Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause. These problems could result in reducing production performance or even production stoppage for a long time. In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients (R2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.