Development of robust model to estimate gas–oil interfacial tension using least square support vector machine: Experimental and modeling study

Abstract The measuring of physical properties in laboratory is very important issue in petroleum industry. Secondary and tertiary oil recovery, gas condensate recovery, especially by gas injection, near-critical fluids recovery and wettability alteration surface tensions are very important to measure. One objective of this study is to perform a precise measuring procedure employing the pendant drop technique. Iranian oil reservoir samples as denser phase and its immiscible injecting gas are used at reservoir condition. While experimental measurements are often expensive and time-consuming, models are commonly used. Moreover, this study presents the potential of the least squares support vector machines (LSSVM) modeling approach to predict the gas–oil interfacial tension. To develop the model, a total of 75 data generated from our experiments covering a wide temperature range of 100 through 200 F and a wide pressure range of 14.7 through 5000 psi are used. Genetic algorithm (GA) as population based stochastic search algorithm was used to gain the optimal LSSVM models parameters respectively. The results revealed that the GA-LSSVM are capable of capturing the complicated nonlinear relationship between the input and output variables. For the purpose of predicting gas–oil interfacial tension, the GA-LSSVM models yielded the mean absolute error (MAE) and coefficient of determination (R2) values of 1.6028 and 0.9988, respectively for the whole data set.

[1]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..

[2]  A. Bahadori,et al.  A LSSVM approach for determining well placement and conning phenomena in horizontal wells , 2015 .

[3]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[4]  M. Ahmadi.  Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[5]  M. Ahmadi,et al.  New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept , 2012 .

[6]  Eric James Mackay,et al.  The Importance of Interfacial Tension on Fluid Distribution During Depressurization , 1998 .

[7]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[8]  Muhsin Tunay Gençoglu,et al.  Prediction of flashover voltage of insulators using least squares support vector machines , 2009, Expert Syst. Appl..

[9]  A. Bahadori,et al.  Prediction of a solid desiccant dehydrator performance using least squares support vector machines algorithm , 2015 .

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

[11]  D. Rao,et al.  Solubility, miscibility and their relation to interfacial tension in ternary liquid systems , 2006 .

[12]  A. Pearce,et al.  Measuring interfacial tensions in a gas-condensate system with a laser-light-scattering technique , 1990 .

[13]  Ali Elkamel,et al.  Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization , 2013 .

[14]  Yildiray Cinar,et al.  An experimental investigation of the balance between capillary, viscous, and gravitational forces during CO2 injection into saline aquifers , 2011 .

[15]  Mohammad Masoumi,et al.  Evolving Smart Model to Predict the Combustion Front Velocity for In Situ Combustion , 2015 .

[16]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  Ali Eslamimanesh,et al.  Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen + water soluble organic promoters using Support Vector Machine algorithm , 2012 .

[20]  Mohammad Ali Ahmadi,et al.  Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach , 2014 .

[21]  Mohammad Ali Ahmadi,et al.  Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion , 2012 .

[22]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[23]  Mohammad Ali Ahmadi,et al.  Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs , 2014 .

[24]  Mohammad Masoumi,et al.  Evolving Connectionist Model to Monitor the Efficiency of an In Situ Combustion Process: Application to Heavy Oil Recovery , 2014 .

[25]  M. Ahmadi.  Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm , 2011 .

[26]  Zhuoyong Zhang,et al.  Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine. , 2009, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[27]  Mohammad Ali Ahmadi,et al.  Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: Application to reservoir simulation , 2015 .

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

[29]  Karen Schou Pedersen,et al.  Phase Behavior of Petroleum Reservoir Fluids , 2006 .

[30]  V. Vapnik.  Pattern recognition using generalized portrait method , 1963 .

[31]  Bjørn Kvamme,et al.  Measurements and modelling of interfacial tension for water + carbon dioxide systems at elevated pressures , 2007 .

[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 .

[33]  D. Broseta,et al.  CO2/water interfacial tensions under pressure and temperature conditions of CO2 geological storage , 2007 .