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This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

Pipelines are the most efficient and safest means for the transportation of oil, gas, and refined petroleum products. Potentially severe consequences of pipeline failures make reliability and risk assessment an essential aspect of safe operation. However, due to limited access to industrial data, reliability and risk assessment studies often rely on experimental, synthetic, or unreliable data, which often raises questions on the proposed method’s credibility. The authors had the opportunity to access a comprehensive dataset from consecutive inline inspection (ILI) runs reporting more than seven years of degradation due to external corrosion of more than 200 km of a cross-country pipeline. This paper presents a step-by-step data processing approach and detailed statistical analysis of a cross-country pipeline’s ILI data. The paper presents stochastic models and defines the parameters required for modeling time-dependent structural integrity and risk assessment, i.e., corrosion-induced failure probability, burst pressure assessment, and containment loss. The accompanying dataset and proposed models for stochastic progress of external corrosion are hoped to serve as an essential source for pipeline risk and reliability studies.

Pipelines are the most vital energy-transportation mediums of today’s energy-intensive economies. To a level, pipeline integrity is tied to the continuous development and robustness of modern societies, where major failures may result in dire environmental, societal, and economic consequences. Therefore, pipeline safety and integrity are crucial for a sustainable future and responsible development. Pipeline integrity management has been a topic of interest for regulators, practitioners, and academicians alike. Over the past four decades, integrity management has evolved from prescriptive visual inspection and assessment to risk-based integrity management using real-time data. This paper aims to capture the evolution of risk-based methods in integrity management, focusing on the last two decades. The paper answers four primary questions: 1 What is integrity management, and how has it evolved? 2 How does the concept of risk fit in integrity management? 3 What are the methods used to assess and manage pipeline integrity? 4 How will integrity accommodate Industry 4.0?This article briefly defines the term integrity, how it has progressed and subsequently provides a structured review and analysis of the public domain literature. The paper focuses on risk-based methods adopted to different integrity management elements and applied at different stages of the pipeline’s life cycle. The paper concludes by evaluating the research progress, the gaps in knowledge, and their implication on adopting Industry 4.0.