Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

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.

[1]  Q. D. Nguyen,et al.  Isothermal start-up of pipeline transporting waxy crude oil , 1999 .

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

[3]  Jennifer D. Adams,et al.  The Origin, Prediction and Impact of Oil Viscosity Heterogeneity on the Production Characteristics of Tar Sand and Heavy Oil Reservoirs , 2006 .

[4]  H. D. Beggs,et al.  Estimating the Viscosity of Crude Oil Systems , 1975 .

[5]  Payam Setoodeh,et al.  Artificial Neural Network Modeling of Surface Tension for Pure Organic Compounds , 2012 .

[6]  Tülay Adali,et al.  Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing , 2002, J. VLSI Signal Process..

[7]  E. Egbogah,et al.  An Improved Temperature-Viscosity Correlation For Crude Oil Systems , 1990 .

[8]  Yaxiong Zhang,et al.  An improved QSPR study of standard formation enthalpies of acyclic alkanes based on artificial neural networks and genetic algorithm , 2009 .

[9]  Laibin Zhang,et al.  Predicting formation lithology from log data by using a neural network , 2008 .

[10]  Changjian Zhou,et al.  Necessity and feasibility of improving the residual resistance factor of polymer flooding in heavy oil reservoirs , 2010 .

[11]  F. F. Farshad,et al.  Evaluation of empirically derived PVT properties for Gulf of Mexico crude oils , 1989 .

[12]  Etienne Barnard,et al.  The estimation of kinematic viscosity of petroleum crude oils and fractions with a neural net , 1993 .

[13]  A. M. Elsharkwy,et al.  Comparing classical and neural regression techniques in modeling crude oil viscosity , 2001 .

[14]  J. Barhen,et al.  Oil Reservoir Properties Estimation Using Neutal Networks , 1997 .

[15]  S. K. Das,et al.  Vapex: An Efficient Process for the Recovery of Heavy Oil and Bitumen , 1998 .

[16]  O. A. Falode,et al.  PREDICTION OF NIGERIAN CRUDE OIL VISCOSITY USING ARTIFICIAL NEURAL NETWORK , 2009 .

[17]  Francisco Navarro,et al.  Pressure-temperature-viscosity relationship for heavy petroleum fractions , 2007 .

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

[19]  H. D. Beggs,et al.  Correlations for Fluid Physical Property Prediction , 1980 .

[20]  Shouchun Wang,et al.  Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil , 2010 .

[21]  Mohamed A. Fahim,et al.  New correlation for predicting the viscosity of heavy petroleum fractions , 1995 .

[22]  K. Movagharnejad,et al.  A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide , 2011 .

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

[24]  John Lohrenz,et al.  Calculating Viscosities of Reservoir Fluids From Their Compositions , 1964 .

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

[26]  Ju-Nam Chew,et al.  A Viscosity Correlation for Gas-Saturated Crude Oils , 1959 .

[27]  Mohammad R. Riazi,et al.  Physical properties of heavy petroleum fractions and crude oils , 1996 .

[28]  A. Margaritis,et al.  The effects of non-Newtonian fermentation broth viscosity and small bubble segregation on oxygen mass transfer in gas-lift bioreactors: a critical review , 2004 .

[30]  Harvey T. Kennedy,et al.  A Correlation of the Viscosity of Hydrocarbon Systems With Pressure, Temperature and Composition , 1968 .

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

[32]  Sohrab Zendehboudi,et al.  Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization , 2012 .