Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis
Publication: Journal of Construction Engineering and Management
Volume 140, Issue 6
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.
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Acknowledgments
The authors gratefully acknowledge the support provided by Qatar National Research Fund (QNRF) for this research project under award No. QNRF-NPRP 09-901-2-343. The authors would also like to acknowledge the operational pipeline engineers of Qatar Petroleum for their invaluable suggestions and recommendations.
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Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 140 • Issue 6 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 2, 2013
Accepted: Dec 31, 2013
Published online: Mar 6, 2014
Published in print: Jun 1, 2014
Discussion open until: Aug 6, 2014
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