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ILI Validation – Overview And Case Studies

ILI Validation – Overview And Case Studies
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The pipeline industry strives to keep pipelines running safely and reliably.  The process involves many steps, specialized groups, and decisions:1, 2, 3

1. The group that chooses the lines or segments to be inspected with in-line inspection (ILI).  This work entails choosing the ILI tools for the anticipated integrity threat.

2. The ILI tool runs to detect and identify defects.

3. Maintenance, repair, and excavation digs.   

4. The ILI validation.

5. Defect growth predictions and risk-based programs to establish maintenance and repair practices. 

Product Number: 51322-18144-SG
Author: Ana Benz
Publication Date: 2022
Industries: Pipelines, Tanks, and Underground Systems , Coatings
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The pipeline industry strives to keep pipelines running safely and reliably. The process involves many steps, including selecting the lines/segments to be inspected with in-line inspection (ILI); maintenance, repair, digs; ILI validation; growth predictions and risk-based programs to establish maintenance and repair practices. Most operators' programs start with in-line inspections, which generate large data sets and provide interpretations about the pipeline integrity without a validation process. Although ILI information is thorough, it is generated by non-destructive and/or indirect measurements. As with any non-destructive examination (NDE) technique, comparisons must be made to direct observations and measurements to ensure accuracy. This paper overviews validation of the ILI data through four case studies: determining whether the tool is functioning per its listed specification, discussing the applicability of validating crack tool ILI runs and presenting two examples of feature misidentification by the ILI tool. Together, these case studies demonstrate the importance of validating ILI data to ensure the integrity of the pipeline is adequately assessed. 

The pipeline industry strives to keep pipelines running safely and reliably. The process involves many steps, including selecting the lines/segments to be inspected with in-line inspection (ILI); maintenance, repair, digs; ILI validation; growth predictions and risk-based programs to establish maintenance and repair practices. Most operators' programs start with in-line inspections, which generate large data sets and provide interpretations about the pipeline integrity without a validation process. Although ILI information is thorough, it is generated by non-destructive and/or indirect measurements. As with any non-destructive examination (NDE) technique, comparisons must be made to direct observations and measurements to ensure accuracy. This paper overviews validation of the ILI data through four case studies: determining whether the tool is functioning per its listed specification, discussing the applicability of validating crack tool ILI runs and presenting two examples of feature misidentification by the ILI tool. Together, these case studies demonstrate the importance of validating ILI data to ensure the integrity of the pipeline is adequately assessed. 

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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.
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