Buch, Englisch, 744 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 449 g
Theory, Case Studies, and Applications
Buch, Englisch, 744 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 449 g
ISBN: 978-1-041-02007-3
Verlag: CRC Press
This comprehensive resource bridges the knowledge gap in applying modern artificial intelligence and machine learning methods to the petroleum sector. It explores how AI, ML, and data analytics enhance efficiency, safety, and productivity in operations across various segments of the oil and gas value chain, including exploration, drilling, production, reservoir management, and renewables integration.
• Covers theoretical and practical aspects of AI/ML applications, from foundational concepts to advanced techniques.
• Features examples and A-to-Z practical workflows, empowering readers to apply what they learn directly to real-world challenges.
• Includes 500 international case studies, highlighting real-world successes, challenges, and lessons learned from a global perspective.
• Offers insights into emerging technologies like Industry 4.0, digital twins, smart fields, and IoT applications, as well as more traditional areas such as drilling optimization and enhanced recovery operations.
• Provides readers with downloadable Python codes for several chapters.
With the growing importance of advanced data-driven approaches, this book provides value to both technical professionals looking for hands-on solutions and academics seeking a well-structured textbook for advanced studies.
Zielgruppe
Postgraduate and Professional Reference
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Ölförderung, Gasförderung
- Naturwissenschaften Physik Mechanik Energie
- Geowissenschaften Geologie Wirtschaftsgeologie
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
Weitere Infos & Material
0. Front Matter. 1. Theory and Background on Artificial Intelligences and Machine Learning Methods. 2. AI Applications in Reservoir Characterization, Geology, and Geophysics. 3. AI Applications in Drilling Engineering. 4. AI Applications in Reservoir Engineering Management. 5. AI Applications in Well Completion, Production, Stimulation. 6. AL Applications in Enhanced Recovery. 7. AI Applications in Facilities, Pipelines, Metering. 8. AI Applications in Unconventionals. 9. AI Applications in Fourth Industry Revolution. 10. AI Applications in Renewable Resources. 11. Future Directions in AI Applications in the Energy Industry.