Hybrid of Two Heuristic Optimizations with LSSVM to Predict Refractive Index as Asphaltene Stability Identifier

Asphaltene precipitation is a big challenge in the petroleum industry. This motivated us to develop a reliable model between refractive index and SARA fraction as a tool for the diagnosis of asphaltene stability. Least-square support vector machine (LSSVM), due to its several unique advantages, has been successfully verified as a predicting method in recent years. However, the success of LSSVMs depends on the adequate choice of the kernel and regularization parameters. We proposed the combination of two search algorithms to deal with the problem of support vector machine parameter selection. On this basis, we combined coupled simulated annealing (CSA) and the Nelder and Mead Simplex method to optimize the parameters. In this hybrid optimization, first, CSA finds suitable starting values and these are passed to the simplex method in order to tune the result. The LSSVM results are promising and accurate, and outperform both neural network and empirical models existing in literature.

[1]  J. Castillo,et al.  Measurement of the Refractive Index of Crude Oil and Asphaltene Solutions: Onset Flocculation Determination , 2010 .

[2]  G. Hirasaki,et al.  ASPHALTENE PRECIPITATION AND SOLVENT PROPERTIES OF CRUDE OILS , 1998 .

[3]  J. G. Meijer,et al.  Influence of Temperature and Pressure on Asphaltene Flocculation , 1984 .

[4]  J. Buckley,et al.  Crude oil and asphaltene characterization for prediction of wetting alteration , 2002 .

[5]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .

[6]  Roger M. Butler,et al.  Recovery of heavy oils using vapourized hydrocarbon solvents: further development of the Vapex process , 1993 .

[7]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[8]  J. Lotrian,et al.  Refraction index measurement by a laser-cube-capillary technique , 2001 .

[9]  E. Hegazi,et al.  Measuring the refractive index of crude oil using a capillary tube interferometer , 2003 .

[10]  Roger M. Butler,et al.  A New Process (VAPEX) For Recovering Heavy Oils Using Hot Water And Hydrocarbon Vapour , 1991 .

[11]  N. E. Burke,et al.  Measurement and Modeling of Asphaltene Precipitation (includes associated paper 23831 ) , 1990 .

[12]  A. Hamed,et al.  Single exposure photography applied to a slow flow field , 1989 .

[13]  Kosta J. Leontaritis,et al.  Asphaltene deposition: a survey of field experiences and research approaches , 1988 .

[14]  A. A. Hamza,et al.  Multiple-beam interference fringes applied to GRIN optical waveguides to determine fiber characteristics. , 1985, Applied optics.

[15]  A. Hamed,et al.  Study of fluid flow using speckle interferometry , 1987 .

[16]  Hans E. Kimmel,et al.  AIChE Spring National Meeting , 2004 .

[17]  A. Solaimany-Nazar,et al.  Modeling of asphaltene deposition in oil reservoirs during primary oil recovery , 2011 .

[18]  Saad F. Alkafeef,et al.  A Simplified Method to Predict and Prevent Asphaltene Deposition in Oilwell Tubings: Field Case , 2005 .

[19]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[20]  C. Phelps,et al.  Asphaltene Precipitation from Live Oils: An Experimental Investigation of Onset Conditions and Reversibility , 2000 .

[21]  B Li,et al.  On-column refractive-index detection based on retroreflected beam interference for capillary electrophoresis. , 1998, Applied optics.

[22]  W. B. Gardner,et al.  Nondestructive interferometric measurement of the delta and alpha of clad optical fibers. , 1977, Applied optics.

[23]  Walter G Chapman,et al.  Application of the One-Third rule in hydrocarbon and crude oil systems , 2010, Fluid Phase Equilibria.

[24]  INVESTIGATION OF ASPHALTENE PRECIPITATION AT ELEVATED TEMPERATURE , 1998 .

[25]  Sungzoon Cho,et al.  Multiple permeability predictions using an observational learning algorithm , 2000 .

[26]  R. F. Krueger.  An Overview of Formation Damage and Well Productivity in Oilfield Operations , 1986 .

[27]  A. V. Bergen,et al.  Screening of crude oils for asphalt precipitation: Theory, practice, and the selection of inhibitors , 1995 .

[28]  S. Basu,et al.  Statistical analysis on parameters that affect wetting for the crude oil/brine/mica system , 2002 .

[29]  Zhengxiao Guo,et al.  Artificial neural network modelling of hydrogen storage properties of Mg-based alloys , 2004 .

[30]  J. Castillo,et al.  Optical fiber extrinsic refractometer to measure RI of samples in a high pressure and temperature systems: Application to wax and asphaltene precipitation measurements , 2006 .

[31]  J. Buckley,et al.  A Two-Component Solubility Model of the Onset of Asphaltene Flocculation in Crude Oils , 2001 .

[32]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  Gavin C. Cawley,et al.  Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters , 2007, J. Mach. Learn. Res..

[35]  E. Behar,et al.  Characterisation of Asphaltenes and Modelling of Flocculation – State of the Art , 2006 .

[36]  Johan A. K. Suykens,et al.  Coupled Simulated Annealing , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  M. Vafaie-Sefti,et al.  Application of association theory to the prediction of asphaltene deposition: Deposition due to natural depletion and miscible gas injection processes in petroleum reservoirs , 2006 .

[38]  G. A. Mansoori,et al.  Organic deposition from reservoir fluids : a thermodynamic predictive technique , 1991 .

[39]  J. Buckley.  Predicting the Onset of Asphaltene Precipitation from Refractive Index Measurements , 1999 .

[40]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[41]  T. G. Monger,et al.  Organic Deposition During CO2 and Rich-Gas Flooding , 1991 .

[42]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[43]  M. A. Ali,et al.  The Effect of Asphaltene Precipitation on Carbonate-Rock Permeability: An Experimental and Numerical Approach , 1998 .

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

[45]  D. Malacara-Hernández,et al.  PRINCIPLES OF OPTICS , 2011 .

[46]  Serguei A. Alexandrov,et al.  Interference method for determination of the refractive index and thickness , 2000 .

[47]  A. Firoozabadi,et al.  Thermodynamic micellizatin model of asphaltene precipitation from petroleum fluids , 1996 .

[48]  Tan Zhu,et al.  Optimization of phosphorus removal from secondary effluent using simplex method in Tianjin, China. , 2005, Journal of hazardous materials.

[49]  Ali Chamkalani,et al.  Correlations between SARA Fractions, Density, and RI to Investigate the Stability of Asphaltene , 2012 .

[50]  Hendra J. Tarigan,et al.  Capillary-Scale Refractive Index Detection by Interferometric Backscatter , 1996 .

[51]  G. Romanos,et al.  Experimental investigation of asphaltene deposition mechanism during oil flow in core samples , 2007 .

[52]  J. Roux,et al.  Viscosimetric and Neutron Scattering Study of Asphaltene Aggregates in Mixed Toluene/Heptane Solvents , 1998 .

[53]  M. Riazi,et al.  Use of the Refractive Index in the Estimation of Thermophysical Properties of Hydrocarbons and Petroleum Mixtures , 2001 .

[54]  Z Otremba,et al.  The impact on the reflectance in VIS of a type of crude oil film floating on the water surface. , 2000, Optics express.

[55]  Abbas Firoozabadi,et al.  Molecular-thermodynamic framework for asphaltene-oil equilibria , 1998 .

[56]  H. S. Fogler,et al.  Study of Asphaltene Precipitation Using Refractive Index Measurement , 2003 .

[57]  J. Speight.  The Chemistry and Technology of Petroleum , 1980 .

[58]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..