Developing a simple-to-use predictive model for prediction of hydrate formation temperature

Abstract

ABSTRACTHydrate formation may be a common occurrence during oil and gas drilling and production operation when temperature of these solid crystalline compounds that formed in the presence of free water decreases at elevated pressure. Also, they have often been found responsible for operating difficulties at wellheads, pipelines and other processing equipment. Nowadays, because of the importance of predicting hydrate formation condition, different accurate methods have been used. Besides the experiential correlations that are common for predicting, the developments in the field of modelling led to the use of different methods in a thermodynamic way. In fact, because of the risk of experimental uncertainties and to remove the need for intricate analytic equations and empirical correlations, the computational intelligence model, which result in the lowest error and based on experimental data, is strongly proposed, in attempts to solve complex industrial problems. In this article, in order to predict gas hydrate formation condition, two smart techniques are established based on feed-forward neural network (artificial neural network (ANN)) which is optimised by imperialist competitive algorithm (ICA). The ICA-ANN model is conducted utilising the empirical data released in the literature and finally the performance of ICA-ANN model is compared with the conventional ANN model. Furthermore, they have been compared with an accurate thermodynamic model at different operating conditions. The outcomes, contrary to expectations, establish that the ICA-ANN model has poor performance when compared with the ANN.