Characterising metal loss and gouging associated with dents, and the identification of corrosion types.
Characterising metal loss and gouging associated with dents, and the identification of corrosion types near the longitudinal seam remain two significant obstacles in inline inspection (ILI) integrity assessments of metal loss defects. Denting poses a particular difficulty, in that secondary features of corrosion and gouging present very different safety and serviceability scenarios: while corrosion in a dent is often minor, metal loss caused by gouging can be quite severe.
Selective seam weld corrosion (SSWC) along older low-frequency electric resistance welding (ERW) seams also presents two different integrity scenarios, and the ILI tool must differentiate the more serious SSWC condition from the less severe conventional corrosion which just happens to be near a low-frequency ERW seam. Both pose identification difficulties that require improved classification of anomalies by ILI in order to enhance pipeline safety.
This paper presents two new classifiers for magnetic flux leakage (MFL)
tools, as this rugged technology is commonly used by pipeline operators for integrity assessments. The new classifier distinguishing between dents with gouges, dents with corrosion monitoring
, and smooth dents uses a high and low magnetisation level approach, combined with a new method for analysing the signals.
In this classifier, detection of any gouge signal is paramount, with the conservative nature of the classifier ensuring reliable identification of gouges. In addition to the high and low field data, the classifier uses the number of distinct metal loss signatures at the dent, the estimated maximum metal loss depth, and the location of metal loss signatures relative to dent profile (e.g. Apex, Shoulder).
The new classifier that distinguishes SSWC from corrosion near the longitudinal weld uses two orientations of the magnetic field: the traditional axial field and a helical magnetic field. Detection of any long narrow metal loss is key to the classifier, and here again its conservative nature ensures a high degree of identification of SSWC. The relative amplitude of the corrosion signal for the two magnetisation directions is an important characteristic, as are the length and width measures of the corrosion features.
These models were developed using ILI data from pipeline anomalies identified during actual inspections. Inspection measurements from excavations, as well as pipe removed from service for laboratory analysis and pressure testing, were used to confirm the results.
In the last decade, the detection capability for ILI tools has improved, enabling smaller corrosion and shallower dents to be reported. While many tools are better at detecting the seam weld in well-trimmed ERW pipe, reporting the smaller corrosion that coincides with dents or the long seam has increased the regulatory numbers of excavations in many countries.
The goal of the regulations is to ensure that mechanical damage in dents and selective corrosion of the long seam (both potentially damaging anomalies) are always detected. In keeping with the spirit of the regulations, the goal of the work presented here is to build classifiers that combine the measurements from multiple sensing systems to detect mechanical damage in dents and selective corrosion of the long seam, while dismissing many of the smaller corrosion features that do not impact pipeline performance.
The classifiers are designed to be conservative, thus some harmless corrosion anomalies may be excavated. This increases the likelihood that all potentially injurious mechanical damage and selective seam corrosion will be identified. This paper discusses the development and verification of these classifiers.
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