Artificial Intelligence for Subsurface Characterization and Monitoring

Erscheinungsjahr 2025
ISBN: 978-0-443-23517-7
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization and monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some longstanding technical challenges. This book explores some of the most important topics in this discipline to explain the unique capability of deep learning in subsurface characterization for hydrocarbon exploration and production and for energy transition. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring. The text is organized into five parts. The first two parts explore deep learning for data enrichment and well log data, including information extraction from unstructured well reports as well as log data QC and processing. Next is a review of deep learning applied to seismic data and data integration, which also covers intelligent processing for clearer seismic images and rock property inversion and validation. The closing section looks at deep learning in time lapse scenarios, including sparse data reconstruction for reducing the cost of 4D seismic data, time-lapse seismic data repeatability enforcement, and direct property prediction from pre-migration seismic data.

Autoren/Hrsg.

Abubakar, Aria

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Geowissenschaften Geologie Wirtschaftsgeologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Ölförderung, Gasförderung

Inhaltsverzeichnis

Part I Deep learning for data enrichment

1. Rejuvenating legacy data by digitizing raster logs
2. Information extraction from unstructured well reports

Part II Deep learning applied to well log data

3. Well log data QC and processing
4. Automatic well marker picking
5. Automatic log interpretationPart III Deep learning applied to seismic data
6. Intelligent processing for clearer seismic images
7. Seismic interpretation with improved quality and efficiency

Part IV Deep learning for data integration

8. Automatic seismic-well tie
9. Rock property inversion and validation

Part V Deep learning in time lapse scenarios

10. Time-lapse seismic data repeatability enforcement
11. Direct property prediction from premigration seismic data

Über Autor(innen)

Abubakar, Aria
Aria Abubakar is a senior R&D manager and scientist/engineer with more than 20 years of academic and industry experience. Aria has a variety of assignments in research, engineering (hardware), and software organization. He is currently the Head of Data Science & Scientific Advisor for Digital Subsurface Solutions at SLB based in the United States. He received his MSc degree in electrical engineering and PhD degree in computational sciences from Delft University of Technology in Delft, the Netherlands. He was the 2020 SEG-AAPG Distinguish Lecturer and the 2014 SEG North America Honorary Lecturer. Aria is the recipient of 2022 Conrad Schlumberger Award of EAGE and 2022 Honorary Membership Award of SEG. He holds over 50 patents/patent applications and has published 5 books and book chapters, over 120 peer-reviewed scientific articles, over 250 peer-reviewed conference papers, and over 50 conference abstracts.

Über Autor(innen)

Abubakar, Aria
Aria Abubakar is a senior R&D manager and scientist/engineer with more than 20 years of academic and industry experience. Aria has a variety of assignments in research, engineering (hardware), and software organization. He is currently the Head of Data Science & Scientific Advisor for Digital Subsurface Solutions at SLB based in the United States. He received his MSc degree in electrical engineering and PhD degree in computational sciences from Delft University of Technology in Delft, the Netherlands. He was the 2020 SEG-AAPG Distinguish Lecturer and the 2014 SEG North America Honorary Lecturer. Aria is the recipient of 2022 Conrad Schlumberger Award of EAGE and 2022 Honorary Membership Award of SEG. He holds over 50 patents/patent applications and has published 5 books and book chapters, over 120 peer-reviewed scientific articles, over 250 peer-reviewed conference papers, and over 50 conference abstracts.