Artificial Intelligence for Subsurface Characterization and Monitoring
Elsevier - Health Sciences Division (Verlag)
978-0-443-23517-7 (ISBN)
- Noch nicht erschienen (ca. Januar 2025)
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Senior R&D manager and scientist/engineer with 20+ years academic & industry experiences. Aria has variety assignments in research, engineering (hardware), and software organization. He is currently the Head of Data Science & Scientific Advisor for Digital Subsurface Solutions at Schlumberger based in the USA. He received M.Sc. degree in electrical engineering and the Ph.D. 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 book & book chapters, over 100 peer-reviewed scientific articles, over 225 peer-reviewed conference papers, and over 50 conference abstracts.
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: correction, outlier detection, and reconstruction
4. Automatic well marker picking
5. Automatic log interpretation
Part 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. Sparse data reconstruction for reducing the cost of 4D seismic data
11. Time-lapse seismic data repeatability enforcement
12. Direct property prediction from pre-migration seismic data
Erscheint lt. Verlag | 1.1.2025 |
---|---|
Verlagsort | Philadelphia |
Sprache | englisch |
Maße | 152 x 229 mm |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik ► Bergbau | |
Technik ► Elektrotechnik / Energietechnik | |
ISBN-10 | 0-443-23517-1 / 0443235171 |
ISBN-13 | 978-0-443-23517-7 / 9780443235177 |
Zustand | Neuware |
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