A glossary defining essential machine learning terms within the seismic interpretation and geoscience community from Principal Component Analysis (PCA) to Self-Organizing Maps (SOM) and everything in between.
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.
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.
The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this webinar, Rocky Roden looks at seismic multi-attribute analysis using Self-Organizing Maps (SOMs), a machine learning approach that distills information from numerous attributes to provide an accurate assessment of DHI characteristics.
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.
This international webinar describes how multi-attribute seismic analysis is applied using the Paradise software to visualize thin beds and facies below classical seismic tuning thickness. The material is presented by Mr. Rocky Roden, an industry thought leader and Senior Consulting Geophysicist for Geophysical Insights.