This book is useful to flow assurance engineers, students, and
industries who wish to be flow assurance authorities in the
twenty-first-century oil and gas industry. The use of digital or
artificial intelligence methods in flow assurance has increased
recently to achieve fast results without any thorough training
effectively. Generally, flow assurance covers all risks associated
with maintaining the flow of oil and gas during any stage in the
petroleum industry. Flow assurance in the oil and
gas industry covers the anticipation, limitation, and/or
prevention of hydrates, wax, asphaltenes, scale, and corrosion
during operation. Flow assurance challenges mostly lead to stoppage
of production or plugs, damage to pipelines or production
facilities, economic losses, and in severe cases blowouts and loss
of human lives. A combination of several chemical and non-chemical
techniques is mostly used to prevent flow assurance issues in the
industry. However, the use of models to anticipate, limit,
and/or prevent flow assurance problems is recommended as the best
and most suitable practice. The existing proposed flow
assurance models on hydrates, wax, asphaltenes, scale, and
corrosion management are challenged with accuracy and precision.
They are not also limited by several parametric assumptions.
Recently, machine learning methods have gained much attention
as best practices for predicting flow assurance issues. Examples of
these machine learning models include conventional approaches such
as artificial neural network, support vector machine (SVM), least
square support vector machine (LSSVM), random forest (RF), and
hybrid models. The use of machine learning in flow assurance
is growing, and thus, relevant knowledge and guidelines on their
application methods and effectiveness are needed for academic,
industrial, and research purposes. In this book, the authors focus
on the use and abilities of various machine learning methods in
flow assurance. Initially, basic definitions and use of machine
learning in flow assurance are discussed in a broader scope within
the oil and gas industry. The rest of the chapters discuss the use
of machine learning in various flow assurance areas such as
hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of
machine learning in practical field applications is discussed to
understand the practical use of machine learning in flow assurance.
General
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