Volume 13, Issue 4, December 2015, Pages 946–957
Abdelrigeeb A. Al-Gathe1, Kh. A. Abd-El Fattah2, Ahmed H. El-Banbi3, and K. A El-Metwally4
1 Department of Petroleum Engineer, Cairo University, Egypt
2 Department of Petroleum Engineer, Cairo University, Egypt
3 Petroleum Engineering, Cairo University, Giza, Egypt
4 Electrical Engineering, Cairo University, Giza, Egypt
Original language: English
Copyright © 2015 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.
In absence of PVT laboratory experiments data on representative fluid samples, it is usually difficult to choose the appropriate PVT correlations to calculate oil viscosity. This difficulty will increase when input data to PVT correlations (oil API gravity, initial gas-oil ratio, specific gravity of separator gas and temperature) vary along the flow from one section to the other in the production system. However, the accuracy of these correlations has become inadequate for the best estimations. The achievements of the Artificial Intelligent (AI) techniques alone open the door to use the hybrid system. This research focuses on the use of predictive NFuzzy model that is a result of combination of the learning capabilities of Neural Networks (NN) with the reasoning capabilities of Fuzzy Logic as a hybrid intelligent system. The proposed approach is based on clustering the PVT data into three clusters (heavy, medium and light oil) based on solution gas oil ratio. Around 500 to 2500 data points for each oil viscosity obtained from Middle East and worldwide laboratory measurements. The data were separated into two parts, 70% of data for training and the rest 30% were utilizing for testing. The present model used to estimate dead viscosity, saturated and under-saturated oil viscosity.
Based on this result, we conclude that NFuzzy exhibits a robust predictive capability for estimation of oil viscosity by providing a good match with the measured values. The additional data samples were selected to compare and validate this model.
Author Keywords: Fuzzy Logic, Neural Networks, Artificial Intelligence.
Abdelrigeeb A. Al-Gathe1, Kh. A. Abd-El Fattah2, Ahmed H. El-Banbi3, and K. A El-Metwally4
1 Department of Petroleum Engineer, Cairo University, Egypt
2 Department of Petroleum Engineer, Cairo University, Egypt
3 Petroleum Engineering, Cairo University, Giza, Egypt
4 Electrical Engineering, Cairo University, Giza, Egypt
Original language: English
Copyright © 2015 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
In absence of PVT laboratory experiments data on representative fluid samples, it is usually difficult to choose the appropriate PVT correlations to calculate oil viscosity. This difficulty will increase when input data to PVT correlations (oil API gravity, initial gas-oil ratio, specific gravity of separator gas and temperature) vary along the flow from one section to the other in the production system. However, the accuracy of these correlations has become inadequate for the best estimations. The achievements of the Artificial Intelligent (AI) techniques alone open the door to use the hybrid system. This research focuses on the use of predictive NFuzzy model that is a result of combination of the learning capabilities of Neural Networks (NN) with the reasoning capabilities of Fuzzy Logic as a hybrid intelligent system. The proposed approach is based on clustering the PVT data into three clusters (heavy, medium and light oil) based on solution gas oil ratio. Around 500 to 2500 data points for each oil viscosity obtained from Middle East and worldwide laboratory measurements. The data were separated into two parts, 70% of data for training and the rest 30% were utilizing for testing. The present model used to estimate dead viscosity, saturated and under-saturated oil viscosity.
Based on this result, we conclude that NFuzzy exhibits a robust predictive capability for estimation of oil viscosity by providing a good match with the measured values. The additional data samples were selected to compare and validate this model.
Author Keywords: Fuzzy Logic, Neural Networks, Artificial Intelligence.
How to Cite this Article
Abdelrigeeb A. Al-Gathe, Kh. A. Abd-El Fattah, Ahmed H. El-Banbi, and K. A El-Metwally, “A Hybrid Neuro-Fuzzy Approach for Black Oil Viscosity Prediction,” International Journal of Innovation and Applied Studies, vol. 13, no. 4, pp. 946–957, December 2015.
