A Dynamic Predictive Analysis on Gas Pipeline Failure Consequence Using Bayesian Network

Abstract

Nowadays, due to the economical, environmental, and social concerns which are the three pillars of sustainable development objectives, natural gas is considered as one of the most important and critical energies. Natural gas pipeline failure and related losses is a kind of devastating disaster because the threats of natural gas failure consequences may provide a huge extension that can simply lead to cascading disasters. Therefore, maintenance and repair of the deteriorating infrastructure like buried gas pipelines have been considered significant considerations amongst stakeholders and researchers in recent years. In this thesis, a dynamic Bayesian network (BN) was used to investigate natural gas pipeline network failure consequences in a probabilistic way. Seven parent nodes including, age, diameter, length, depth, population, time of occurrence, and land-use have been considered for the developed model. Twelve consequences factors also were recognized based on the literature review and expert’s opinion. Production loss, asset loss, environmental loss, and social and reputational loss were also measured as the overall losses due to the failure consequences. The proposed model can manage both static and dynamic systems employing quantitative and/or qualitative data. Methods of the extreme-condition test, scenario analysis, sensitivity analysis, and partial validation test were utilized to validate the model and to detect the key and leading parent nodes and their effect on the overall loss index. The developed static and dynamic BN models will help the decision-makers to manage and prioritize their asset effectively. To demonstrate the applicability and effectiveness of the developed model, the gas pipeline network of the City of Regina, SK, in Canada is studied. The results show that age and diameters are the most important and sensitive parameters amongst others which need to take into consideration for disaster reaction decision-making and loss prevention.