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Research Papers: Pipeline Systems

Intelligent Risk Assessment for Pipeline Third-Party Interference

[+] Author and Article Information
Jinqiu Hu1

College of Mechanical and Transportation Engineering,  China University of Petroleum, Beijing, China 102249

Laibin Zhang, Wei Liang

College of Mechanical and Transportation Engineering,  China University of Petroleum, Beijing, China 102249

Cunjie Guo

PetroChina Beijing Gas Pipeline Co., Ltd., Beijing, China 100012

1

Corresponding author.

J. Pressure Vessel Technol 134(1), 011701 (Dec 02, 2011) (9 pages) doi:10.1115/1.4004622 History: Received December 26, 2010; Accepted April 07, 2011; Revised April 07, 2011; Published December 02, 2011; Online December 02, 2011

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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Figures

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Figure 1

The illegal valves and hoses installed in the pipeline

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Figure 2

The drilled holes had been repaired by welding hats

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Figure 3

Third-party interference in the pipeline crossing project

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Figure 4

Gas pipeline failure caused by third-party interference to Shaanxi–Beijing gas pipeline

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Figure 5

Pipeline segmentation according to the information itself and its surroundings

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Figure 6

Risk assessment index architecture for the third-party interference

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Figure 7

Risk pattern classification model based on SOM theory

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Figure 8

Line 1#–line 3# of Shaanxi–Beijing gas pipeline

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Figure 9

Estimated risk value of selected pipeline sections

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Figure 10

Total score of potential hazard of selected pipeline sections

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Figure 11

Visualization of the SOM of potential hazard data

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Figure 12

The seven variables shown with bar-chart in each map unit (Blue: SA, Green: GS, Red: CO)

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Figure 13

Total score of leakage impact of selected pipeline sections

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Figure 14

Visualization of the SOM of risk data

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