An improved support vector regression model for estimation of saturation pressure of crude oils

• In current study, compositional data of crude oils were formulated to saturation pressure.
• Optimized SVR was employed for modeling aforementioned formulation.
• GA, PSO, ICA, CS and BA were optimization techniques which used in this study.
• BA demonstrated better performance compared than other optimization technique and previous published models.

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.