Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Massively Parallel Distributed Computing and Agentic Framework for Subsurface Data Management and Analysis |
| Συγγραφείς: |
Débora Barretto, Juliana Fernandes, Vikram Sahukar, Sashi Gunturu, Vineet Patel, Atharva Gadad, Rodrigo Eiras |
| Πηγή: |
19th Congress of the Brazilian Geophysical Society - Proceedings. |
| Στοιχεία εκδότη: |
Brazilian Geophysical Society, 2025. |
| Έτος έκδοσης: |
2025 |
| Περιγραφή: |
The energy sector confronts a significant challenge in managing and extracting value from ever-expanding multidimensional datasets, with seismic and sensor data reaching petabyte scales. Legacy software applications lack the scalability and interoperability to handle this volume and variety, leading to disjointed workflows and the underutilization of valuable data. This work introduces a solution that leverages a massively parallel distributed computing environment on AWS, combined with Generative AI (GenAI), to enable scalable and intelligent energy data flow orchestration. The proposed architecture utilizes AWS EMR running on EKS to process petabyte-scale data and employs GenAI for automated metadata mapping, enrichment, and validation against the OSDU® data standards. This work presents a case study that was undertaken with a major North American operator demonstrating the solution's efficacy. |
| Τύπος εγγράφου: |
Article |
| DOI: |
10.22564/19cisbgf2025.463 |
| Αριθμός Καταχώρησης: |
edsair.doi...........bb603cb92f0a2ba213122be4e6be7c7a |
| Βάση Δεδομένων: |
OpenAIRE |