Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning technologies is helping solve problems.
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.