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Crude oil and its derivate have many applications in almost all industries as O&G are the main resources that move the entire world. The oil and gas industry operate in demanding environments that pose significant challenges to equipment and infrastructure integrity. Facilities such as Gas Oil Separation Plant (GOSP) tanks, submerged areas, DGA columns, desulfurization units, and sour gas treatment facilities are subjected to high temperatures, corrosive substances, and harsh operating conditions.
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In industrial plants such as oil & gas and chemical plants, the plant piping is covered with insulative materials such as mineral wools and metal cladding for thermal insulation. The piping under insulation is subject to more severe corrosive environment than that exposed to the outdoor, due to rainwater entering through the cladding joints and condensation caused by temperature fluctuation. In addition, since the piping is covered with the insulation materials, it is impossible to identify the corrosion from the outside, increasing the risk of leakage accidents due to delays in corrosion mitigations.
MIC is a major problem in many industrial sectors, especially in the oil and gas industry. It is widely believed that almost 20% of all corrosion costs can be attributed to MIC. The shale gas and oil industry suffers from mostly MIC rather conventional abiotic CO2/H2S corrosion. Very severe MIC with fast failures are seen in field operations with very harsh operating conditions such as high salinity and nutrient-rich water, including treated municipal wastewater that promotes microbial growth. In some situations, titanium and plastic pipes are used to cope with MIC.
Convolutional deep neural networks are one of the main machine learning techniques applied to computer vision and object recognition tasks. Currently, they are very popular due to their proven effectiveness in solving image classification tasks and their significant theoretical and practical importance to the advancement of the deep learning field. Examples of successful image classification networks developed are AlexNet, VGG, and GoogLeNet.1,2,3
The electrical conductivity of the electrolyte is one of the key parameters in the electromechanics of corrosion. Highly conductive electrolytes will permit more current and increase corrosion rates. Conversely, resistive electrolytes will enable less current to flow until the necessary conditions for corrosion are no longer satisfied or slowed.
In recent years, unexpected failure caused by sulfidation corrosion have increased presumably because many refineries diversify the crude oils to process them. Crude oils contain corrosive species such as sulfides, nitrides, chlorides, organic acids and chemical additives. In these corrosive species, sulfides in the fluids cause sulfidation corrosion operating at temperature above approximately 260 °C1.
The concrete biological shields (CBSs) of light water reactors are affected by neutron and gamma irradiation at high radiation doses, resulting in the degradation of the concrete’s material properties. Several studies in the literature focused on evaluating both the expansion of aggregate-forming minerals and the resulting loss of mechanical properties. Modeling efforts have been carried out to predict theradiation-induced volumetric expansion (RIVE) and damage using different numerical methods such as the finite element method or fast-Fourier transform (FFT).
Drag Reducing Agents (DRA) usage in liquid petroleum pipelines has increased over the past few decades, as they improve the mechanical efficiency of flow systems, but their potential impact on different aspects affecting corrosion management has not been fully evaluated. For example: DRA may a) decrease mass transfer and velocity near-wall, reducing flow induced localized corrosion or erosion-corrosion; b) introduce changes in the oil/water interface, affecting water-in-oil stratification and water-oil phase inversion point; c) affect the function of corrosion inhibitors by adsorbing to surfaces or direct chemical interaction.
The potential effect on water accumulation was not included in the model developed for the Pipeline Research Council International, Inc (PRCI) or in other models that are typically used6 for the indirect inspection step of the Liquid Petroleum Internal Corrosion Direct Assessment methodology (LP-ICDA).
Assessing the corrosion degradation of aboveground tank bottom plates is a critical challenge for the industry. Internal inspections are a useful way of assessing the integrity of assets but might severely impact normal plant operation. In 2006, Chang et al. conducted a study on storage tank accidents and concluded that 74% of reported accidents occurred in petrochemical refineries, and 85% of them had caused fire and explosions.