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Journal of Pressure Vessel Technology | Volume 134 | Issue 1 | Research Paper
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
Article
References
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Citing Articles

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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References

1
Xueping, D., Yan, L., Rong, L., Du, X., Liu, Y., Liu, R., Zhang, Y., 2010, “Prediction of Fire Risk Region and Prevention Measures for Natural Gas Pipeline,” http://news.gasshow.com/News/SimpleNews.aspx?newsid = 278453
2
Jo, Y. D., and Ahn, B. J., 2002, “Analysis of Hazard Area Associated With High Pressure Natural-Gas Pipeline,” J. Loss Prev. Process Ind., 15 (3), pp. 179–188.  [CrossRef]
3
National Transportation Safety Board Pipeline Accident Report for Texas Eastern Transmission Corporation Natural Gas Pipeline Explosion and Fire, Edison, New Jersey, March 23, 1994, PB95-916501, NTSB/PAR-95/01.
4
Miura, S., and Mishima, T., 1995, “Damage Monitoring System for Pipelines,” "IGRC95 Proceedings", Nov. 6–9.
5
Cao, Y. G., Shi, Y. J., Zhang, S. H., Xue, S. F., and Sun, X. Y., 2010, “Research on Strength Degradation of Oil Transmission Pipeline by Third-Party Damages,” Eng. Failure Anal., 17 (4), pp. 992–1007.  [CrossRef]
6
Cao, Y. G., Sun, X. Y., Zhang, S. H., and Xue, S. F., 2010, “Field Experiments and FEM Analysis of Third-Party Damaged Oil Transmission Pipeline,” Eng. Failure Anal., 17 (1), pp. 344–352.  [CrossRef]
7
Brito, A. J., and de AlmeidaA. T., 2009, “Multi-Attribute Risk Assessment for Risk Ranking of Natural Gas Pipelines,” Reliab. Eng. Syst. Saf., 94 (2), pp. 187–198.  [CrossRef]
8
Gharabagh, M. J., Asilian, H., and Mortasavi, S. B., 2009, “Comprehensive Risk Assessment and Management of Petrochemical Feed and Product Transportation Pipelines,” J. Loss Prev. Process Ind., 22 (4), pp. 533–539.  [CrossRef]
9
Markowski, A. S., and Mannan, M. S., 2009, “Fuzzy Logic for Piping Risk Assessment (pfLOPA),” J. Loss Prev. Process Ind., 22 (6), pp. 921–927.  [CrossRef]
10
Park, K. S., Lee, J. H., and Jo, Y. D., 2004, “An Approach to Risk Management of City Gas Pipeline,” Process Saf. Environ. Prot., 82 (6), pp. 446–452.  [CrossRef]
11
GB50251-2003, “Code for Design of Gas Transmission Pipeline Engineering, Ministry of Construction,” 2003-10-01.
12
Li, T. S., and Huang, C. L., 2009, “Defect Spatial Pattern Recognition Using a Hybrid SOM–SVM Approach in Semiconductor Manufacturing,” Expert Syst. Appl., 36 (1), pp. 374–385.  [CrossRef]
13
Kangas, J., and Kohonen, T., 1996, “Developments and Applications of the Self-Organizing Map and Related Algorithms,” Math. Comput. Simul., 41 (1–2), pp. 3–12.  [CrossRef]
14
Kohonen, T., 2006, “Self-Organizing Neural Projections,” Neural Networks, 19 (6–7), pp. 723–733.  [CrossRef]

Figures

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+The illegal valves and hoses installed in the pipeline
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The illegal valves and hoses installed in the pipeline
Figure 1

The illegal valves and hoses installed in the pipeline

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+The drilled holes had been repaired by welding hats
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The drilled holes had been repaired by welding hats
Figure 2

The drilled holes had been repaired by welding hats

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+Third-party interference in the pipeline crossing project
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Third-party interference in the pipeline crossing project
Figure 3

Third-party interference in the pipeline crossing project

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+Gas pipeline failure caused by third-party interference to Shaanxi–Beijing gas pipeline
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Gas pipeline failure caused by third-party interference to Shaanxi–Beijing gas pipeline
Figure 4

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

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+Pipeline segmentation according to the information itself and its surroundings
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Pipeline segmentation according to the information itself and its surroundings
Figure 5

Pipeline segmentation according to the information itself and its surroundings

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+Risk assessment index architecture for the third-party interference
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Risk assessment index architecture for the third-party interference
Figure 6

Risk assessment index architecture for the third-party interference

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+Risk pattern classification model based on SOM theory
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Risk pattern classification model based on SOM theory
Figure 7

Risk pattern classification model based on SOM theory

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+Line 1#–line 3# of Shaanxi–Beijing gas pipeline
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Line 1#–line 3# of Shaanxi–Beijing gas pipeline
Figure 8

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

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+Estimated risk value of selected pipeline sections
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Estimated risk value of selected pipeline sections
Figure 9

Estimated risk value of selected pipeline sections

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+Total score of potential hazard of selected pipeline sections
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Total score of potential hazard of selected pipeline sections
Figure 10

Total score of potential hazard of selected pipeline sections

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+Visualization of the SOM of potential hazard data
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Visualization of the SOM of potential hazard data
Figure 11

Visualization of the SOM of potential hazard data

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+The seven variables shown with bar-chart in each map unit (Blue: SA, Green: GS, Red: CO)
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The seven variables shown with bar-chart in each map unit (Blue: SA, Green: GS, Red: CO)
Figure 12

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

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+Total score of leakage impact of selected pipeline sections
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Total score of leakage impact of selected pipeline sections
Figure 13

Total score of leakage impact of selected pipeline sections

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+Visualization of the SOM of risk data
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Visualization of the SOM of risk data
Figure 14

Visualization of the SOM of risk data

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