Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure

Abstract

CO2 injection is known as one of the most reliable enhanced oil recovery techniques. The success of every gas injection process depends highly on the minimum miscibility pressure (MMP) needed for the injected gas and the oil to reach miscibility. Therefore, determination of the MMP between the two fluids is of great importance. In this study, a novel intelligent model based on adaptive neuro fuzzy interface system (ANFIS) was developed for predicting MMP values between pure/impure CO2 and reservoir oil at different reservoir conditions based on 270 experimental data points for pure and impure CO2, dead and live oils, reservoir temperature ranges from 293.72K to 388.73K, and experimental MMP values range from 6.54MPa to 31.30MPa. The ANFIS model was optimized by five different approaches; including Back Propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Differential Evolution (DE). A large number of data points from various sources of literature were gathered for model development and verification. In addition, various statistical and graphical error analyses were employed to evaluate the performance of the developed models as well as to compare them with major literature models for MMP prediction. The results showed that the ANFIS model optimized by PSO has the highest accuracy among all the models developed in this study as well as literature models, with an average absolute percent relative error of only 7.53%. In addition, all models developed in this study are more accurate than the existing models and their accuracy are as follows: ANFIS-PSO>ANFIS-GA>ANFIS-ACO>ANFIS-BP>ANFIS-DE. Lastly, the ANFIS models developed here can be inserted in any simulator to increase the accuracy of predicting pure and impure CO2-crude oil MMP.