Buch, Englisch, 300 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g
Including Geosciences, Reservoir Engineering, and Production Engineering with Python
Buch, Englisch, 300 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g
ISBN: 978-1-4842-6093-7
Verlag: Apress
Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry.
What You Will Learn
- Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry
- Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used
- Study interesting industry problems that are good candidates for being solved by machine and deep learning
- Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry
Who This Book Is For
Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Geowissenschaften Geologie Wirtschaftsgeologie
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Ölförderung, Gasförderung
Weitere Infos & Material
Chapter 1: Towards Oil and Gas 4.0.- Chapter 2: Python Programming Primer.- Chapter 3: Overview of Machine and Deep Learning Concepts.- Chapter 4: Geophysics and Seismic Data Processing.- Chapter 5: Geomodeling.- Chapter 6: Reservoir Engineering.- Chapter 7: Production Engineering.- Chapter 8: Opportunities, Challenges, and Expected Future Trends.