Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models

Document Type : Research Paper


1 Department of Petroleum Engineering, Sahand University of Technology, Tabriz, Iran

2 Faculty of Engineering, University of Garmsar, Garmsar, Iran

3 Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, Iran

4 Department of computer Engineering, Faculty of Engineering, , Shahid Chamran University, Ahwaz, Iran

5 Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

6 Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

7 Department of Petroleum Engineering Petroleum Industry University, Ahvaz, Iran


Determining BPP is one of the critical parameters for the development of oil and gas reservoirs and have this parameter requires a lot of time and money. As a result, this study aims to develop a new predictive model for BPP that uses some available input variables such as solution oil ratio (Rs), gas specific gravity (γg), API Gravity (API). In this study, two innovatively combined hybrid algorithms, DWKNN-GSA and DWKNN-ICA, are developed to predict BPP. The outcomes of the study show the models developed are capable of predicting BPP with promising performance, where the best result was achieved for DWKNN-ICA (RMSE = 0.90276 psi and R2 = 1.000 for the test dataset). Moreover, the performance comparison of the developed hybrid models with some previously developed models revealed that the DWKNN-ICA outperforms the former empirical models with respect to perdition accuracy. In addition to presenting new techniques in the present study, the effect of each of the input parameters on BPP was evaluated using Spearman's correlation coefficient, where the API and Rs have the lowest and the highest impact on the BPP.


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