A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils

Abstract Viscosity is an important measure of fluid resistance to shear stress; therefore, efficient estimation of oil viscosity in various operating conditions is very important. Several variables, such as oil API gravity (API), pressure (P), saturation pressure (Pb), reservoir temperature (Tf), are employed in the estimation of crude oil viscosity. A hybrid group method of data handling (GMDH) artificial neural network, optimized with genetic algorithm (GA), was herein proposed to obtain efficient polynomial correlation to estimate oil viscosity. This correlation was compared with 5 correlations presented in the previous research using the large set of Iranian oil data. Also, sensitivity analysis of the obtained correlation was carried out to study the influence of input parameters on the correlation output. A comprehensive computational and statistical result was provided to evaluate the performance of the proposed methods. Results showed that these models were very good approximations for estimating the viscosity of Iranian crude oils.

[1]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[2]  Salih O. Duffuaa,et al.  Viscosity Correlations for Saudi Arabian Crude Oils , 1987 .

[3]  Oyinkepreye D. Orodu,et al.  PREDICTION OF CRUDE OIL VISCOSITY USING FEED-FORWARD BACK- PROPAGATION NEURAL NETWORK (FFBPNN) , 2012 .

[4]  Farshid Torabi,et al.  The Development of an Artificial Neural Network Model for Prediction of Crude Oil Viscosities , 2011 .

[5]  O. Glaso,et al.  Generalized Pressure-Volume-Temperature Correlations , 1980 .

[6]  Sarit Dutta,et al.  PVT correlations for Indian crude using artificial neural networks , 2010 .

[7]  M. Bayat,et al.  NEW VISCOSITY CORRELATIONS FOR DEAD CRUDE OILS , 2007 .

[8]  F. Kalantary,et al.  An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms , 2009 .

[9]  Carlton Beal,et al.  The Viscosity of Air, Water, Natural Gas, Crude Oil and Its Associated Gases at Oil Field Temperatures and Pressures , 1946 .

[10]  Rafa Labedi,et al.  Improved correlations for predicting the viscosity of light crudes , 1992 .

[11]  Nader Nariman-zadeh,et al.  Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms , 2009 .

[12]  Vida Varahrami,et al.  Good Prediction of Gas Price between MLFF and GMDH Neural Network , 2012 .

[13]  Abdul Azeez Abdul Raheem,et al.  Prediction of crude oil viscosity curve using artificial intelligence techniques , 2012 .

[14]  Ali Moeini,et al.  Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process , 2005 .

[15]  Adel M. Elsharkawy,et al.  Models for predicting the viscosity of Middle East crude oils , 1999 .

[16]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[17]  Nader Nariman-zadeh,et al.  Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition , 2002 .

[18]  Ali Naseri,et al.  A correlation approach for prediction of crude oil viscosities , 2005 .

[19]  Ali Abedini,et al.  A NEW CORRELATION FOR PREDICTION OF UNDERSATURATED CRUDE OIL VISCOSITY , 2010 .

[20]  Z. Schmidt,et al.  Large data bank improves crude physical property correlations , 1994 .

[21]  Mohammad Najafzadeh,et al.  Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers , 2011 .