Viscosity––temperature–pressure relationship of extra-heavy oil (bitumen): empirical modelling versus artificial neural network (ANN)

O. S. Alade, Dhafer Al Shehri, Mohamed Mahmoud, K. Sasaki

研究成果: ジャーナルへの寄稿学術誌査読

5 被引用数 (Scopus)

抄録

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 (R2 1) for the viscosity data of the heavy oil samples used in this study.
本文言語英語
論文番号2390
ページ(範囲)1-13
ページ数13
ジャーナルEnergies
12
DOI
出版ステータス出版済み - 6月 21 2019

フィンガープリント

「Viscosity––temperature–pressure relationship of extra-heavy oil (bitumen): empirical modelling versus artificial neural network (ANN)」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル