Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique

Abstract

Accurate determination of the bubble point pressure (BPP) is extremely important in several applications in oil industry. In reservoir engineering applications the BPP is an essential input for the reservoir simulation and reservoir management strategies. Also, in production engineering the BPP determines the type of the inflow performance relationship that describes the reservoir production performance. Accurate estimation of the BPP will eliminate the risk of producing in two-phase region. Current correlations can be used to determine the BPP with high errors, and this will lead to poor reservoir management. Artificial intelligent tools used in the previous studies did not disclose the models they developed, and they stated the models as black box. The aim of this research is to develop a new empirical correlation for BPP prediction using artificial intelligent techniques (AI) such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we extracted the weights and the biases from AI models and form a new mathematical model for BPP prediction. The results obtained showed that the ANN model was able to estimate the BPP with high accuracy (correlation coefficient of 0.988 and average absolute error percent of 7.5%) based on the specific gravity of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir as compared with ANFIS and SVM. The developed mathematical model from the ANN model outperformed the previous AI models and the empirical correlations for BPP prediction. It can be used to predict the BPP with a high accuracy (the average absolute error (3.9%) and the coefficient of determination ( $$R^{2})$$ of 0.98).