Prediction of heavy oil viscosity using a radial basis function neural network

被引：8

作者：

Tatar, Afshin ^{[1
]}

Barati-Harooni, Ali ^{[2
]}

Moradi, Siyamak ^{[3
]}

Nasery, Saeid ^{[2
]}

Najafi-Marghmaleki, Adel ^{[2
]}

Lee, Moonyong ^{[4
]}

Le Thi Kim Phung ^{[5
]}

Bahadori, Alireza ^{[6
,7
]}

机构：

[1] Islamic Azad Univ, Young Researchers & Elite Club, North Tehran Branch, Tehran, Iran

[2] Islamic Azad Univ, Ahvaz Branch, Young Researchers & Elite Club, Ahvaz, Iran

[3] Petr Univ Technol, Abadan Fac Petr Engn, Abadan, Iran

[4] Yeungnam Univ, Sch Chem Engn, Gyongsan, South Korea

[5] Hochiminh City Univ Technol, Dept Chem Proc & Equipment, Ho Chi Minh City, Vietnam

[6] Southern Cross Univ, Sch Environm Sci & Engn, Lismore, NSW, Australia

[7] Australian Oil & Gas Serv Pty Ltd, Lismore, NSW, Australia

来源：

PETROLEUM SCIENCE AND TECHNOLOGY | 2016年 / 34卷 / 21期

关键词：

Genetic algorithm; heavy oil; radial basis function; viscosity; CARBON-DIOXIDE SOLUBILITY; FUZZY INFERENCE SYSTEM; PRESSURE; MODEL; TEMPERATURE; SATURATION; MIXTURE;

D O I：

10.1080/10916466.2016.1221966

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

080707 [能源环境工程]; 082001 [油气井工程];

摘要：

Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data.

引用

页码：1742 / 1748

页数：7

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