SPE Live: Generative AI Implementations on Spatial Data

This SPE Live introduces three prominent approaches to generative AI, followed by a presentation of a novel method utilizing two of these approaches to efficiently generate high-quality geomodels essential for understanding the probabilistic distributions of geological properties within a subsurface hydrocarbon play. By combining a hierarchical vector-quantized autoencoder with an autoregressive model, significant acceleration of both unconditional and conditional geomodel generation is achievable through this implementation of generative AI.

Speaker: Dr. Siddharth Misra, Associate Professor at Texas A&M University.

Moderated by Yusuf Ajibola Falola, PhD student, Department of Petroleum Engineering Texas A&M University.

Additional Resources:

• Data Science and Engineering Analytics Technical Section

From OnePetro:

• In-House Integrated Big Data Management Platform for Exploration and Production Operations Digitalization: From Data Gathering to Generative AI through Machine Learning Implementation Using Cost-Effective Open-Source Technologies – Experienced Mature Workflow
• Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment
• A Conceptual Model to Maximize Project Efficiency Through Automated Scheduling Using Generative AI Models