Pipeline risk factors analysis using the Pearson correlation coefficient method and the random forest importance factor method

Abstract

The safety of pipelines has become one of the key issues in academia and industry. With the development of data science, many methods have been proposed to handle a large amount of operational data and to understand the impact of various factors on pipeline safety. Weld failure is related to many factors, and identifying their correlation helps to grasp weld information better, develop maintenance strategies in advance, and improve weld management. In this study, two methods for understanding correlations of failure factors of girth welds were studied, the Pearson correlation coefficient method and the random forest importance coefficient method. Factors such as repair history, whether connector, whether crossing, and pipe joint length, are considered. The magnitude of the impact of each factor on pipeline safety can be judged to ensure that the hard requirements of each factor are met during the pipeline development process, while reducing the probability of risk to the pipeline. A case of a gas trunk line in China was studied to test the method. The relevance of key elements such as short pipe, repair history, pipe joint length, and geological hazards to the detection of non-destructive defects in pipelines were analyzed. The results show that correlation analysis of pipeline-related factors can provide guidance for pipeline safety and help reduce pipeline risks.