Muutke küpsiste eelistusi

Application of Big Data in Petroleum Streams [Kõva köide]

(Pandit Deendayal Petroleum Uni, India), (Pandit Deendayal Petroleum University, India)
  • Formaat: Hardback, 168 pages, kõrgus x laius: 246x174 mm, kaal: 560 g, 25 Tables, black and white; 54 Line drawings, black and white; 54 Illustrations, black and white
  • Ilmumisaeg: 09-May-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032028963
  • ISBN-13: 9781032028965
Teised raamatud teemal:
Application of Big Data in Petroleum StreamsSuurem pilt
  • Formaat: Hardback, 168 pages, kõrgus x laius: 246x174 mm, kaal: 560 g, 25 Tables, black and white; 54 Line drawings, black and white; 54 Illustrations, black and white
  • Ilmumisaeg: 09-May-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032028963
  • ISBN-13: 9781032028965
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032028965.html
Märksõnad:
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 datas 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.
Preface xi
About the Authors xiii
1 Introduction
1(36)
1.1 Oil and Gas Industry
1(7)
1.1.1 Influence
1(3)
1.1.2 Challenges
4(1)
1.1.3 Future scope
4(2)
1.1.4 Industry knowledge
6(2)
1.2 Petroleum Upstream
8(5)
1.2.1 Exploration
9(1)
1.2.2 Drilling
9(1)
1.2.3 Production
10(1)
1.2.4 Segment finances
10(1)
1.2.5 Segment knowledge
11(2)
7.5 Petroleum Midstream
13(4)
1.3.1 Processing
14(1)
1.3.2 Transportation
15(1)
1.3.3 Storage
16(1)
1.3.4 Segment knowledge
16(1)
1.4 Petroleum Downstream
17(5)
1.4.1 Refining and processing
17(1)
1.4.2 Supply and trade
18(1)
1.4.3 Marketing and retail
18(1)
1.4.4 Segment finances
19(1)
1.4.5 Segment knowledge
20(2)
7.5 Big Data
22(15)
1.5.1 Big data tools
26(1)
1.5.2 Big data work flow
27(1)
1.5.3 Big data applications
28(1)
1.5.4 Big data in petroleum streams
29(3)
References
32(5)
2 Petroleum Operations
37(30)
2.1 Geoscience
37(2)
2.2 Exploration and Drilling
39(2)
2.3 Reservoir Studies
41(5)
2.4 Production and Transportation
46(4)
2.5 Petroleum Refinery
50(2)
2.6 Natural Gas
52(4)
2.7 Health and Safety
56(3)
2.8 Finance and Financial Markets
59(8)
References
63(4)
3 Big Data's 6Vs
67(10)
3.1 Introduction
67(2)
3.2 Big Data's 6Vs in Geoscience
69(1)
3.3 Big Data's 6Vs in Exploration & Drilling
69(1)
3.4 Big Data's 6Vs in Reservoir Studies
70(1)
3.5 Big Data's 6Vs in Production and Transportation
71(1)
3.6 Big Data's 6Vs in Petroleum Refinery
72(1)
3.7 Big Data's 6Vs in Petroleum Natural Gas
73(1)
3.8 Big Data's 6Vs in Petroleum Health and Safety
74(1)
3.9 Big Data's 6Vs in Petroleum Finance and Financial Markets
75(1)
3.10 Conclusion
76(1)
References
76(1)
4 Benefits of Big Data
77(8)
4.1 Introduction
77(1)
4.2 Industry-Wide Benefits
77(2)
4.3 Benefits of Big Data in Geoscience
79(1)
4.4 Benefits of Big Data in Exploration and Drilling
80(1)
4.5 Benefits of Big Data in Reservoir Studies
81(1)
4.6 Benefits of Big Data in Production and Transportation
81(1)
4.7 Benefits of Big Data in Petroleum Refinery
82(1)
4.8 Benefits of Big Data in Natural Gas
82(1)
4.9 Benefits of Big Data in Health and Safety
83(1)
4.10 Benefits of Big Data in Finance and Financial Markets
83(1)
4.11 Conclusion
84(1)
References
84(1)
5 Applications of Big Data
85(12)
5.1 Introduction
85(1)
5.2 Industry-Wide Applications
85(3)
