Condition prediction models for oil and gas pipelines using regression analysis

Mohammed S. El-Abbasy, Ahmed Senouci, Tarek Zayed, Farid Mirahadi, Laya Parvizsedghy

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

Although they are the safest means of transporting oil and gas products, pipelines can sometimes fail with hazardous consequences and large business losses. The decision to replace, repair, or rehabilitate depends mainly on the condition of the pipeline. Assessing and predicting its condition is therefore a key step in the maintenance plan of a pipeline. Several models have recently been developed to predict pipeline failures and conditions. However, most of these models were limited to the use of corrosion as the sole factor to assess the condition of pipelines. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on expert opinion. The models were able to satisfactorily predict pipeline condition with an average percent validity above 96% when applied to the validation data set. The models are expected to help decision makers assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation planning.
Original languageEnglish
Article number4014013
JournalJournal of Construction Engineering and Management
Volume140
Issue number6
DOIs
Publication statusPublished - 1 Jun 2014
Externally publishedYes

Keywords

  • Condition prediction
  • Oil and gas pipelines
  • Quantitative methods
  • Regression analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Strategy and Management
  • Industrial relations
  • Building and Construction

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