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.
Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of Mexico oil/gas field. The SOM analyses using DHI characteristics and seismic attributes to reveal hydrocarbon contacts, amplify attenuation features and define ampliltude conformance in a Class 3 AVO.
Exploring shallow Yegua formation as an independent method to accurately identify anomalies and exposing direct hydrocarbon indicators using Self-Organizing Map (SOM) analysis to enhance conventional seismic interpretation to reveal anomalies.
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.