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 multiple attributes to evaluate a 3D volume in offshore South America containing unexpected high pressure zone and the application of seismic attributes in a SOM to help define seismic facies and isolate the pressure zone.
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.
Top 5 class 3 direct hydrocarbon indicator characteristics, top five class 2 DHI characteristics, reasons for failure, implications for resource calculations in exploration and implications for reserve calculations.