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Workflows Using Machine Learning Boost Interpretation Confidence

Posted by Lorena Guerra on Oct 25, 2018 11:39:47 AM RSS Share Post

It is virtually impossible for the human mind to integrate and extract, in a timely manner, the huge amounts of subsurface data now available to interpreters.  The newest version of SeisEarth (Paradigm 18) uses several state-of-the-art Machine Learning-based technologies to extract unprecedented amounts of information from vast and heterogeneous data sources for use in geological classifications, rock property predictions, and anomaly detections. These technologies are offered in the form of automated and easy-to-use guided workflows embedded in the integrated interpretation platform.

  • Waveform Classification Workflow - allows interpreters to easily perform seismic trace-based classification to create facies maps while interpreting data and performing reservoir characterization and modeling.
  • Rock Type Classification Workflow - A supervised classification algorithm to predict facies distribution and probability of occurrence away from well control, using the Democratic Neural Network Association™ (DNNA) method.
  • Attribute Clustering Workflow - A new unsupervised classification method used to create classification volumes from poststack and/or prestack seismic data. 

For a more detailed description, click here.

Tags: machine learning in oil and gas, SeisEarth, Paradigm, Oil and Gas Software, Energy, Emerson E&P Software, Waveform Classification