Machine Learning And Flow Assurance In Oil And Gas Production Bhajan Lal by Bhajan Lal, Cornelius Borecho Bavoh, Jai Krishna Sahith Sayani, (eds.) 9783031242304, 3031242300 instant download after payment.

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.…