Abstract
In September 2021, the API released the third edition of the 1163 Standard “In-line Inspection Systems Qualification”. This edition brought many improvements over previous versions, including more detail in Section 8 “System Results Validation”, which defines the methodologies used to validate ILI run tolerances. The standard describes three levels of validation, with ‘Level 3’ requiring the operator calculate ILI tool measurement performance with real-world data measured in validation spools and excavation sites. Real-world, inspection data sets have some characteristics that make them difficult to use to accurately estimate measurement performance, one of which is ‘truncation’, that is data with a lower- or upper-bound threshold above which no data is reported. For example, most UTCD ILI tools have a lower truncation level, such as 1 mm for crack height, which represents a signal threshold below which measurements are either not reliable, or not reported. Although small features below the reporting threshold exist on the pipeline, they are not normally reported by the ILI tool.
This paper describes a model to estimate ILI tool performance using API 1163 Level 3 methods when the data set has a lower-truncation threshold. The model is tested with simulation data to show how it responds over a wide range of feature population characteristics, and then applied to two real field data sets. Comparisons are made between the truncation algorithm and the standard non-truncated version of the algorithm, to show where the new algorithm performs best and is most useful to implement pipeline integrity mitigations. The model used in this study is consistent with the example documented in API 1163 - Appendix C, the Bayesian inference method. The results of the model produce measurement performance specifications that can be used as inputs in a pipeline risk or reliability analysis. The influence of truncated data sets is common in the field of inspection and NDE (including thickness measurements), as it reflects the reality that there are features below reporting threshold. The steps required to format the results for use, and achieve more accurate measurement performance results (e.g., unity charts), are described in this paper.
