Bubble Point Pressure and Oil Formation Volume Factor Correlations

This paper evaluates published correlations and neural network models for bubble point pressure and oil formation volume factor for accuracy and flexibility to represent hydrocarbon mixtures from different geographical locations worldwide. The study presents a new correlation for bubble point pressure based on global data with improvement in performance over published correlations. It also presents new neural network models and compares their performance to numerical correlations. The evaluation examines the performance of correlations with original published coefficients and with coefficients calculated based on global data, data from a specific geographical locations, and data for a limited oil gravity range. The evaluation of each coefficient class includes geographical and oil gravity grouping analysis. The results show that the classification of correlation models as most accurate for a specific geographical area is invalid to be used for these two fluid properties. Statistical and trend performance analysis shows that some of the correlations are violating the physical behavior of hydrocarbon fluid properties. Published neural network models are missing major model parameters to reproduce. New developed models performed better, but suffer from stability and trend problems.