Using machine learning to analyze 5 instantaneous attributes helped reveal patterns across 5 instantaneous attributes and unique Eagle Ford facies.
Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2
Results of a Self-Organizing Map (SOM) of many instantaneous attributes to reveal different types of facies and shale that apply machine learning to improve resolution and reveal facies.
Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4
Concurrent analysis of multiple attributes through machine learning to spectral decomposition sub-bands and other geology that apply attributes for stratigraphic and structural resolution.
A case study of 10 square mile Eagle Ford Shale Trend utilizing machine learning in Paradise to apply inversion and conventional attributes. Applying multiple attributes to emphasize sweet spots in unconventional resources by understanding reservoir geology, geochemistry, geomechanics, faults and fractures.