Adaptive neuro-fuzzy approach for reservoir oil bubble point pressure estimation

• ANFIS was designed for estimation of reservoir oil saturation pressures.
• A large experimental data bank was compiled for the development of the model.
• Hybrid optimization technique gave more accurate results than backpropagation method.
• The developed models are more accurate than all other well-known published correlations.

A new method based on adaptive network-based fuzzy inference system (ANFIS) approach was designed and developed for improved estimation of reservoir oil bubble point pressure using commonly available field data. More than 750 data series from different geographical locations worldwide was gathered for modeling. Two different ANFIS networks (by changing the training optimization algorithms) were compared with evaluation of networks accuracy in bubble point pressure prediction and subsequently the suitable network was determined. The predictions of selected network are in good agreement with the corresponding experimental data with the squared correlation coefficient of 0.97. In addition, a comparative study was carried out between the developed model and other published correlations. In comparison with the published literature correlations, the results showed that proposed ANFIS can be used as a powerful model for improved prediction of reservoir oil bubble point pressure.