Application of Big Data in Petroleum Streams
1st Edition
Published on 8. May 2022
182 pages
978-1-000-58002-0 (ISBN)
Description
The book aims to provide comprehensive knowledge and information pertaining to application or implementation of big data in the petroleum industry and its operations (such as exploration, production, refining and finance).
The book covers intricate aspects of big data such as 6Vs, benefits, applications, implementation, research work and real-world implementation pertaining to each petroleum-associated operation in a concise manner that aids the reader to apprehend the overview of big data's role in the industry.
The book resonates with readers who wish to understand the intricate details of working with big data (along with data science, machine learning and artificial intelligence) in general and how it affects and impacts an entire industry. As the book builds various concepts of big data from scratch to industry level, readers who wish to gain big data-associated knowledge of industry level in simple language from the very fundamentals would find this a wonderful read.
The book covers intricate aspects of big data such as 6Vs, benefits, applications, implementation, research work and real-world implementation pertaining to each petroleum-associated operation in a concise manner that aids the reader to apprehend the overview of big data's role in the industry.
The book resonates with readers who wish to understand the intricate details of working with big data (along with data science, machine learning and artificial intelligence) in general and how it affects and impacts an entire industry. As the book builds various concepts of big data from scratch to industry level, readers who wish to gain big data-associated knowledge of industry level in simple language from the very fundamentals would find this a wonderful read.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Adult education
Product notice
Reflowable
Illustrations
25 Tables, black and white; 54 Line drawings, black and white; 54 Illustrations, black and white
ISBN-13
978-1-000-58002-0 (9781000580020)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jay Gohil | Manan Shah
Application of Big Data in Petroleum Streams
Book
05/2024
1st Edition
CRC Press
€72.40
Shipment within 10-20 days
Jay Gohil | Manan Shah
Application of Big Data in Petroleum Streams
Book
05/2022
1st Edition
CRC Press
€132.80
Shipment within 10-20 days
Persons
Mr. Jay Gohil is pursuing Bachelor of Technology in Information and Communication Technology at Pandit Deendayal Energy University. He has authored three research papers, two conference papers, two book chapters and a book (this) during his academic study. His research interests include Big Data, Data Science, Machine Learning, Deep Learning, Data Mining and Artificial Intelligence, and he has communicated research work in esteemed journals in these areas. He has been a research intern at ISRO (Indian Space Research Organization, Ahmedabad, India) and Ryerson University (Toronto, Canada). He is also a Google DSC Lead, Microsoft Learn Student Ambassador, Intel Student Ambassador for IoT, IBM Z Ambassador, AWS Community Builder and deeplearning.ai Event Ambassador.
Dr. Manan Shah has a B.E. in Chemical Engineering from LD College of Engineering and an M.Tech. in Petroleum Engineering from School of Petroleum Technology, PDPU. He has completed his Ph.D. in the area of exploration and exploitation of Geothermal Energy in the state of Gujarat. He is currently Assistant Professor in the Department of Chemical Engineering, School of Technology (SOT), PDPU, and Research Scientist in Centre of Excellence for Geothermal energy (CEGE). One of his areas of research is power generation from low enthalpy geothermal reservoirs using Organic Rankine Cycle. He was also involved in the designing of a Geothermal Space Heating and Cooling system at Dholera and doing research on hybrid setup in the renewable energy sector. Dr. Shah has received the Young Scientist Award from the Science and Engineering Research Board (SERB). He has published several articles in reputed international journals in the areas of renewable energy, petroleum engineering, water quality and chemical engineering. He serves as an active reviewer for several Springer and Elsevier international journals.
Dr. Manan Shah has a B.E. in Chemical Engineering from LD College of Engineering and an M.Tech. in Petroleum Engineering from School of Petroleum Technology, PDPU. He has completed his Ph.D. in the area of exploration and exploitation of Geothermal Energy in the state of Gujarat. He is currently Assistant Professor in the Department of Chemical Engineering, School of Technology (SOT), PDPU, and Research Scientist in Centre of Excellence for Geothermal energy (CEGE). One of his areas of research is power generation from low enthalpy geothermal reservoirs using Organic Rankine Cycle. He was also involved in the designing of a Geothermal Space Heating and Cooling system at Dholera and doing research on hybrid setup in the renewable energy sector. Dr. Shah has received the Young Scientist Award from the Science and Engineering Research Board (SERB). He has published several articles in reputed international journals in the areas of renewable energy, petroleum engineering, water quality and chemical engineering. He serves as an active reviewer for several Springer and Elsevier international journals.
Content
1 Introduction; 2 Petroleum Operations; 3 Big Data's 6Vs; 4 Benefits of Big Data; 5 Applications of Big Data; 6 Implementation of Big Data; 7 Big Data Platforms; 8 AI Algorithms; 9 Research on Big Data; 10 Real-World Implementation of Big Data; 11 Traits of Companies with Superior Big Data Implementation; 12 Challenges of Big Data; 13 Future Scope of Big Data; 14 Conclusion
