An integrated framework for criticality evaluation of oil & gas pipelines based on fuzzy logic inference and machine learning

Oil & gas transportation pipeline is susceptible to failure because of the influence of external complex environment and internal aggressive medium. It is necessary to accurately evaluate the failure criticality of the pipeline for developing reasonable protective measures. However, due to the complexity and uncertainty of the failure scenarios of the oil & gas pipeline, direct quantitative evaluation of the failure criticality is very difficult. Therefore, this paper proposed a novel evaluation framework based on an integration of fuzzy logic inference and machine learning approaches. In this framework, transportation interruption effect, safety/health effect, environment/ecological effect, and equipment maintenance effect are set as the influencing factors on failure criticality of pipelines. Fuzzy logic inference was applied to generate the mapping relationship of the influencing factors to criticality index and establish the prediction model for criticality index evaluation of oil & gas pipelines. For facilitating the evaluation process, three machine learning approaches (i.e., multilayer perceptron, support vector regression and random forest) were employed to fit the relationship so as to construct an additional, easy-to-use prediction model. Furthermore, the sensitivity of each influencing factor was discussed by Sobol method. The results show that the random forest model has better prediction capability in comparison of the other two models, and safety/health effect and environment/ecological effect have the biggest impact on criticality. Finally, an application example of natural gas pipeline was conducted by using the proposed framework, which verified its practicability and flexibility.