Development of an Adaptive Neuro-Fuzzy based Model for Prediction of Minimum Miscibility Pressure

In this paper, a neuro-fuzzy hybrid approach was used to construct a CO2 MMP predicting system during design a gas injection project. In particular, we used an adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. In neuro-fuzzy inference system, zero order Sugeno-type inference technique was used to perform approximate reasoning of fuzzy input variables. In addition, hybrid learning algorithm, combining back propagation learning and linear least-squares estimator, was preferred for the adaptation of free parameters. Consequently, neuro-fuzzy model was compared with results obtained using multiple linear regression methodology in addition to other conventional models to make comparison among different techniques. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for MMP forecasting

کلیدواژه ها:

Gas injection ، Minimum miscibility pressure ، Enhanced oil Recovery ، Neuro-fuzzy model