In Situ Monitoring of Corrosion under Insulation Using Electrochemical and Mass Loss Measurements

Corrosion under insulation (CUI) refers to the external corrosion of piping and vessels when they are encapsulated in thermal insulation. To date, very limited information (especially electrochemical data) is available for these “difficult-to-test” CUI conditions. This study was aimed at developing a novel electrochemical sensing method for in situ CUI monitoring and analysis. Pt-coated Ti wires were used to assemble a three-electrode electrochemical cell over a pipe surface covered by thermal insulation. The CUI behavior of X70 carbon steel (CS) and 304 stainless steel (SS) under various operating conditions was investigated using mass loss, linear polarization resistance (LPR), and electrochemical impedance spectroscopy (EIS) measurements. It was found that both the consecutive wet and dry cycles and cyclic temperatures accelerated the progression of CUI. LPR and EIS measurements revealed that the accelerated CUI by thermal cycling was due to the reduced polarization resistance and deteriorated corrosion film. Enhanced pitting corrosion was observed on all tested samples after thermal cycling conditions, especially for CS samples. The proposed electrochemical technique demonstrated the ability to obtain comparable corrosion rates to conventional mass loss data. In addition to its potential for in situ CUI monitoring, this design could be further applied to rank alloys, coatings, and inhibitors under more complex exposure conditions.

Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI

Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.

Analysis of Temperature Variations, Types of Insulation and Coating on Corrosion Under Insulation on ASTM A53 Pipes

Corrosion Under Insulation (CUI) can be described as localized corrosion that forms as a result of the penetration of water or moisture through an insulating material. The pipe material used is of the ASTM A53 standard and the fluid used in seawater because almost all industries are located on the coast. This type of coating is carried out on the test pipe using Meiji Epoxy Filler. The test method is carried out by flowing seawater fluid in pipes with water temperature variations of 30°C, 50°C, and 70°C. This pipe varies the type of insulation by using glasswool and Rockwool (ASTM G 189-07). This insulation is conditioned in a wet state by giving 2 ml of seawater drops with a pH value of 4 per 6 hours. The test equipment is divided into 3 series according to temperature variations with 4 test specimens and 2 coating variations respectively. The test time was carried out for 336 hours to obtain the corrosion rate results using the ASTM G31-72 weight loss method. The results showed that the type of Glasswool insulation with specimens coated had the lowest corrosion rate value of 0.00483 mmpy at a temperature of 30°C when compared to the same type of treatment on Rockwool insulation of 0.00724 mmpy or an increase of 2.41 times. This study shows that the type of insulation, temperature variation, and coating greatly affect the rate of corrosion and the type of corrosion that occurs is uniform corrosion.

Corrosion and Remaining Life Assessment

This article illustrates the use of the fitness-for-service (FFS) code to assess the serviceability and remaining life of a corroded flare knockout drum from an oil refinery, two fractionator columns affected by corrosion under insulation in an organic sulfur environment, and an equalization tank with localized corrosion in the shell courses in a chemicals facility. In the first two cases, remaining life is assessed by determining the minimum thickness required to operate the corroded equipment. The first is based on a Level 2 FFS assessment, while the second involves a Level 3 assessment. The last case involves several FFS assessments to evaluate localized corrosion in which remaining life was assessed by determining the minimum required thickness using the concept of remaining strength factor for groove-like damage and evaluating crack-like flaws using the failure assessment diagram. Need for caution in predicting remaining life due to corrosion is also covered.

MACHINE-LEARNING-BASED EVALUATION OF CORROSION UNDER INSULATION IN FERROMAGNETIC STRUCTURES

Corrosion under insulation CUI is one of the challenging problems in pipelines used in the gas and oil industry as it is hidden and difficult to detect but can cause catastrophic accidents. Pulsed eddy current (PEC) techniques have been identified to be an effective non-destructive testing (NDT) method for both detecting and quantifying CUI. The PEC signal’s decay properties are generally used in the detection and quantification of CUI. Unfortunately, the well-known inhomogeneity of the pipe material’s properties and the presence of both cladding and insulation lead to signal variation that reduces the effectiveness of the measurement. Current PEC techniques typically use signal averaging in order to improve the signal-to-noise ratio (SNR), with the drawback of significantly-increasing inspection time. In this study, the use of Gaussian process regression (GPR) for predicting the thickness of mild carbon steel plates has been proposed and investigated with no signal averaging used. With mean absolute errors (MAE) of 0.21 mm, results show that the use of GPR provides more accurate predictions compared to the use of the decay coefficient, whose averaged MAE is 0.36 mm. This result suggests that the GPR-based method can potentially be used in PEC NDT applications that require fast scanning. ABSTRAK: Hakisan di bawah penebat CUI adalah salah satu masalah yang mencabar dalam saluran paip yang digunakan dalam industri gas dan minyak kerana tersembunyi dan sukar dikesan tetapi boleh menyebabkan bencana. Teknik Pulsed eddy current (PEC) telah dikenal pasti sebagai kaedah ujian bukan pemusnah yang berkesan (NDT) untuk mengesan dan mengukur CUI. Sifat kerosakan isyarat PEC umumnya digunakan dalam pengesanan dan pengukuran CUI. Malangnya, sifat tidak tepat yang terkenal dari sifat bahan paip dan kehadiran pelapisan dan penebat menyebabkan variasi isyarat yang mengurangkan keberkesanan pengukuran. Teknik PEC semasa biasanya menggunakan rata-rata isyarat untuk meningkatkan nisbah isyarat-ke-kebisingan (SNR), dengan kelemahan peningkatan masa pemeriksaan dengan ketara. Dalam kajian ini, penggunaan regresi proses Gauss (GPR) untuk meramalkan ketebalan plat keluli karbon ringan telah diusulkan dan diselidiki dan tidak ada rata-rata isyarat yang digunakan. Dengan ralat mutlak (MAE) 0,21 mm, hasil menunjukkan bahawa penggunaan GPR memberikan ramalan yang lebih tepat dibandingkan dengan penggunaan pekali peluruhan, yang rata-rata MAE adalah 0,36 mm. Hasil ini menunjukkan bahawa kaedah berasaskan GPR berpotensi digunakan dalam aplikasi PEC NDT yang memerlukan pengimbasan pantas.

