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Preventing Corrosion under Insulation in Piping - Trends after Evaluating Digital Data from Thousands of Piping Circuits

Picture for Preventing Corrosion under Insulation in Piping - Trends after Evaluating Digital Data from Thousands of Piping Circuits
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Extensive guidelines have been published for selecting where to search for corrosion under insulation (CUI). The guidelines are based on CUI failures and near misses. Piping CUI inspection programs collect the data outlined as relevant in specific company practices.

Product Number: 51323-18871-SG
Author: Eddie Sanchez, Michael Townsend, Michael Nichols, Katherine Jonsson, Eric Sjerve, Ana Benz
Publication Date: 2023
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$20.00
$20.00

Insulation provides an environment where corrosion can progress and remain undetected unless vigilantly monitored. Various approaches and inspection techniques are used to monitor the hidden losses. Current inspection methods often result in digital images – images that have improved corrosion under insulation (CUI) insights. Nevertheless, optimizing a CUI search remains challenging. Today’s digital world gives (almost) standardized inspection data more amenable to scrutiny than in the past. This paper examines piping CUI trends for thousands of piping circuits - after outlining steps followed during CUI surveys. The trends identified through data analytics are summarized through graphs evaluating multiple variables. Specifically, different insulation types and thicknesses, pipe diameters, and operating temperatures, among others, are compared. Finally, a predictive model to aid in selecting where to perform detailed inspections is outlined.

Insulation provides an environment where corrosion can progress and remain undetected unless vigilantly monitored. Various approaches and inspection techniques are used to monitor the hidden losses. Current inspection methods often result in digital images – images that have improved corrosion under insulation (CUI) insights. Nevertheless, optimizing a CUI search remains challenging. Today’s digital world gives (almost) standardized inspection data more amenable to scrutiny than in the past. This paper examines piping CUI trends for thousands of piping circuits - after outlining steps followed during CUI surveys. The trends identified through data analytics are summarized through graphs evaluating multiple variables. Specifically, different insulation types and thicknesses, pipe diameters, and operating temperatures, among others, are compared. Finally, a predictive model to aid in selecting where to perform detailed inspections is outlined.

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Corrosion under Insulation (CUI) and External Corrosion continue to be a major issue for Petrochemical facilities.  Refineries have been investing in a CUI and External Corrosion inspection program. This paper details the methodology  for addressing this damage and lessons learned throughout the implementation.
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A Prediction and Inspection Technology for Detecting the Corrosion Under Insulation

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Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets for which the oil and gas industry is not immune. Over the last few decades, both downstream and upstream industry segments have recognized the magnitude of CUI and challenges faced by the industry in its ability to handle CUI risk-based assessment, predictive detection and inspection of CUI. It is a concern that is hidden, invisible to inspectors and prompted mainly by moisture ingress between the insulation and the metallic pipe surface. The industry faces significant issues in the inspection of insulated assets, not only of pipes, but also tanks and vessels in terms of detection accuracy and precision. Currently, there is no reliable NDT detection tool that can predict the CUI spots in a safe and fast manner. In this study, a cyber physical-based approach is being presented to identify susceptible locations of CUI through a collection of infrared data overtime. The experimental results and data analysis demonstrates the feasibility of utilizing machine-learning techniques coupled with thermography to predict areas of concern. This is through a simplified clustering and classification model utilizing the Convolutional Neural Networks (CNN). This is a unique and innovative inspection technique in tackling complex challenges within the oil and gas industry, utilizing trending technologies such as big data analytics and artificial intelligence.
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