A model for predicting failure of oil pipelines

Oil and gas pipelines transport millions of dollars of goods everyday worldwide. Even though they are the safest way to transport petroleum products, pipelines do still fail generating hazardous consequences and irreparable environmental damages. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models were limited to one failure type, such as corrosion failure, or relied mainly on expert opinion analysis. The objective of this paper is to develop a model that predicts the failure cause of oil pipelines based on factors other than corrosion. Two models are developed to help decision makers predict failure occurrence. Regression analysis and artificial neural networks (ANNs) models were developed based on historical data of pipeline accidents. The two models were able to satisfactory predict pipeline failures due to mechanical, operational, corrosion, third party and natural hazards with an average validity of 90% for the regression model and 92% for the ANN model. The developed models assist decision makers and pipeline operators to predict the expected failure cause(s) and to take the necessary actions to avoid them.

• Record URL:
  • https://doi.org/10.1080/15732479.2012.756918
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15732479
• Supplemental Notes:
  • Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Senouci, Ahmed
  • Elabbasy, Mohamed
  • Elwakil, Emad
  • Abdrabou, Bassem
  • Zayed, Tarek
• Publication Date: 2014-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 375-387
• Serial:
  • Structure and Infrastructure Engineering
  • Volume: 10
  • Issue Number: 3
  • Publisher: Taylor & Francis
  • ISSN: 1573-2479
  • EISSN: 1744-8980
  • Serial URL: http://www.tandfonline.com/loi/nsie20

Subject/Index Terms

• TRT Terms: Failure analysis; Mathematical prediction; Neural networks; Petroleum pipelines; Pipeline safety; Regression analysis; Risk analysis
• Subject Areas: Maintenance and Preservation; Pipelines; Safety and Human Factors; I61: Equipment and Maintenance Methods;

Filing Info

• Accession Number: 01518665
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 20 2014 1:39PM