Modeling of Gas Hydrate Formation in the Presence of Inhibitors by Intelligent Systems

Gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. the main purpose of this study is investigating phenomenon of gas hydrate formation with the presence of kinetic inhibitors in operation gas transmission, and prediction of gas hydrate formation rate in the pipeline. in this regard, by using experimental data and intelligent systems (artificial neural networks and adaptive neural–fuzzy system), two different high efficient and accurate models were designed to predict hydrate formation rate of co2, c1, c3, and i-c4. it was found that such models can be used as powerful tools, for prediction of gas hydrate formation rate with total average of absolute deviation less than 6%.