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This is a Quantitative Study study.

October 10, 2025

Application of a Statistical Approach When Data Is Limited in Pipeline Integrity Management

Key Points

Effective application of limited data improves pipeline management safety, relying on statistical methods.
The Agresti-Coull interval method estimates critical population boundaries based on minimal samples for confidence.
Three main applications include validating ILI results and assessing toughness levels for pipelines with limited data.
Identifying insufficient sample sizes can prompt the need for broader data collection efforts from similar pipelines.

Abstract

Abstract Data from measurements on pipelines is valuable for Pipeline Integrity Management, or PIM. However, acquiring this data post-construction may be expensive from the costs associated with excavation, potential interruption of normal operations due to sample cutout, and subsequent destructive testing in a lab. Therefore, effective application of the limited amount of collected data to the rest of the pipeline, with an appraisable confidence level, is critical for efficiently managing the safety of pipelines. In API 1163 (2021 Ed.), the Agresti-Coull interval method, or ACIM, is used for the level 2 assessment of in-line inspection, or ILI, results evaluation. The ACIM estimates the boundaries of a variable in the entire population associated with a certain confidence level based on the value of the variable determined from the available limited data or samples. This paper first reviews the ACIM and then describes three applications in PIM. The first one is ILI results validation, as API 1163 proposed. After an ILI inspection, a selection of detected features would be excavated and evaluated. The ACIM is used to derive the confidence level of the performance of the tool based on the comparison between the as-found and ILI-called values. The second application is on Traceable-Verifiable-Complete, or TVC, records. For vintage pipelines with missing records, field measurements can be taken of pipe properties from selected sample joints. The ACIM can be used to determine the number of such samples needed to satisfy the confidence at a specified level. The last application is for selecting a conservative toughness level for crack assessment in a pipeline from which the toughness data is limited. The ACIM is used to find a conservative toughness level for the pipeline based on a given confidence level and the number of tested samples. The ACIM can also indicate when the number of samples is too small and should be expanded to a larger sample pool, including from similar pipe from other pipelines. The limitations of the ACIM approach are also discussed based on these application cases.

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