Abstract
The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.
Highlights
As the safest and most economical way to transport dangerous and flammable substances in large volumes and over long distances, a pipeline network is built around the stable, continuous, and safe operation under an increasing consumption of natural gas, petroleum, and refined products [1].To prevent the occurrence of a breakdown and to maintain the integrity of related assets, inspection has been used to ensure that all pipes and equipment are fit-for-service
Based on the analysis above, we propose a spatial statistic based risk assessment approach to prioritize the inspection of pipeline networks
Through the application of the spatial statistic model: Poisson point process, the modeled object is transformed from a stretch or a section of a pipeline to the whole district where the pipeline network is located, which overcomes the difficulty of having insufficient data for model building
Summary
As the safest and most economical way to transport dangerous and flammable substances in large volumes and over long distances, a pipeline network is built around the stable, continuous, and safe operation under an increasing consumption of natural gas, petroleum, and refined products [1]. The amount of failure data is too small to use frequentist probability based methods when building a model for a stretch or section of a pipeline network. The basis for the probability assignment is noteworthy because the assumptions and hypotheses of the aforementioned approaches cannot be falsified This is illustrated by the example of Bayesian probability based methods, which use the relative level of risk uncertainty to sort. Through the application of the spatial statistic model: Poisson point process, the modeled object is transformed from a stretch or a section of a pipeline to the whole district where the pipeline network is located, which overcomes the difficulty of having insufficient data for model building This approach provides a credible basis for risk assessment because the assumptions and hypotheses in our approach can be falsified by the application of statistical tests. The application of our approach is described in a case study, and the availability is verified
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