INTEGRITY ASSESSMENT OF CORRODED OIL AND GAS PIPELINES USING MACHINE LEARNING: A SYSTEMATIC REVIEW

Author(s):  
Afzal Ahmed Soomro ◽  
Ainul Akmar Mokhtar ◽  
Jundika Chandra Kurnia ◽  
Najeebullah Lashari ◽  
Huimin Lu ◽  
...  
Author(s):  
Husain Mohammed Al-Muslim ◽  
Abul Fazal M. Arif

Mechanical damage in transportation pipelines is a threat to its structural integrity. Failure in oil and gas pipelines is catastrophic as it leads to personal fatalities, injuries, property damage, loss of production and environmental pollution. Therefore, this issue is of extreme importance to Pipeline Operators, Government and Regulatory Agencies, and local Communities. As mechanical damage can occur during the course of pipeline life due to many reasons, appropriate tools and procedures for assessment of severity are necessary. There are many parameters that affect the severity of the mechanical damage related to the pipe geometry and material properties, the defect geometry and boundary conditions, and the pipe state of strain and stress. Moreover, multiple damaged areas may exist and interact like in the case of a hit by a multiple-tooth excavator. The main objective of this paper is to determine the distance and orientation where interaction of multiple dents is significant. The strain and stress fields are evaluated for static pressure loading and the stress range and fatigue life are evaluated for cyclic pressure loading. Accordingly, guidelines are developed for the integrity assessment of multiple dents in oil and gas pipelines. The input parameters of the problem including the pipe material, pipe geometry, dent dimensions, and distance and orientation between two dents have a great variability. Therefore, probabilistic design approach is applied to determine the sensitivity and correlation between the output and input parameters. The base case deterministic FEA model has been validated with full-instrumented full-scale tests conducted by Pipeline Research Council International as part of their active program to fully characterize mechanical damage.


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