Microorganisms are ubiquitously present in petroleum reservoirs and the facilities that produce them. Pipelines, vessels, and other equipment used in upstream oil and gas operations provide a vast and predominantly anoxic environment for microorganisms to thrive. The biggest technical challenge resulting from microbial activity in these engineered environments is the impact on materials integrity. Oilfield microorganisms can affect materials integrity profoundly through a multitude of elusive (bio)chemical mechanisms, collectively referred to as microbiologically influenced corrosion (MIC). MIC is estimated to account for 20 to 30% of all corrosion-related costs in the oil and gas industry. This book is intended as a comprehensive reference for integrity engineers, production chemists, oilfield microbiologists, and scientists working in the field of petroleum microbiology or corrosion. Exhaustively researched by leaders from both industry and academia, this book discusses the latest technological and scientific advances as well as relevant case studies to convey to readers an understanding of MIC and its effective management.

Helps and facilitates bacterial and MIC troubleshooting for asset owners. No theoretical suggestions or instructions, which are based on pure lab work or research-but were never tested in the field-are included.

Provides comprehensive coverage of corrosion inhibitors in the oil and gas industries Considering the high importance of corrosion inhibitor development for the oil and gas sectors, this book provides a thorough overview of the most recent advancements in this field. It systematically addresses corrosion inhibitors for various applications in the oil and gas value chain, as well as the fundamentals of corrosion inhibition and interference of inhibitors with co-additives. Corrosion Inhibitors in the Oil and Gas Industries is presented in three parts. The first part on Fundamentals and Approaches focuses on principles and processes in the oil and gas industry, the types of corrosion encountered and their control methods, environmental factors affecting inhibition, material selection strategies, and economic aspects of corrosion. The second part on Choice of Inhibitors examines corrosion inhibitors for acidizing processes, inhibitors for sweet and sour corrosion, inhibitors in refinery operations, high-temperature corrosion inhibitors, inhibitors for challenging corrosive environments, inhibitors for microbiologically influenced corrosion, polymeric inhibitors, vapor phase inhibitors, and smart controlled release inhibitor systems. The last part on Interaction with Co-additives looks at industrial co-additives and their interference with corrosion inhibitors such as antiscalants, hydrate inhibitors, and sulfide scavengers. -Presents a well-structured and systematic overview of the fundamentals and factors affecting corrosion -Acts as a handy reference tool for scientists and engineers working with corrosion inhibitors for the oil and gas industries -Collectively presents all the information available on the development and application of corrosion inhibitors for the oil and gas industries -Offers a unique and specific focus on the oil and gas industries Corrosion Inhibitors in the Oil and Gas Industries is an excellent resource for scientists in industry as well as in academia working in the field of corrosion protection for the oil and gas sectors, and will appeal to materials scientists, electrochemists, chemists, and chemical engineers.

A multi-disciplinary, multi-industry overview of microbiologically influenced corrosion, with strategies for diagnosis and control or prevention Microbiologically Influenced Corrosion helps engineers and scientists understand and combat the costly failures that occur due to microbiologically influenced corrosion (MIC). This book combines recent findings from diverse disciplines into one comprehensive reference. Complete with case histories from a variety of environments, it covers: Biofilm formation Causative organisms, relating bacteria and fungi to corrosion mechanisms for groups of metals Diagnosing and monitoring MIC Electrochemical techniques, with an overview of methods for detection of MIC The impact of alloying elements, including antimicrobial metals, and design features on MIC MIC of non-metallics Strategies for control or prevention of MIC, including engineering, chemical, and biological approaches This is a valuable, all-inclusive reference for corrosion scientists, engineers, and researchers, as well as designers, managers, and operators.

