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.
Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps
Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition methods such as Self-Organizing Maps in Paradise.
Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes to use in a multi-attribute analysis using SOM. The process in Paradise reveals natural clustering by pattern recognition in the data helping define aspects like stratigraphy, seismic facies, DHI features and sweet spots for shale.