In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process tak...In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.展开更多
基金financial support from Natural Sciences and Engineering Research Council of Canada (NSERC), Innovate NL, and Statoil Canada
文摘In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.
