Prediction Model for Minimum Miscibility Pressure in CO2-Oil Based on Automated Machine Learning

As a key means of improving oil recovery, the application effect of CO2 flooding technology is restricted by many factors, the effectiveness of CO2 flooding technology is restricted by many factors, with the minimum miscibility pressure (MMP) required to achieve the miscible phase of CO2-crude oil being the most critical parameter for determining oil displacement efficiency. In this study, the CO2-oil MMP prediction model was established using automatic machine learning, and the MMP was predicted and the results were analyzed. The results demonstrate that automatic machine learning can simplify the model development process through automatic model selection, hyperparameter optimization, and model fusion, significantly improving the prediction performance and accuracy of the model while accurately predicting the CO2-oil MMP. By comparing the predicted values of the model with the results of the slim tube experiment, the error was found to be within 5 %, indicating that the automatic machine learning model can effectively predict the MMP in Bayan Oilfield, providing valuable guidance for the development of CO2 flooding in oilfields.