Application of Artificial Neural Networks on Measurement of Gas Hydrates in Pipelines

Abstract

In Transportation or transmission of Deep water hydrocarbon transportation pipeline, the hydrocarbon flow can decrease due to the growth of gas hydrates which lead to additional operational cost and time constraints. The monitoring of Deepwater transmission pipelines is quite crucial as they are operated at high pressures and low temperatures. So, the risk of Gas hydrate formation conditions is highly prevalent and poses a major operational and safety challenge. In recent times, Artificial Neural Network (ANN) is very critically used over the research of hydrates because of its pros of high data simulation capacity and accurate curve fitting nature. Therefore, the aim of this work is to provide the latest review on application of Artificial Neural Network (ANN) in the prediction of gas hydrate formation in Deepwater gas pipelines. Moreover, this study potentially paves the way for the knowledge of latest research carrying out in the prediction of gas hydrates which also helps in the development of advanced algorithms with respect to the work mentioned or discussed here.