Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂-Crude Oil Minimum Miscibility Pressure

The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time-consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2-crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine-tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO-LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.