Volume 4, Issue 2, July 2014, Pages 121–135
Abdelrigeeb A. Al-Gathe1, Kh. A. Abd-El Fattah2, and K.A. El-Metwally3
1 Department of Petroleum Engineer, Cairo University, Egypt
2 Department of Petroleum Engineer, Cairo University, Egypt
3 Department of Petroleum Engineer, Cairo University, Egypt
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bubble point pressure is the most crucial Pressure-Volume-Temperature property of reservoir fluid, which plays a critical role in almost all tasks related to reservoir and production engineering. There are numerous approaches for predicting various Bubble point pressure properties, namely, empirical correlations and few computational intelligence schemes. The achievements of Neural Networks (NN), Fuzzy Logic (FL) Genetic Algorithm (GA), and Expert System (ES) alone open the door to the Hybrid Systems; a genetically optimized neural network (GA-ANN) and Neuro-Fuzzy (NF) modeling techniques to play a major role in petroleum industry.
In this paper, a novel comprehensive approach to the prediction of the bubble point pressure (Pb) using two hybrid systems (GA-ANN and NF) and Expert System is introduced. A total of about 160 data points from Middle East oil samples were used. Twenty three correlations of Pb are integrated to develop Expert System. The performance of the proposed techniques is compared against performance of the most accurate general correlations for Pb calculation. Statistical error analysis was also used to check the validation of the proposed techniques. From the results of this study, it can be pointed out that these methods are more accurate and reliable.
Author Keywords: Neural Network, fuzzy logic, Neuro-Fuzzy, Expert Systems.
Abdelrigeeb A. Al-Gathe1, Kh. A. Abd-El Fattah2, and K.A. El-Metwally3
1 Department of Petroleum Engineer, Cairo University, Egypt
2 Department of Petroleum Engineer, Cairo University, Egypt
3 Department of Petroleum Engineer, Cairo University, Egypt
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Bubble point pressure is the most crucial Pressure-Volume-Temperature property of reservoir fluid, which plays a critical role in almost all tasks related to reservoir and production engineering. There are numerous approaches for predicting various Bubble point pressure properties, namely, empirical correlations and few computational intelligence schemes. The achievements of Neural Networks (NN), Fuzzy Logic (FL) Genetic Algorithm (GA), and Expert System (ES) alone open the door to the Hybrid Systems; a genetically optimized neural network (GA-ANN) and Neuro-Fuzzy (NF) modeling techniques to play a major role in petroleum industry.
In this paper, a novel comprehensive approach to the prediction of the bubble point pressure (Pb) using two hybrid systems (GA-ANN and NF) and Expert System is introduced. A total of about 160 data points from Middle East oil samples were used. Twenty three correlations of Pb are integrated to develop Expert System. The performance of the proposed techniques is compared against performance of the most accurate general correlations for Pb calculation. Statistical error analysis was also used to check the validation of the proposed techniques. From the results of this study, it can be pointed out that these methods are more accurate and reliable.
Author Keywords: Neural Network, fuzzy logic, Neuro-Fuzzy, Expert Systems.
How to Cite this Article
Abdelrigeeb A. Al-Gathe, Kh. A. Abd-El Fattah, and K.A. El-Metwally, “New Artificial Intelligent Approach for Bubble Point Pressure,” International Journal of Innovation and Scientific Research, vol. 4, no. 2, pp. 121–135, July 2014.
