A soft computing approach for the determination of crude oil viscosity: Light and intermediate crude oil systems

Abstract Crude oil viscosity is a key property needed for petroleum engineering analysis such as evaluation of fluid flow in porous media, reservoir performance, reservoir simulation, etc. This property is traditionally measured through expensive and time consuming laboratory measurements. In this communication, about 1500 dead oil viscosity data points of light and intermediate crude oil systems from various geological locations have been collected. Afterward, a soft computing approach, namely least square support vector machine (LSSVM), has been utilized to develop two distinct viscosity models for temperatures below and above 313.15 K. The parameters of these models have been optimized using coupled simulated annealing (CSA) optimization tool. The results of this study indicated that the developed models can predict dead oil viscosity at all temperatures and oil API gravities with enough accuracy. In addition, statistical and graphical error analyses illustrated that the proposed CSA-LSSCM models outperform all of pre-existing models. Besides, the relevancy factor showed that oil API gravity has the greatest effect on dead oil viscosity. Finally, the Leverage approach demonstrated that the proposed models are statistically valid and acceptable, and only 2% of the data points may be regarded as the probable outliers.

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

[2]  Wen Shen,et al.  ARX model based fault detection and diagnosis for chillers using support vector machines , 2014 .

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

[4]  Ali Naseri,et al.  Toward reservoir oil viscosity correlation , 2013 .

[5]  Arash Azamifard,et al.  Toward a predictive model for predicting viscosity of natural and hydrocarbon gases , 2014 .

[6]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[7]  Soe W. Myint,et al.  A support vector machine to identify irrigated crop types using time-series Landsat NDVI data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[8]  William D. McCain,et al.  The properties of petroleum fluids , 1973 .

[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]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[11]  A. Mohammadi,et al.  Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor , 2015 .

[12]  W. Svrcek,et al.  One parameter correlation for bitumen viscosity , 1988 .

[13]  J. Ancheyta,et al.  Comparison of correlations based on API gravity for predicting viscosity of crude oils , 2014 .

[14]  F. F. Farshad,et al.  Viscosity Correlations for Gulf of Mexico Crude Oils , 1995 .

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

[16]  Abdolhossein Hemmati-Sarapardeh,et al.  On determination of natural gas density: Least square support vector machine modeling approach , 2015 .

[17]  O. Olamigoke,et al.  A Correlation to Predict the Viscosity of Light Crude Oils , 2006 .

[18]  Marco Villa,et al.  Pressure-volume-temperature correlations for heavy and extra heavy oils , 1995 .

[19]  Majid Amidpour,et al.  A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils , 2014 .

[20]  Ali Naseri,et al.  Reservoir oil viscosity determination using a rigorous approach , 2014 .

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

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

[23]  Tarek Ahmed,et al.  Reservoir Engineering Handbook , 2002 .

[24]  Abdolhossein Hemmati-Sarapardeh,et al.  Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature , 2015, Korean Journal of Chemical Engineering.

[25]  M. A. Al-Marhoun.  Evaluation of empirically derived PVT properties for Middle East crude oils , 2004 .

[26]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[27]  Marco Villa,et al.  Reliability Analysis on PVT Correlations , 1994 .

[28]  Abdolhossein Hemmati-Sarapardeh,et al.  Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids , 2014 .

[29]  Kaiyun Fu,et al.  The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process , 2014 .

[30]  T. Bennison.  PREDICTION OF HEAVY OIL VISCOSITY , 1998 .

[31]  Alireza Bahadori,et al.  Rigorous modeling for prediction of barium sulfate (barite) deposition in oilfield brines , 2014 .

[32]  C. Ghotbi,et al.  Experimental Investigation of Rheological and Morphological Properties of Water in Crude Oil Emulsions Stabilized by a Lipophilic Surfactant , 2013 .

[33]  F. Gharagheizi,et al.  Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding , 2015 .

[34]  Mohammad Soleimani Lashkenari,et al.  Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations , 2013, Petroleum Science.

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

[36]  A. Naseri,et al.  A NEURAL NETWORK MODEL AND AN UPDATED CORRELATION FOR ESTIMATION OF DEAD CRUDE OIL VISCOSITY , 2012 .

[37]  Feng Ye,et al.  Application of support vector machine in drag reduction effect prediction of nanoparticles adsorption method on oil reservoir’s micro-channels , 2015 .

[38]  Munirudeen A. Oloso,et al.  Prediction of Crude Oil Viscosity and Gas/Oil Ratio Curves Using Recent Advances to Neural Networks , 2009 .

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

[40]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

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

[42]  R. Al-Maamari,et al.  New Correlating Parameter for the Viscosity of Heavy Crude Oils , 2006 .

[43]  S. Ayatollahi,et al.  A rigorous approach to predict nitrogen-crude oil minimum miscibility pressure of pure and nitrogen mixtures , 2015 .

[44]  M. Garay,et al.  Oil Mixture Viscosity Behavior: Use in Pipeline Design , 2001 .

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

[46]  A. N. El-hoshoudy,et al.  New correlations for prediction of viscosity and density of Egyptian oil reservoirs , 2013 .

[47]  A. Mehrotra.  Generalized one-parameter viscosity equation for light and medium liquid hydrocarbons , 1991 .

[48]  Cem Sarica,et al.  Assessment and Development of Heavy Oil Viscosity Correlations , 2005 .

[49]  Alireza Bahadori,et al.  Determination of the equilibrated calcium carbonate (calcite) scaling in aqueous phase using a reliable approach , 2014 .

[50]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[51]  Colin R. Goodall,et al.  13 Computation using the QR decomposition , 1993, Computational Statistics.

[52]  O. Alomair,et al.  A viscosity prediction model for Kuwaiti heavy crude oils at elevated temperatures , 2014 .

[53]  A. Mohammadi,et al.  Experimental measurement and modeling of saturated reservoir oil viscosity , 2014, Korean Journal of Chemical Engineering.

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