Estimation of bubble point pressure and solution gas oil ratio using artificial neural network

Abstract

Bubble point pressure and gas solubility are considered vital pressure-volume-temperature properties of crude oil samples as both play a significant role in reservoir and production engineering calculations. These two fluid oil properties can be measured experimentally or using mathematical correlations, the experimental methods are time consuming and expensive, although they give a reliable result. Therefore, using mathematical correlations can be as an alternative way, however, existing correlation having number of limitations. Hence, this study presents a novel numerical model based on artificial neural network (ANN) model and universal mathematical correlation to forecast bubble point pressure and gas solubility. The artificial neural network model based on back-propagation learning algorithm and sigmoid function with numerous inputs include API gravity, reservoir temperature, and gas specific gravity. The ANN model is developed and validated by utilizing more than 450 data points collected from numerous fields located worldwide with various reservoir characterization. In this study, the bubble point pressure is estimated first and then used to forecast gas solubility based on the calculated correlation coefficient. The results of this study concluded that in order to optimize the ANN model, collected data must to be distributed as follows; 65% for training, 20% for testing and 15% for the validation processes with R2 of 0.988 and average absolute percent relative error (AAPRE) =5.56% for the bubble point pressure and 4.5% for the gas solubility. A comparison study is performed between the new ANN extracted correlation and other well-known models (Standing, Petrosky and Frashad, McCain, Vasquez and Beggs, Marhoun, Glaso, Dokla, and Lasater correlations) to show the robustness of the presented correlation. The results show that the presented correlation has the ability to predict bubble point pressure and gas solubility precisely with lowest AAPRE compared to other models.