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 (γ and σ²) of the LS-SVM model. This model showed a high coefficient of determination (R² = 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.

中文翻译：

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混合连接模型确定了CO2油溶胀系数。

对原油和CO ₂之间相互作用的深入了解提供了对基于CO ₂的强化采油（EOR）工艺设计和模拟的了解。当CO ₂与原油接触时，就会发生溶解过程。这种现象导致机油膨胀，这取决于机油的温度，压力和组成。基于CO ₂的EOR工艺中的残余油饱和度与油溶胀系数成反比。因此，重要的是高精度地估计该影响参数。当前的研究建议基于最小二乘支持向量机（LS-SVM）的预测模型来计算CO ₂–油溶胀系数。遗传算法用来优化超参数（γ和σ ²的LS-SVM模型的）。 根据可用的实验数据，该模型在确定CO _2-油溶胀因子的同时，具有较高的测定系数（R ² = 0.9953）和较低的均方误差值（MSE = 0.0003）。结果发现，LS-SVM是一种简单，准确的方法，可确定不确定性可忽略的CO _2-油溶胀系数。当没有足够的实验数据时，该方法可以并入商业油藏模拟器中，以包括CO _2-油溶胀因子的影响。