Pitting and Corrosion Rates of Coated, Uncoated, and Insulated A333 Steel Pipelines in Marine Harsh Environment

Pipelines are one of the most economical and safe means of transporting useful materials, and their design life depends on protection mechanisms present. Marine environments increase corrosion rates due to moisture, and elements like chloride which increase localized pitting rates. Thirty-six A333 low temperature carbon steel pipelines were placed at Argentia, NL, an extremely corrosive environment (C5) near high tide mark. The experiment consisted of coated, uncoated, and insulated pipes. Exposed for a period of two years, corrosion rate, optical inspections, and pit depth were recorded at intervals. The highest average pit and maximum pit depth occurred in uncoated insulated pipes and coated uninsulated pipes. The highest average mass loss occurred in uncoated (insulated and uninsulated) pipes. The least mass loss and pit depths generally occurred in coated pipes (both insulated and uninsulated). Corrosion near the ends of the pipes were more significant than other locations. Final averaged corrosion rates for insulated coated and uncoated pipes, were 0.017 and 0.021mm/yr respectively. Corrosion rates for uninsulated coated and uncoated pipes, were 0.014mm/yr and 0.023mm/yr respectively. Maximum and mean pit depths for insulated coated and uncoated pipes were, 180/156 and 256/205 microns, respectively, while for uninsulated coated and uncoated were 210/177 and 182/148 microns, respectively. Some coated and uncoated insulated pipes had negligible pitting and corrosion. Results provide an increased understanding of corrosion rates, corrosion under insulation corrosion, and under coating, and pitting data for pipelines in service in marine harsh environments.

Using Industry Data to Develop Improved Methods to Manage Corrosion Under Insulation

Corrosion Under Insulation (CUI) continues to be an issue in many oil, gas and petrochemical installations. This paper builds on previous work to examine why the industry has struggled to come up with reliable and cost-effective solutions to the CUI problem. The limitations of different multidiscipline innovations are discussed together with positive examples of the latest promising industry projects and research, including risk management guidance, improved coatings, insulation system materials and design, non-destructive screening techniques and permanently embedded monitors. Key learnings from this review demonstrate the importance of better use of industry plant data to achieve improvements in managing CUI in all innovation disciplines.

Risk Based Management of Corrosion Under Insulation

Risk Based Management of Corrosion Under Insulation, DNVGL-RP-G109© Corrosion Under Insulation (CUI) is a major challenge for different process industries. Today it is managed in many ways, ranging from full removal of insulation to minimal maintenance including some inspection with insufficient non-destructive testing. These two extremes exhibit a lack of understanding and a lack of systematic approach in managing the CUI risk, globally. The paper will describe the results from a recent (2017-2019) CUI Joint Industry Project (JIP) where the oil and gas industry in the North Sea area has established a methodology for managing the CUI threat. In this context managing the CUI threat involves risk assessment, risk mitigation, risk update and experience transfer in a systematic manner. The methodology assesses four CUI barriers: material, coating, water wetting and design. DNV GL has made this methodology available for the industry in DNVGL-RP-G109 "Risk Based Management of Corrosion Under Insulation" Copyright © DNV AS. 2019 All rights reserved. (1) issued in December 2019. The Recommended Practice is issued alongside a cloud-based web application, the "CUI Manager" Copyright © DNV AS. 2020 All rights reserved. (2) that ease and supports the implementation of the work process described in the RP. This web application can be aligned with individual company specific requirements, as well as solely rely on the DNV GL RP methodology, or use a combination of the two.

Influence of robust drain openings and insulation stand-offs on corrosion under insulation behavior of carbon steel

CUI (corrosion under insulation) is among the key concerns for the integrity of process equipment and pipelines. Various measures to detect and fix the damages from CUI pose significant maintenance expenditures in hydrocarbons processing facilities. The key reason behind CUI is the limitation of thermal insulations to absorb the moisture and soak the underneath metal from wicking action. Other than CUI, trapped moisture in the soaked thermal insulations causes heat loss from process systems, thereby posing the risk of additional damage mechanisms and increased operating expenditures. This study addresses the impact of robust drain openings and insulation stand-offs on the CUI rate of carbon steel under four different testing conditions namely isothermal wet, isothermal wet-dry, cyclic wet, and cyclic wet-dry, respectively. Corroded specimens were further characterized using surface topography and scanning electron microscope. The impacts of temperature and moisture cycling on the corrosion attributes were also characterized using the linear polarization resistance method followed by an investigation of corrosion modes via optical microscopy. Insulation stand-offs in conjunction with robust drain opening resulted in the lowest corrosion rate. With insulation stand-offs and drain openings, the cyclic temperature conditions caused higher metal loss than that in isothermal conditions.