Machine Learning and Flow Assurance in Oil and Gas Production

This book is useful for flow assurance engineers, students, and industries that want to be flow assurance authorities in the 21st-century oil and gas industry. It discusses the use of digital or artificial intelligence methods in flow assurance to achieve fast results without thorough training. Flow assurance covers all risks associated with maintaining the flow of oil and gas during any stage in the petroleum industry, and challenges lead to stoppage of production, plugs, damage to pipelines or production facilities, economic losses, and blowouts. Machine learning methods have gained attention as best practices for predicting flow assurance issues, and this book focuses on the use and abilities of various machine learning methods in flow assurance.

Format: Hardback
Length: 177 pages
Publication date: 12 March 2023
Publisher: Springer International Publishing AG

This comprehensive book is a valuable resource for flow assurance engineers, students, and professionals seeking to excel in the dynamic field of oil and gas industry flow assurance in the twenty-first century. The adoption of digital or artificial intelligence methods in flow assurance has surged in recent years, enabling rapid and effective results without extensive training. Flow assurance encompasses all risks associated with ensuring the uninterrupted flow of oil and gas throughout the various stages of the petroleum industry. It involves the proactive identification, mitigation, and prevention of hydrates, waxes, asphaltenes, scale, and corrosion, which can pose significant challenges to operations. These challenges often result in production stoppages, pipeline or production facility damage, economic losses, and, in severe cases, blowouts and loss of human lives. To address these complex issues, a combination of chemical and non-chemical techniques is commonly employed. However, it is widely recognized that the use of models to anticipate, limit, and/or prevent flow assurance problems is the preferred and most effective approach. The existing proposed flow assurance models for hydrates, waxes, asphaltenes, scale, and corrosion management face accuracy and precision challenges, as well as limitations imposed by various parametric assumptions. In recent times, machine learning methods have garnered significant attention as best practices for predicting flow assurance issues. These machine learning models encompass a range of conventional approaches, including artificial neural networks, support vector machines (SVMs), least square support vector machines (LSSVMs), random forests (RF), and hybrid models. The utilization of machine learning in flow assurance is on the rise.

To support the effective application of these methods, there is a growing need for comprehensive knowledge and guidelines on their methodologies and effectiveness. This book aims to fill this knowledge gap by providing a comprehensive overview of the latest developments, trends, and best practices in flow assurance, encompassing both chemical and non-chemical techniques. It delves into the principles and methodologies of flow assurance, covering topics such as hydrate formation, wax deposition, asphaltene inhibition, scale control, corrosion prevention, and flow assurance modeling. The book also discusses the use of machine learning in flow assurance, highlighting its advantages, limitations, and potential applications. It provides practical insights and case studies to illustrate the successful implementation of machine learning in flow assurance, showcasing its ability to improve efficiency, reduce costs, and enhance safety in oil and gas operations. By presenting a holistic approach to flow assurance, this book equips professionals with the necessary skills and knowledge to navigate the complexities of the twenty-first-century oil and gas industry. Whether you are a seasoned flow assurance engineer, a student pursuing a career in the field, or a industry professional seeking to expand your expertise, this book is an essential resource for achieving success in flow assurance.

Weight: 453g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031242304
Edition number: 1st ed. 2023