Recognition of oil & gas pipelines operational states using graph network structural features

The monitoring and recognition of operational states pattern is a crucial part for maintaining the safety, reliability and profitability of oil and gas pipeline systems. However, there are fewer methods to monitor the operational status of long-distance pipelines through operational data alone. In this paper, a purely data -driven approach is proposed for detecting and identifying the operational status of pipeline systems based on machine learning methods and the data log of pipelines. Firstly, a logic rule-based method is proposed to enrich the labels for each segment of operational data. Secondly, a change point-based detection model is used to detect the change of operational state in pipeline system or equipment. Then, a framework of oil pipeline operational pattern recognition methods based on graph structural features is proposed. Finally, the proposed model is applied to a real-world data from a pipeline system in China. Both the accuracy and the breadth of the recognition results can be improved by the use of real-time data validation and a human-machine interface. The results show that the precision of a change point-based detection model can reach more than 85% for different scenarios, and a reduction in missed rate of 17%-26%. Compared with the statistical feature-based method, the proposed method has improved the accuracy for all types of scenarios to a certain extent. The most significant improvement in recognition accuracy was achieved in the valve switch state and the combined state, with an increase of 30.8% and 5% respectively.