5.3 Applications of Big Data in Geoscience
88(1)
5.4 Applications of Big Data in Exploration and Drilling
89(1)
5.5 Applications of Big Data in Reservoir Studies
89(2)
5.6 Applications of Big Data in Production and Transportation
91(1)
5.7 Applications of Big Data in Petroleum Refinery
92(1)
5.8 Applications of Big Data in Natural Gas
92(1)
5.9 Applications of Big Data in Health and Safety
92(1)
5.10 Applications of Big Data in Finance and Financial Markets
93(1)
5.11 Conclusion
94(3)
References
94(3)
6 Implementation of Big Data
97(4)
6.1 Introduction
97(1)
6.2 Implementation Process
97(2)
6.3 Operation-Specific Aspects of Big Data
99(1)
6.4 Conclusion
100(1)
References
100(1)
7 Big Data Platforms
101(6)
7.1 Introduction
101(1)
7.2 Platforms
101(1)
7.3 Benefits
102(2)
7.4 Other Aspects
104(1)
7.5 Roles
104(1)
7.6 Conclusion
105(2)
References
105(2)
8 Al Algorithms
107(4)
8.1 Introduction
107(1)
8.2 Major AI Algorithms
107(1)
8.3 Operation-Specific Aspects
108(2)
8.4 Conclusion
110(1)
References
110(1)
9 Research on Big Data
111(18)
9.1 Introduction
111(1)
9.2 Industry-Wide Research
111(2)
9.3 Research of Big Data in Geoscience
113(1)
9.4 Research of Big Data in Exploration and Drilling
114(2)
9.5 Research of Big Data in Reservoir Studies
116(1)
9.6 Research of Big Data in Production and Transportation
117(2)
9.7 Research of Big Data in Petroleum Refinery
119(1)
9.8 Research of Big Data in Natural Gas
119(1)
9.9 Research of Big Data in Health and Safety
120(2)
9.10 Research of Big Data in Finance and Financial Markets
122(2)
9.11 Conclusion
124(5)
References
124(5)
10 Real-World Implementation of Big Data
129(12)
10.1 Introduction
129(1)
10.2 Industry-Wide Real-World Implementation
129(2)
10.3 Real-World Implementation of Big Data in Geoscience
131(1)
10.4 Real-World Implementation of Big Data in Exploration and Drilling
131(1)
10.5 Real-World Implementation of Big Data in Reservoir Studies
132(1)
10.6 Real-World Implementation of Big Data in Production and Transportation
133(1)
10.7 Real-World Implementation of Big Data in Petroleum Refinery
133(1)
10.8 Real-World Implementation of Big Data in Natural Gas
134(1)
10.9 Real-World Implementation of Big Data in Health and Safety
135(1)
10.10 Real-World Implementation of Big Data in Finance and Financial Markets
136(1)
10.11 Conclusion
137(4)
References
138(3)
11 Traits of Companies with Superior Big Data Implementation
141(4)
11.1 Introduction
141(1)
11.2 Major Traits of Companies with Superior Big Data Implementation
141(2)
11.3 Conclusion
143(2)
12 Challenges of Big Data
145(6)
12.1 Introduction
145(1)
12.2 Major Challenges
145(3)
12.3 Conclusion
148(3)
References
149(2)
13 Future Scope of Big Data
151(4)
13.1 Introduction
151(1)
13.2 Major Aspects of Future Scope
151(2)
13.3 Conclusion
153(2)
References
154(1)
14 Conclusion
155(10)
14.1 Introduction
155(1)
14.2 Petroleum Operations
155(2)
14.3 6Vs of Big Data
157(1)
14.4 Benefits
157(1)
14.5 Applications
157(1)
14.6 Implementation
157(2)
14.7 Big Data Platforms
159(1)
14.8 A1 Algorithms
160(1)
14.9 Research
160(1)
14.10 Real-World Implementation
160(2)
14.11 Superior Implementation Traits
162(1)
14.12 Challenges
162(2)
14.13 Future Scope
164(1)
14.14 End
164(1)
References 165(2)
Index 167
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.