Application of Big Data in Petroleum Streams

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.

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