Natural gas plays an important role in the transition from fossil fuels to new energy sources. With the expansion of pipeline networks, there are also problems with the safety of pipeline network operations in the process of transportation. Among them, third-party damage is a key factor affecting the safety of pipelines. In this paper, the risk factors of third-party damage are analyzed, and an evaluation model of natural gas pipeline damage is established using the GeNIe Modeler. Through Bayesian network reverse reasoning and a maximum cause chain analysis from the four aspects of personnel, environment, management, and equipment, it was found that the top five factors that have significant influence on third-party damage, are safety investment, the completeness of equipment, safety inspection frequency, the management of residents along the pipeline, and safety performance, with the posteriori probability in the model of 97.3%, 95.4%, 95.2%, 95.1%, 95.1%, respectively. Consequently, it is necessary for pipeline operation companies to secure investment on safety, to make sure that the safety equipment (system) works and is in a good condition, to maintain the safety inspection frequency in an organization, to build a management system for residents along the pipeline, and to conduct routine safety performance assessments accordingly.

Oil depots in the coastal areas of China are prone to disasters caused by heavy rain due to the monsoon climate. Studies focusing on heavy rainfall vulnerability in coastal oil depots are limited. Therefore, we evaluated the safety of oil depots based on four factors in this study: personnel, equipment and facility, environment, and resilience. Complex networks, analytic hierarchy processes, and information entropy theory were used to establish an evaluation index system including four first-level indicators, nine second-level indicators, and 40 third-level indicators. Scores of 40 evaluation indicators were taken as the input, a vulnerability level of oil depots affected by heavy rain was gained as the output, and results were presented visually (different warning levels distinguished by color) to help oil depot enterprises improve their safety performance under extreme weather conditions.