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Precipitation of asphaltene largely affects the production rate of crude oil owing to the clogging of transportation pipeline as well as formation damage. Therefore, it is imperative to search for a robust method for the modeling of asphaltene precipitation. This paper aims to propose a new mathematical model for computing asphaltene precipitation as a function of titration data including dilution ratio, temperature, and molecular weight of solvent. This model is constructed based on integrating the support vector regression with the imperialist competitive algorithm. Optimization increases the performance of support vector regression by virtue of determining the optimal values of their parameters. The constructed model is applied to experimental data and its performance is compared with scaling equation which is traditionally employed for modeling of asphaltene precipitation. The results of this study show that the integration of imperialist competitive algorithm and support vector regression has a better performance than the scaling equation. This paper offers that the support vector regression optimized by virtue of the imperialist competitive algorithm is a reliable model for modeling asphaltene precipitation. [ABSTRACT FROM AUTHOR]

Use of intelligence based approach for modeling of crude oil saturation pressure is viable alternative since this parameter plays influential role in the reservoir calculation. The objective of current study is to develop a smart model based on fusing of support vector regression model and optimization technique for learn the relation between the saturation pressure and compositional data viz. temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. The optimization methods improve performance of the support vector regression (SVR) model through finding the proper value of their free parameters. The optimization methods which embedded in the SVR formulation in this study are genetic algorithm (GA), imperialist competitive algorithm (ICA), particle swarm optimization algorithm (PSO), cuckoo search algorithm (CS), and bat-inspired algorithm (BA). The optimized models were applied to experimental data given in open source literatures and the performance of optimization algorithm was assessed by virtue of statistical criteria. This evaluation resulted clearly show the superiority of BA when integrated with support vector regression for determining the optimal value of its parameters. In addition, the results of aforementioned optimized models were compared with currently available predictive approaches. The comparative results revealed that hybrid of BA and SVR yield robust model which outperform other models in term of higher correlation coefficient and lower mean square error. [ABSTRACT FROM AUTHOR]