Characterization of Defects on Ferromagnetic Tubes Using Magnetic Flux Leakage

2019 ◽  
Vol 55 (5) ◽  
pp. 1-10 ◽  
Author(s):  
V. Suresh ◽  
A. Abudhahir ◽  
Jackson Daniel
Author(s):  
Vanessa Co ◽  
Scott Ironside ◽  
Chuck Ellis ◽  
Garrett Wilkie

Management of mechanical damage is an issue that many pipeline operators are facing. This paper presents a method to characterize dents based on the analysis of the BJ Vectra Magnetic Flux Leakage (MFL) tool signals. This is an approach that predicts the severity of mechanical damage by identifying the presence of some key elements such as gouging, cracking, and metal loss within dents as well as multiple dents and wrinkles. Enbridge Pipelines Inc. worked with BJ Services to enhance the knowledge that can be gained from MFL tool signals by defining tool signal subtleties in dents. This additional characterization provides information about the existence of gouging, metal loss, and cracking. This has been accomplished through detailed studies of the ILI data and follow-up field investigations, which validate the predictions. One of the key learnings has been that the radial and circumferential components of the MFL Vectra tool are highly important in the characterization and classification of mechanical damage. Non-destructive examination has verified that predictions in detecting the presence of gouging and cracking (and other defects within dents based on tool signals) have been accurate.


Author(s):  
Kevin W. Ferguson

With the age of the original Panhandle Eastern Pipeline (PEPL) Company pipelines, it’s not a matter of if anomalies will be found when an ILI tool is run, it’s a matter of how many and how severe. When a final report is received from an ILI vendor, burst pressures are typically calculated using Modified B31G, 0.85dL. The results can seem unmanageable, but success has been had doing further assessments on some anomalies without excavating them all. This assessment has been developed and performed by PEPL on three sets of Tuboscope ILI data and one set of Baker Hughes CPIG data. The method to be discussed was first employed in 2002. It provides a more accurate characterization of the defect and provides the company the ability to more effectively allocate resources. Efforts have been made to review the color scan of a vendor’s raw High Resolution Magnetic Flux Leakage (HRMFL) data, and perform an assessment using Effective Area Analysis without excavating hundreds of anomalies that prove no threat to the pipeline. This assessment is done by hand on the computer and in many cases returns a burst pressure higher than that calculated using Modified B31G, 0.85dL. The following is a case study that shows how multiple defects have been assessed prior to excavation in an attempt to more accurately characterize the defect, and allow for a better allocation of resources. Digs have been performed to validate the process, and the results will be discussed.


The application of hall sensors in Magnetitic Flux Leakage (MFL) has played an important role in Above Storage Tank (AST) on detection of defect caused by corrosion to improve productivity and to avoid catastrophe. The MFL sensor measured magnetic flux distribution in three axes Bx , By and Bz. Currently, there are several signal monitoring methods constructed by analysing MFL signal distribution upon defect detection. This paper presents the methodology of optimized Integrated Kurtosis-based Algorithm for Z-filter (I-KazTM) Coefficient using multilevel signal decomposition technique to analyse the MFL signal distribution on the defect in the correlation of MFL scanning device speed and position. The MFL scanning device comprises 11 hall effect sensors position in array coupled with a linear guide to ensuring a constant velocity of scanning. In order to obtain an optimum signal distribution, I-KazTM 3D is proposed as one of the derivatives of I-KazTM to analyse multiple velocities of scanning. The characterization of the defect can be estimated by analysing the deflection of magnetic flux leakage in the y-axis, By as the scanner approach the defect region before being analysed by I-KazTM from the beginning until the end of the workpiece.


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