Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Olalekan Alade, Dhafer Al Shehri, Mohamed Mahmoud and Kyuro Sasaki
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Olalekan Alade: Department of Petroleum Engineering, College of Petroleum and Geosciences, King Fahd University of Minerals & Petroleum, Dhahran 3225, Saudi Arabia
Dhafer Al Shehri: Department of Petroleum Engineering, College of Petroleum and Geosciences, King Fahd University of Minerals & Petroleum, Dhahran 3225, Saudi Arabia
Mohamed Mahmoud: Department of Petroleum Engineering, College of Petroleum and Geosciences, King Fahd University of Minerals & Petroleum, Dhahran 3225, Saudi Arabia
Kyuro Sasaki: Resources Production and Safety Engineering Laboratory, Department of Earth Resources Engineering, Kyushu University, Fukuoka 812-0053, Japan

Energies, 2019, vol. 12, issue 12, 1-13

Abstract: The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R 2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

Keywords: heavy oil; viscosity; artificial neural network; pressure; temperature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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