Prediction of the minimum miscibility pressure for CO2 flooding based on a physical information neural network algorithm

Abstract The minimum miscibility pressure (MMP) is a crucial parameter in assessing the miscibility of CO2 displacement and evaluating the effectiveness of oil displacement. Traditional methods for calculating MMP are intricate and time-consuming, involving numerous related parameters. Therefore, precise and efficient determination of MMP is highly significant in the development of CO2-driven reservoirs. This study first utilized the Pearson correlation coefficient to analyse the correlation factor mechanism of 36 sets of fine-tube experimental data. Subsequently, the physical information neural network (PINN) prediction model was employed with reservoir temperature, crude oil composition, and injected gas type as input parameters. The PRI state equation and Glaso correlation equation drove the model, with parameter optimization and training conducted under both physical and data driving. The model demonstrates high prediction accuracy and strong generalization ability. Finally, Validation of the model was performed using fine-tube experimental data from 5 other wells, revealing a relatively small relative deviation between calculated and experimental values, with an average coefficient of determination of 0.95 and an average relative error of 4.42%. The prediction accuracy was improved by about 75% compared to other machine learning algorithms. This model holds potential for application in on-site reservoir development, enhancing the measurement accuracy of the minimum miscible pressure of pure CO2 flooding, greatly shortening the design cycle of reservoir development, expediting the process of reservoir development, and providing technical guidance for improving oil and gas recovery and pure CO2 flooding exploration and development.