Abstract
In-Line Inspection (ILI) technology is considered one of the safest and most efficient and reliable inspection method to inspect hydrocarbon pipelines. The retrieved data are usually validated and verified upon successful completion of the inspection. This paper is intended to introduce a new approach to validate the ILI run based on a statistical analysis comparing the new ILI run with a previous ILI run of the same pipeline by leveraging a root mean square (RMS) model to quantify the similarity between the datasets. API-1163 and Canadian Energy Pipeline Association (CEPA) offer consistent criteria as a validation methodology for a new ILI run. Also, this paper will demonstrate a new scoring criterion for accepting Magnetic Flux Leakage (MFL) runs with partial data loss as number of MFL runs experience unexpected data loss, which might affect the minimum reporting threshold of the tool. The approach will help pipeline operators to identify the criticality of the missed data via a detailed comparison with the previous MFL run for the same pipeline and detailed analysis of the behavior of the tool during the run. The scoring criteria is aligned with the Pipeline Operators Forum (POF) requirements for data loss. Multiple case studies extracted from actual data will be presented throughout the paper.
