A liquid loading prediction method of gas pipeline based on machine learning

The liquid loading is one of the most frequently encountered phenomena in the transportation of gas pipeline, reducing the transmission efficiency and threatening the flow assurance. However, most of the traditional mechanism models are semi-empirical models, and have to be resolved under different working conditions with complex calculation process. The development of big data technology and artificial intelligence provides the possibility to establish data-driven models. This paper aims to establish a liquid loading prediction model for natural gas pipeline with high generalization ability based on machine learning. First, according to the characteristics of actual gas pipeline, a variety of reasonable combinations of working conditions such as different gas velocity, pipe diameters, water contents and outlet pressures were set, and multiple undulating pipeline topography with different elevation differences was established. Then a large number of simulations were performed by simulator OLGA to obtain the data required for machine learning. After data preprocessing, six supervised learning algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), artificial neural network (ANN), plain Bayesian classification (NBC), and K nearest neighbor algorithm (KNN), were compared to evaluate the performance of liquid loading prediction. Finally, the RF and KNN with better performance were selected for parameter tuning and then used to the actual pipeline for liquid loading location prediction. Compared with OLGA simulation, the established data-driven model not only improves calculation efficiency and reduces workload, but also can provide technical support for gas pipeline flow assurance. (c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Automated summary

This paper aims to establish a liquid loading prediction model for natural gas pipeline using machine learning, which can accurately predict the liquid loading and provide technical support for gas pipeline flow assurance. The data-driven model improves calculation efficiency and reduces workload, and is based on a large number of simulations and various working conditions.