With the current generation of in-line inspection (ILI) tools capable of recording terabytes of data per inspection and obtaining millimeter resolution on features, integrity sciences are becoming awash in a sea of data. However, without proper alignment and relationships, all this data can be at best noise and at worst lead to erroneous assumptions regarding the integrity of a pipeline system. This paper will explore the benefits of a statistical alignment method utilizing joint characteristics, such as length, long seam orientation (LSO), wall thickness (WT) and girth weld (GW) counts to ensure precision data alignment between ILI inspections. By leveraging the “fingerprint” like morphology of a pipeline system many improvements to data and records systems become possible including but not limited to:
• Random ILI Tool performance errors can be detected and compensated for.
• Repair history and other records become rapidly searchable.
• New statistically accurate descriptions are created by leveraging the sensitivities of various ILI technologies.
One area of material data improvement focused on within this paper relates to long seam type detection through ILI tools. Due to the differing threat susceptibility of various weld types, it is accordingly important to identify the long seam weld types for integrity management purposes. Construction records of older vintage lines do not always contain information down to the joint level; therefore, ILI tools may be leveraged to increase the accuracy of construction records down to this level. In this paper, the possibility of ILI tools, such as magnet flux leakage tools, ultrasonic crack tools, and ultrasonic metal loss tools, to distinguish different types of longitudinal seam welds is also discussed.