Smart correlation of compositional data to saturation pressure

Saturation pressure is one of the foremost parameters of crude oil which plays a key role in petroleum calculations. Experimentally, determination of this parameter in laboratory is costly and labor demanding. In this study, an improved intelligent model based on neural network optimized with genetic algorithm-pattern search technique is proposed for building quantitative formulation between saturation pressure and compositional data, including temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. Genetic algorithm-pattern search technique is embedded in neural network formulation for finding optimal weights and biases of neural network. A comparison among the proposed model and published models in literature reveals the superiority of our model in terms of better accuracy and higher generalization. Improved neural networked showed R-square of 0.9892 and MSE of 17,617.99 which concludes that it is a promising alternative for determination of saturation pressure which is able to eliminating expenses of laboratory measurements and significantly saving time. This study showed GA considerably enhanced performance of conventional neural networks.