To develop a holistic understanding of corrosion mechanisms in upstream oil and gas pipelines mechanical properties of the corrosion product layers as well as corrosion mechanisms need to be studied for better prediction of general and localized corrosion. Various ongoing research has focused on the topic of sour corrosion mechanisms while minimal attention has been paid to ascertaining the mechanical properties of the iron sulfide layers developed in these environments. The effects of fluid flow (i.e. erosion/corrosion wall shear stress) as well as the impact of different operations (i.e. wellbore cleaning wireline tools) on the internal pipeline wall may lead to a partial removal of corrosion product layers. This is an important topic since the mechanical damage of protective iron sulfide layers may lead to localized corrosion. To investigate the magnitude of stress required to damage iron sulfide layers up to the point of exposing the substrate well-defined iron sulfide layers were developed in a 4-liter glass cell and the mechanical properties of the layers such as hardness and adhesive strength were investigated using a mechanical tester. To develop the iron sulfide layer UNSG10180 carbon steel specimens were exposed to a 1 wt.% NaCl solution at pH of 6.0 well purged with a 10 mol.% H2S/N2 mixture. Fes layers were developed at two solution temperatures 30⁰C and 80⁰C and the hardness and interfacial shear strength of the layers formed after 1 day and 3 days were investigated. The morphological characteristics of the FeS layers under investigation were examined by conducting an SEM and cross-sectional analysis. XRD analysis confirmed mackinawite as the phase of the iron sulfide layer. While the interfacial shear strength of this FeS layer was found to be 5 magnitudes higher than the maximum flow related shear stress the integrity may be compromised if these layers are subjected to other mechanical impacts that may occur during production.

Within the last decade researchers have gained a significant understanding of the role of biofilms in the onset of microbial-induced corrosion in anthropogenic water systems. Biofilms are the preferred habitat for microorganisms and in this environment they are protected from harsh chemical treatments shear stress and predators. Within the biofilm matrix microorganisms thrive in conditions responsible for promoting a corrosive environment. The types of bacteria responsible for corrosion vary from system to system. In this study we used a novel biomonitoring system installed in 28 unique industrial water systems across the United States. These systems provided not only a diverse geographical biofilm sample set but also allowed the evaluation in the seasonal variation within the biofilm samples collected from the same system. Metagenomic sequence analysis was utilized to provide a new understanding of the variations in bacterial populations existing in biofilms. Data revealed from the analysis will provide insight on the phylogenetically and metabolically distinct species present in the biofilms. This will aid in the formation of new anti-biofilm strategies to help minimize microbial-induced corrosion.

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.