Robust Model for the Determination of Wax Deposition in Oil Systems

Wax
deposition is a serious problem during oil production in the
petroleum industry. Therefore, accurate prediction of this solid deposition
problem can result in increasing the efficiency of oil/gas production.
In this article, a novel approach is proposed to develop a predictive
model for the estimation of wax deposition. An intelligent reliable
model is proposed using a robust soft computing approach, namely,
least-squares support vector machine (LSSVM) modeling optimized with
the coupled simulated annealing (CSA) optimization approach. Our results
demonstrate that there is good agreement between predictions based
on the CSA-LSSVM model and experimental data on wax deposition. Furthermore,
the performance of the newly developed model is compared with the
performance of neural network and multisolid models for predicting
wax deposition. The results of this comparison indicate that the proposed
method is superior, in terms of both accuracy and generality, to the
neural network and multisolid models. Finally, to check whether the
newly developed CSA-LSSVM model is statistically correct and valid,
the leverage approach, in which the statistical Hat matrix, the Williams
plot, and the residuals of the model results lead to the identification
of probable outliers, is applied. It is found that all of the wax
deposition experimental data used in the present study seem to be
reliable and that only one point is outside the applicability domain
of the developed models for wax deposition.