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While the oil and gas industry has witnessed increased applications of molecular microbiological methods (MMMs) for diagnosing and managing microbiologically influenced corrosion (MIC) in the past decade, the process for establishing clear links between microbiological conditions and corrosion mechanisms is still emerging. Different MMMs provide various types of information about microbial diversity, abundance, activity and function, all of which are quite different from the culture-based results that are familiar to oil and gas industry corrosion professionals. In addition, a multidisciplinary process for establishing the significance of molecular microbiological data in regard to corrosion threat identification, mitigation and monitoring has yet to be clearly established. As a result, the benefits of employing MMMs for MIC management are not yet being fully realized or appreciated. Regardless of advances in technology, the microbiological insights being afforded by MMMs will not be embraced by many oil and gas asset operators until their significance relative to corrosion management and asset integrity are made more transparent. The need for an initiative to link corrosion, microbiological technologies and disciplinary experts together to reach a common understanding is discussed here. [ABSTRACT FROM AUTHOR]

Microbiologically influenced corrosion (MIC) is the terminology applied where the actions of microorganisms influence the corrosion process. In literature, terms such as microbial corrosion, biocorrosion, microbially influenced/induced corrosion, and biodegradation are often applied. MIC research in the oil and gas industry has seen a revolution over the past decade, with the introduction of molecular microbiological methods: (MMM) as well as new industry standards and procedures of sampling biofilm and corrosion products from the process system. This review aims to capture the most important trends the oil and gas industry has seen regarding MIC research over the past decade. The paper starts out with an overview of where in the process stream MIC occurs − from the oil reservoir to the consumer. Both biotic and abiotic corrosion mechanisms are explained in the context of managing MIC using a structured corrosion management (CM) approach. The corrosion management approach employs the elements of a management system to ensure that essential corrosion control activities are carried out in an effective, sustainable, well-planned and properly executed manner. The 3-phase corrosion management approach covering of both biotic and abiotic internal corrosion mechanisms consists of 1) corrosion assessment, 2) corrosion mitigation and 3) corrosion monitoring. Each of the three phases are described in detail with links to recent field cases, methods, industry standards and sampling protocols. In order to manage the corrosion threat, operators commonly use models to support decision making. The models use qualitative, semi-quantitative or quantitative measures to help assess the rate of degradation caused by MIC. The paper reviews four existing models for MIC Threat Assessment and describe a new model that links the threat of MIC in the oil processing system located on an offshore platform with a Risk Based Inspection (RBI) approach. A recent field case highlights and explains the conflicting historic results obtained through serial dilution of culture media using the most probable number (MPN) method as compared to data obtained from corrosion monitoring and the quantitative polymerase chain reaction (qPCR) method. Results from qPCR application in the field case have changed the way MIC is monitored on the oil production facility in the North Sea. A number of high quality resources have been published as technical conference papers, books, educational videos and peer-reviewed scientific papers, and thus we end the review with an updated list of state-of-the-art resources for anyone desiring to become more familiar with the topic of MIC in the upstream oil and gas sector. [ABSTRACT FROM AUTHOR]

• Expert system to aid microbiologically influenced corrosion failure assessments. • The model is based on machine learning Artificial Neural Networks (ANNs). • Fifteen experts evaluated 65 case studies related to internal pipeline failures. • Two ANN models were developed: one including confidence level and the other not. • This expert system is a potential tool for non-experts to screen pipeline failures. This study highlights the modeling of an expert system for the classification of internal microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil and gas industry. The model is based on artificial neural networks (ANNs) and involves the participation of 15 MIC experts. Each expert evaluated a number of model case studies ranging from MIC- to non-MIC-driven upstream pipeline failures. The model accounts for variations in microbiological testing methods, microbiological sample types, and degradation morphology, among other variables. It also accounts for missing datasets, which is commonly the case in actual failure assessments. The outcome is an expert system model whose outputs are classes (classification ANN) which comprises MIC potential and data confidence. The performances of two approaches are contrasted in this study. One classifies the output in 5 classes, a 5-output classification (5OC) model; the other in 3 classes, a 3-output classification (3OC) model. The 5OC model had an accuracy of 62.0% while the simpler 3OC model had a better accuracy of 74.8%. This modelling exercise has demonstrated that knowledge from experts can be captured in a reasonably effective model to screen for possible MIC failures. It is hoped that this study not only may contribute to a better understanding of the prevalence of MIC in the oil and gas sector, but also may highlight the key areas necessary to improve the diagnosis of MIC failures in the future. [ABSTRACT FROM AUTHOR]