A review on pipeline integrity management utilizing in-line inspection data

Abstract

Abstract Pipelines are widely used in transporting large quantities of oil and gas products over long distances due to their safety, efficiency and low cost. Integrity is essential for reliable pipeline operations, for preventing expensive downtime and failures resulting in leaking or spilling oil or gas content to the environment. Pipeline integrity management is a program that manages methods, tools and activities for assessing the health conditions of pipelines and scheduling inspection and maintenance activities to reduce the risks and costs. A pipeline integrity management program mainly consists of three major steps: defect detection and identification, defect growth prediction, and risk-based management. In-line inspections (ILI) are performed periodically using smart pigging tools to detect pipeline defects such as corrosion and cracks. Significant advances are needed to accurately evaluate defects based on ILI data, predict defect growth and optimize integrity activities to prevent pipeline failures, and pipeline integrity management has drawn extensive and growing research interests. This paper provides a comprehensive review on pipeline integrity management based on ILI data. Signal processing methods for defect evaluation for different types of ILI tools are presented. Physics-based models and data-driven methods for predicting defect growth for pipelines with different categories of defects are discussed. And models and methods for risk-based integrity management are reviewed in this paper. Current research challenges and possible future research trends in pipeline integrity management are also discussed.