This paper presents the data analytics performed to ensure safe implementation of the Plausible Profiles (Psqr) corrosion assessment model, (presented in a companion paper at IPC 2020). These analytics were performed during the rigorous review of the model and its implementation by both internal (TC Energy) and external experts. This work addresses key questions posed during the review. As the validation of Psqr model was performed on a unique data set of metal-loss clusters that had ILI measurements, laser measurements (in-ditch measurements), and well monitored burst tests, it provided an unprecedented set of validation data that could represent many perspectives, such as model performance ( with all other uncertainties removed), in-ditch decision scenario, and ILI based decision scenario. Moreover, the morphologies of the 30 clusters tested was a good representation of large clusters of corrosion that have failed historically in the pipeline industry. In studying the post-ILI failures due to corrosion in the industry it was found that morphology played a significant role. Previous model validations were mostly performed on simple single anomalies or simple clusters with few features.

During the extensive review of Psqr model there was a set of recurring questions that emerged from pipeline integrity engineers, regulators, and consultants. These recurring questions and the ensuing data analytics used to address these questions are presented in this paper. The questions addressed in this paper are:

1\. Does the unity plot of ILI-based burst pressure against laser-based burst pressure effectively demonstrate safety? Or can in-ditch validation verify the safety of a model?

2\. Does simple or “conservative” model compensate for ILI error?

3\. Does less “conservative” model reduce safety?

4\. Do fewer digs lead to less safety?

 

The analysis of this unprecedented and comprehensive set of data led to great learning and revealed how safety can be provided optimally with good understanding of how ILI uncertainties, model errors, in-ditch uncertainties, and safety factors interact and play into integrity. It also revealed the role of common misunderstandings that are barriers to effective pipeline integrity assessment.  Overcoming these misunderstandings have helped in developing a more effective ILI based corrosion management program that will avoid more failures and reduce unnecessary integrity actions.


A Data Driven Validation of a Defect Assessment Model and its Safe Implementation

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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