Multidimensional risk classification with global sensitivity analysis to support planning operations in a transportation network of natural gas pipelines

Multidimensional risk assessment for natural gas pipelines has proved to be an efficient approach for companies to evaluate multiple risk perspectives and to determine different mitigation actions. Therefore, making decisions to ensure the system's best performance, taking into account safety issues, is a challenging task. This paper proposes a multidimensional risk classification with global sensitivity analysis by making a structured analysis of uncertainty that uses a Monte Carlo Simulation and visualization tools. For this analysis, five scenarios of a group of parameters are set according to operational and technical issues, consequences and the preferential properties of the model for pipeline sections. Then, sensitivity levels are statistically validated and defined for each section, thereby generating information on modifications to the classification of levels of risk. For example, 10% variation in the group of parameters related to the Critical Danger Radius (CDR), 6 out of 8 pipeline sections present high sensitivity levels; showing that their original risk classifications do not remain the same. Also, 4 sections are expected to be in the high risk class, while 1 section is expected to be in medium class and 3 sections in the low risk class. Consequently, managers should prioritize mitigation strategies to those sections with significant risk categories The advantages of our proposals are twofold. First, the proposed methodology enables decisions to be made on classifying risks which incorporate evidence on the sensitivity and uncertainties of natural gas pipelines. Secondly, effective information on classifying risk is provided to the decision-maker by using statistical validations and visualization tools. Thus, managers can enhance their perception of risks with regard to a natural gas pipeline, can generate insights to control mitigations and allocate resources to ensure safety for people, and can avoid losses regarding the environment and their companies' properties.