Particle Swarm Optimization Based Empirical Correlation for Prediction of Hydrate Formation in Deep-Water Pipeline

Abstract

Since formation of hydrate in deep water pipeline could cause problems such as decreasing hydrocarbon production and increasing operational cost and time, this work offers to ascertain when and where hydrate will form with respect to change in pressure and temperature in deep water gas pipeline. The pressure is relatively high in deep water pipeline, and it is entirely possible to meet the hydrate formation conditions and pose a significant operational and security challenge. The study aims to develop a correlation that will help in finding hydrate formation pressure and temperature conditions of gas mixture flowing in deep water pipeline. The correlation is based on gas hydrates formation temperature with and without concentration of inhibitors. On the basis of existing published experimental data from the work by ‘E. Dendy Sloan’ and ‘Riki Kabayashi’, a new correlation will be developed using Particle Swarm Optimization. This research provides an effective coefficients for predicting hydrate formation Pressure / Temperature conditions for deep water gas pipeline.