Predictive risk-based model for oil and gas pipelines

L. Parvizsedghy, Tarek Zayed

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Among different means of oil and gas transportation, pipelines are considered to be the safest and reasonably efficient. However, reviewing the incidents recorded on oil and gas pipelines in the United States of America (1992-2012) proved that the consequences of failures in these pipelines have been considerable: over 5.5 billion dollars of asset damage, 380 fatalities and 1,500 injuries, in addition to more than 5.5 million barrels of product loss. The domain certainly requires attention. In addition, there is a lack of research available in this crucial field. Therefore, the objective of this research is to develop a risk model for oil and gas pipelines. Data from USA incidents recorded on oil and gas pipelines from 1992 to 2011 are utilized to build the intended model and identify risk factors leading to oil and gas pipeline failure while considering their consequences of failure. An artificial neural network (ANN) with two hidden layers through applying back propagation approach is trained to develop the model which will help decision makers to estimate how significant would be a failure on a pipeline for the identified risk factors.
Original languageEnglish
Title of host publicationProceedings, Annual Conference - Canadian Society for Civil Engineering
PublisherCanadian Society for Civil Engineering
Pages194-203
Number of pages10
Publication statusPublished - 1 Jan 2013
Externally publishedYes
EventAnnual Conference of the Canadian Society for Civil Engineering 2013: Know-How - Savoir-Faire, CSCE 2013 - Montreal, Canada
Duration: 29 May 20131 Jun 2013

Conference

ConferenceAnnual Conference of the Canadian Society for Civil Engineering 2013: Know-How - Savoir-Faire, CSCE 2013
Country/TerritoryCanada
CityMontreal
Period29/05/131/06/13

ASJC Scopus subject areas

  • Engineering(all)

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