Abstract
In petroleum industry, pipeline is singled out as it is the safest and the most economically viable means of transporting large quantities of oil and natural gas. However, accidents to pipelines because of the third-party interference have been recorded. An intelligent risk assessment approach is proposed to estimate the risk of each pipeline section and classify various risk patterns, using self-organization mapping neural network theory, which incorporates the factors of pipeline laying conditions, historical damage records, safety-related actions, management measures, and the environment around the underling pipeline. A field case study of Shaanxi–Beijing gas pipeline in China is undertook so that the effectiveness of the proposed risk pattern classification approach could be verified, which helps safety engineer to take effective and accurate safety measures according to different risk patterns.
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