Toward Predictive Models for Estimation of Bubble-point Pressure and Formation Volume Factor of Crude Oil Using an Intelligent Approach

Accurate estimation of reservoirs fluid properties, as vital tools of reservoir behavior simulation and reservoir economic investigations, seems to be necessary. In this study, two important properties of crude oil, bubble point pressure (Pb) and formation volume factor (Bob), were modelled on the basis of a number of basic oil properties: temperature, gas solubility, oil API gravity and gas specific gravity. Genetic programming, as a powerful method, was implemented on a set of 137 crude oil data and acceptable correlations were achieved. In order to evaluate models, two test datasets (17 data for Pb and 12 data for Bob) were used. The squared correlation coefficient (R) and average absolute relative deviation (AARD %) over the total dataset (training + test) are 0.9675 and 8.22% for Pb and 0.9436 and 2.004% for Bob, respectively. Simplicity and high accuracy are the advantages of the obtained models.