Development of an artificial neural network model for prediction of bubble point pressure of crude oils

Bubble point pressure is one of the most important pressure-volume-temperature properties of crude oil, and it plays an important role in reservoir and production engineering calculations. It can be precisely determined experimentally. Although, experimental methods present valid and reliable results, they are expensive, time-consuming, and require much care when taking test samples. Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties (e.g., bubble point pressure); however, these methods have a number of limitations. In the present study, a novel numerical model based on artificial neural network (ANN) is proposed for the prediction of bubble point pressure as a function of solution gas-oil ratio, reservoir temperature, oil gravity (API), and gas specific gravity in petroleum systems. The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world. An optimization process was performed on networks with different structures. Based on the obtained results, a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure. The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gas-oil ratios in the range of 8.61-3298.66 SCF/STB, temperatures between 74 and 341.6 °F, oil gravity values of 6-56.8 API and gas gravity values between 0.521 and 3.444. The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses. The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.