The concept of in-line inspection (ILI) tool validation could be succinctly summarized as follows: identify dig locations based on ILI reported features, measure these features in the ditch, compare in-ditch results with ILI predictions, log the results for future ILI assessments at specified intervals, and then proceed to the next pipeline segment. However, the complexities involved in ILI tool validation make it interesting. Each pipeline and ILI run is unique, presenting its own set of challenges that must be addressed. 

This paper will discuss a case in which a pipeline underwent repeat axial magnetic flux leakage (MFL) ILI runs, which reported thousands of defects of varying geometries and depths. The focus will be on the efforts that may be performed to validate this tool run and to ensure the pipeline’s safety for continued operation. 

Understanding the levels of validation as outlined in API 1163 is essential in whether the tool run can be deemed validated. When validation digs are performed to assess tool performance, particularly when there is a wide array of defects reported, it is important to account for the types of defects that may be found and to have appropriate technology available to capture these defects. There are many facets that can be explored with more data, and handling that appropriately can be a challenge. The results can give a wealth of insight into the tool’s performance and the condition of the line. This paper will provide recommendations on what factors an operator should consider when obtaining a large data set of correlated features from validation digs. Understanding ILI tool performance and the true condition of the pipeline is critical to maintaining the integrity of pipeline assets.