The advancement of methods and technologies in the oil and gas industries calls for new insight into the corrosion problems these industries face daily. With the application of more precise instruments and laboratory techniques as well as the development of new scientific paradigms, corrosion professionals are also witnessing a new era in the way d

Failure Analysis of Microbiologically Influenced Corrosion serves as a complete guide to corrosion failure analysis with an emphasis on the diagnosis of microbiologically influenced corrosion (MIC). By applying the principles of chemistry, microbiology, and metallurgy, readers will be able to reliably determine the mechanistic cause of corrosion damage and failures and select the appropriate methods for mitigating future corrosion incidents. FEATURES Provides background information on the forensic process, types of data or evidence needed to perform the analysis, industrial case studies, details on the MIC failure analysis process, and protocols for field and lab use Presents up-to-date advances in molecular technologies and their application to corrosion failure investigations Offers specific guidelines for conducting MIC failure analyses and case studies to illustrate their application Examines state-of-the-art information on MIC analytical tools and methods With authors with expertise in microbiology, corrosion, materials, and failure investigation, this book provides tools for engineers, scientists, and technologists to successfully combat MIC issues.

Microbiologically influenced corrosion (MIC) is a difficult degradation mechanism to diagnose in pipeline systems due to the complex interaction between biotic (i.e., microbial) and abiotic (e.g., fluid chemistry, pipe/vessel metallurgy/corrosion, and operating conditions) factors. This complexity often makes it difficult to accurately assess pipeline failures due to MIC. However, even with available data, failure investigators often face a number of challenges in diagnosing MIC such as how to properly integrate the available datasets, questions regarding data accuracy (e.g., confidence in the sampling and/or analysis method used) and lack of available information from operators (e.g., missing data). As a result, practical MIC failure assessments are most often performed by experts or specialists with significant knowledge and working experience in this topic. Based on these issues, the objectives of this thesis are three-fold: 1) to quantify the actual prevalence of MIC related pipeline failures in Alberta's oil and gas sector, 2) to perform a gap analysis of failure investigation methods used to assess these pipeline failures, and 3) to develop a novel expert system based on machine learning to assist both experts and non-experts in assessing potential MIC related pipeline failures. The first part of this study highlights a review and analysis of MIC related pipeline incidents in the province of Alberta, Canada over a three-year period (2017-2019). This review was used to quantify the occurrence of MIC failures relative to other corrosion mechanisms, and to conduct a gap analysis of MIC failure investigation techniques being used relative to the current state of the art. Over this three-year period, MIC was found to be responsible for 13.6% and 4.8% of all pipeline leak incidents due to internal and external corrosion, respectively (either as the main failure mechanism or as a contributing factor). Most of these failures were seen to occur in small diameter upstream pipelines (with less than or equal to 220.3 mm outside diameter) carrying mainly multiphase fluids (oil-water emulsions) or produced water. In terms of the failure investigation methods currently being used, it was noted that there was some inconsistency among reports and a number of important gaps were identified. Various assessments lacked microbiological test data, in particular, tests which specifically identify microbial functional groups or speciation, which is critical to confirm observed corrosion mechanisms. Furthermore, a number of these assessments identified MIC primarily on the basis of corrosion morphology, which has been shown to be an incorrect assumption and approach without additional evidence. Details related to sampling methods were also lacking in these assessments, which created some uncertainty as to the quality of data obtained. Overall, most assessments did a reasonable job in characterizing and including chemical (solids, fluids, and corrosion products), metallurgical/ corrosion, and operating data. However, the integration of these various layers of evidence (i.e., connecting corrosion to microbiological activity, and eliminating possible abiotic corrosion mechanisms) was missing in many reports. The second part of 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 machine learning (artificial neural network) and involves the participation of 15 MIC subject matter experts (SMEs). Each expert evaluated a number of model case studies representative of both MIC and non-MIC related upstream pipeline failures. The model accounts for variations in microbiological testing methods, microbiological sample types, degradation morphology, among others, and also incorporates cases with select missing datasets which is commonly found in actual failure assessments. The output classifications comprised elements of both potential for MIC and confidence in the data available. The results were contrasted for 5- and 3-output classification models (5OC and 3OC, respectively). The 5OC model had an overall accuracy of 62.0% while the simpler 3OC model had a better accuracy of 74.8%. This modelling exercise has demonstrated that knowledge from subject matter experts can be captured in a reasonably effective model to screen for possible MIC failures. It is hoped that this study contributes to a better understanding of the prevalence of MIC in the oil and gas sector, and highlights the key areas necessary to improve the diagnosis of MIC failures in the future.