Using machine learning to classify a 100-square-mile seismic volume in the Niobrara, geoscientists were able to interpret thin beds below seismic tuning and identify anomalies at resolutions not possible with traditional interpretation.
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.
Using machine learning to analyze 5 instantaneous attributes helped reveal patterns across 5 instantaneous attributes and unique Eagle Ford 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.