Machine Learning and Flow Assurance in Oil
and Gas Production
Bhajan Lal · Cornelius Borecho Bavoh ·
Jai Krishna Sahith Sayani
Editors
Machine Learning and Flow
Assurance in Oil and Gas
Production
Editors
Bhajan Lal Cornelius Borecho Bavoh
Chemical Engineering Department Chemical Engineering Department
Universiti Teknologi Petronas Universiti Teknologi PETRONAS
Seri Iskandar, Perak, Malaysia Bandar Seri Iskandar, Malaysia
Jai Krishna Sahith Sayani
Department of Chemical and Bioprocess
Engineering
University College Dublin
Belfield, Ireland
ISBN 978-3-031-24230-4 ISBN 978-3-031-24231-1 (eBook)
https://doi.org/10.1007/978-3-031-24231-1
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Preface
The use of digital or artificial intelligence methods in flow assurance has increased
recently to effectively achieve fast results without any thorough training. 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. The occurrence of flow assurance challenges
mostly leads to stoppage of production or plugs, damage of 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 suitable practice. The existing proposed
flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion manage-
ment are challenged with accuracy and precision. They are not also limited by
several parametric assumptions. Recently, machine learning methods have gained
much attention as the best practice for predicting flow assurance issues. Examples
of these machine learning models include conventional approaches such as Artifi-
cial Neural Network, Support Vector Machine (SVM), Least Squares 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, we focused 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
v
vi Preface
machine learning in flow assurance. This book is useful to flow assurance engineers,
students, and industries who wish to be flow assurance authorities in the twenty-first-
century smart oil and gas industry.
Seri Iskandar, Malaysia Bhajan Lal
Bandar Seri Iskandar, Malaysia Cornelius Borecho Bavoh
Belfield, Ireland Jai Krishna Sahith Sayani
Contents
1 Machine Learning and Flow Assurance Issues . . . . . . . . . . . . . . . . . . . 1
Cornelius Borecho Bavoh and Bhajan Lal
2 Machine Learning in Oil and Gas Industry . . . . . . . . . . . . . . . . . . . . . . 7
Jai Krishna Sahith Sayani and Bhajan Lal
3 Multiphase Flow Systems and Potential of Machine Learning
Approaches in Cutting Transport and Liquid Loading
Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Muhammad Saad Khan, Abinash Barooah, Bhajan Lal,
and Mohammad Azizur Rahman
4 Machine Learning in Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Jai Krishna Sahith Sayani and Bhajan Lal
5 Machine Learning in Asphaltenes Mitigation . . . . . . . . . . . . . . . . . . . . 81
Ali Qasim and Bhajan Lal
6 Machine Learning for Scale Deposition in Oil and Gas Industry . . . 105
Sirisha Nallakukkala and Bhajan Lal
7 Machine Learning in CO2 Sequestration . . . . . . . . . . . . . . . . . . . . . . . . 119
Amirun Nissa Rehman and Bhajan Lal
8 Machine Learning in Wax Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Ihtisham Ul Haq and Bhajan Lal
9 Machine Learning Application in Gas Hydrates . . . . . . . . . . . . . . . . . . 155
Ali Qasim and Bhajan Lal
10 Machine Learning Application Guidelines in Flow Assurance . . . . . 175
Cornelius Borecho Bavoh and Bhajan Lal
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