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The model was evaluated and effects of various parameters on corrosion rates are described. Corrosion rates obtained from the model are compared with actual field and lab testing data as a basis to quantify accuracy and efficacy.
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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.
The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO(1) journal released in 2020.1 It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI(2) the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions.2
The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO1 journal released in 20201. It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI2 the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions2.
Sulfur and acidic impurities in crude oils pose serious hot oil corrosion problems in crude distillation units (CDU) and associated vacuum distillation units (VDU), especially with the increase in processing of lowquality, opportunity crudes.1-4 In the range of 200-400ËšC, reactive sulfur compounds cause sulfidation corrosion of ferritic carbon and chrome steels in CDU, VDU, and front ends of downstream units operating at hot oil temperatures.5-7 Over the same temperature range, naturally occurring carboxylic acids in crudes can be so aggressive that higher alloy, austenitic stainless steels containing >2.5% Mo are required for processing high acid oils.8-11 Although sulfidation and acid corrosion occur over the same temperature range, they differ in two significant ways. Sulfidation forms an iron sulfide solid that is semiresistant to further corrosion and relatively insensitive to flow velocity. Acids form oil soluble organic salts that can be washed away especially in areas of high turbulence.12-14
A top-of-line corrosion (TLC) model integrated into a CO2/H2S corrosion prediction model. The TLC model determines the top of the line corrosion rate of carbon steel based on water chemistry and film-wise condensation rate. The effect of various glycols, such as Monoethylene Glycol, Diethylene glycol and Triethylene Glycol, are included.
In Corrosion/2021, the authors introduced a molecular mechanistic model that quantifies and predicts SNAPS corrosion rates. During Corrosion/2022, we presented the mechanistic corrosion prediction framework describing the molecular basis of the model’s reactions, kinetics, and mass transport of ROSC to vessel walls. In this molecular model, sulfidation corrosion is calculated for direct heterolytic reaction of ROSC with solid surfaces.
Asociación Nacional de Ingenieros de Corrosión. (NACE). Evaluación confirmatoria directo de corrosión externa (ECCDA) para tuberías ferrosas enterradas.
NACE external corrosion confirmatory direct assessment (ECCDA) process for buried onshore ferrous piping systems. The process is also predictive.