Prediction of Bubble Point Pressure Using Artificial Intelligence AI Techniques

Abstract

Abstract It is very important to determine or predict the bubble point pressure (BPP) with high accuracy in petroleum industry. Laboratory measurement of the BPP requires collecting actual samples from the bottom of the wellbore and simulates the reservoir conditions at the lab. This operation takes long time and high cost. To overcome this issue, many empirical correlations were developed to predict the BPP with wide range of average percent error. In this research, we will use artificial intelligent (AI) techniques to predict the bubble point pressure using published data (760 data sets). Two different AI techniques will be used, artificial neural network (ANN) (back propagation network (BPN) and radial basis functions networks (RBF)), and fuzzy logic tool (FL) to develop the model. The obtained results will be compared with the available correlations in the literature. The results obtained showed that all AI models were able to predict the bubble point pressure with a high accuracy. The new fuzzy logic (FL) model outperforms all the artificial neural network models and the most common published empirical correlations. BPN, RBF and FL models provide predictions of bubble point pressure with correlation coefficient of 0.9926, 0.9969, and 0.9995, respectively.