Petroleum Science >2013,??Issue 1:??126-133 DOI: https://doi.org/10.1007/s12182-013-0259-4
Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations Open?Access
文章信息
作者：Mohammad Soleimani Lashkenari,Majid Taghizadeh1 and Bahman Mehdizadeh
作者單位：
Chemical Engineering Department, Babol University of Technology, P.O. Box 484, 4714871167 Babol, Iran;Chemical Engineering Department, Babol University of Technology, P.O. Box 484, 4714871167 Babol, Iran;National Iranian South Oil Company, Ahwaz, Iran
投稿時(shí)間：2012-07-01
引用方式：Lashkenari, M.S., Taghizadeh, M. & Mehdizadeh, B. Pet. Sci. (2013) 10: 126. https://doi.org/10.1007/s12182-013-0259-4
文章摘要
Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several
approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson,
1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959;
Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the
results obtained by these methods have significant errors when compared with the experimental data.
In this study a robust artificial neural network (ANN) code was developed in the MATLAB software
environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the
three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999;
Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at
three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples
collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with
the models previously published in literature, the ANN model has a better accuracy and performance in
predicting the viscosity of Iranian crudes.
approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson,
1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959;
Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the
results obtained by these methods have significant errors when compared with the experimental data.
In this study a robust artificial neural network (ANN) code was developed in the MATLAB software
environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the
three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999;
Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at
three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples
collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with
the models previously published in literature, the ANN model has a better accuracy and performance in
predicting the viscosity of Iranian crudes.
關(guān)鍵詞
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Viscosity, crude oil, artificial neural network, empirical equations