations in their application. Furthermore, most of the available packages are developed for dead oil and there is no reliable model to predict the effect of CO2 on live oil physical properties. The objective of this study was to develop a more reliable model to predict the effects of CO2 on crude oil properties (CO2 solubility in oil, oil-swelling factor and CO2-oil mixture density and viscosity) for both dead and live oils. The new genetic algorithm (GA)-based modelling technique used in this study is based on GA software developed in an earlier work(1).