The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir characterization information than is available from traditional interpretation methods.
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.
Latest Technology for Seismic Interpretation: Direct detection & delineation of facies architecture in the Eagle Ford Group or How did the Eagle Ford GP get Made? A presentation by Patricia Santogrossi at the 2016 SEG Annual Convention.
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.