Application of Big Data in Petroleum Streams

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A01=Jay Gohil
A01=Manan Shah
advanced petroleum informatics
Ai Algorithm
Apache Storm
artificial intelligence for energy sector
Author_Jay Gohil
Author_Manan Shah
Big Data
Big Data Analysis
Big Data Implementation
Big Data Platform
Big Data Solutions
Big Data Tools
Big Data's Integration
Category=GPH
Category=KNBP
Category=UYQ
Cloud Service
Data Envelopment Analysis
data science applications
Decline Curve Analysis
digital oilfield analytics
Downstream Segment
Drilling Operations
EOR
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_nobargain
eq_non-fiction
Future Scope
Machine Learning Models
Oil And Gas Industry
Petroleum Engineers
Petroleum Operations
predictive maintenance modelling
process optimisation algorithms
PVT
Real Time Stream
Real World Implementation
reservoir simulation techniques
SVM
Upstream Segment

Product details

  • ISBN 9781032028965
  • Weight: 560g
  • Dimensions: 174 x 246mm
  • Publication Date: 09 May 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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.

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