Upscaling High-Resolution Core-Based Facies to Basin-Scale Stratigraphic Models

Explore a machine learning workflow for integrating high-resolution core-based facies data with wireline logs to create basin-scale stratigraphic models. Learn how to overcome challenges in characterizing subsurface geological reservoirs, particularly in mudrock systems with thin-bedded heterogeneity. Discover techniques for upscaling discrete core-based facies to the reservoir scale using a case study from the Third Bone Spring Sand and Wolfcamp A and B units in the Delaware Basin of West Texas. Examine the integration of categorical lithofacies core descriptions, X-ray fluorescence core scanning, and open-hole wireline logs as model input parameters. Gain insights into using core-based rock attribute data to describe high-resolution facies in training datasets and applying these classifications 'beyond the core' to predict rock facies across an entire basin using large volumes of open-hole triple combo wireline logs.

Upscaling high-resolution core-based facies to basin-scale stratigraphic models