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

Abstract

This paper presents data analytics that demonstrates the safe implementation of defect assessment models which use uncertain measurements of defect and material properties as inputs. Even though this validation is done for a corrosion assessment model implementation, it can be generalized for any defect assessment validation where the inputs have uncertainty (as they do in implementation). The questions arising from the validation of the Plausible Profiles (Psqr) model and related review led to a large amount of data analytics to demonstrate various aspects of safety in implementation. The data analytics demonstrates how the safety of model implementation can be verified using a well-designed set of data. The validation of Psqr model was conducted on a unique set of data consisting of metal-loss corrosion clusters with Inline Inspection (ILI) reported size, laser scan-measured dimension, and well monitored burst testing pressure. Therefore, this validation provided an unprecedented set of validation data that could represent many perspectives, such as model performance (with all uncertainties associated with other parameters removed), in-the-ditch decision scenario, and ILI-based decision scenario. Moreover, the morphologies of the 30 corrosion clusters tested is a good representation of large corrosion clusters that have failed historically in the pipeline industry. One of learnings from post-ILI failures due to corrosion in the industry is that corrosion morphology played a significant role. Previous model validations were mostly performed on simple single anomalies or simple clusters with few individual corrosion anomalies. It is important that a corrosion model is validated using real corrosion morphologies that are representative of in-service conditions. The analysis of this unprecedented and comprehensive set of data led to great learning and revealed how safety can be achieved optimally with good understanding of how uncertainties associated with ILI sizing error, material property, model error, 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.