Corrosion under insulation (CUI) is defined as any form of external corrosion that occurs on the underlying metal beneath insulated equipment, due to water ingress through the insulation layer. This type of corrosion is frequently observed in oil and gas production, where insulated piping is prevalent, and has historically remained a predominant materials integrity issue. The prediction and direct visualisation of CUI are challenging tasks because of the coverage of the insulation layer(s) and any external jacketing or cladding. Several factors, including the local/ambient environment, system design, and the piping installation process, can influence how CUI initiates and propagates. In this review, CUI background, CUI monitoring, and CUI mitigation strategies are discussed.

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A critical challenge facing the integrity of many assets throughout the oil and gas industry is directly related to corrosion under insulation (CUI). Unfortunately, the lack of adequate inspection technologies adds to this well-known industrial challenge. Presented in this paper is an inspection tool enhanced using Artificial Intelligence (AI) that can provide field inspection engineers with a facility heat map of insulated asset integrity allowing inspection prioritization. The approach used in this research, and presented here, was to enhance the output of already known and field approved thermographic technologies using a purpose built AI based on Machine Learning (ML). By examining the progression of thermal images, captured over time (<20 minutes), 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. Additional benefits to this technique include enhanced safety through remote inspection and additional cost savings from monitoring assets online. To develop and verify the CUI technology results from in-house laboratory tests followed by field validation outcomes will be presented. Laboratory trials were carried out using a series of insulated field assets with different levels of degradation and structural integrity set up to mimic the thermal behavior of in-process assets. This initial feasibility study allowed the definition of key parameters required to build an effective ML model. Following in-house trials a series of field tests and visual verification was performed on both hot and cold insulated assets to gather a sufficient amount of datasets to train the predictive algorithm. To enhance this learning process, synthetic data was created based on real field asset configurations and operating parameters. Finally, during the technology validation phase, again on field assets, the AI technique coupled with a commercial field approved thermographic camera returned a predictive accuracy in the range of 85 – 90%. The work presented in this paper provides a solution for the current lack of technologies to monitor the presence of CUI by enabling and enhancing the output from already known and field approved technologies, such as thermography, using AI. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the complexity of asset preparation (scaffolding) and downtime.

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Biocides are oilfield chemicals that are widely used to control bacterial activity throughout the oil industry. A feasibility study has been explored to develop detection techniques for biocide batch treatments, preferably on-line and in real-time, for their potential use in seawater flooding system. Several methods to measure key components of the biocide formulation were investigated and reported in previous study [1]. The enzymatic activity of an immobilized acetylcholine esterase (AChE) on the column material was successfully inhibited by some model compounds, but not by the actual biocides commonly used in Saudi Aramco seawater flooding system. In this paper, an alternative assay for biocide detection in the Saudi Aramco seawater flooding system was investigated for its applicability for the development of on-line biocide sensor. The assay was based on the detection of aldehyde functionality in the biocide mixture through measurement of a fluorescent derivative formed in the reaction of aldehyde groups and dimedone in the presence of ammonium acetate. The reaction of aldehyde groups with dimedone was demonstrated in seawater matrix, and the formed fluorescent product was successfully measured. The results showed that the dimedone-based assay was very sensitive, and relatively straightforward to perform. The ruggedness test also indicated that the assay is sensitive to minor changes of various specific conditions of the method. It is concluded that the dimedone assay is suitable for further development of a real-time biocide monitoring system to detect the presence of biocide slugs in seawater flooding system. The development of an automated on-line biocide sensor based on dimedone assay is underway.