Paradise: Predictive Analytics and
Machine Learning for Seismic Interpretation

 

A recent article by AOGR underscores the need for predictive analytics to efficiently search for the diffraction energy and separate it from the higher (specular) energy reflection event.  Machine learning provides an approach to this challenge by enabling automatic classification and separation of wavefield patterns associated with different subsurface shale features (faults, fractures and continuous events) and even different styles of noise (e.g., ambient noise, acquisition footprint, etc.). Referencing the broad potential of by machine learning methods in general, Dr. Tom Smith, President of Geophysical Insights, offers this vision: 

“Going forward, we should be able to query our seismic data for information with learning machines, just as effortlessly and with as much reliability as we query the web for the nearest gas station.” 

The core of the Paradise workbench is machine learning technology, built specifically for seismic interpretation.  With the above vision as the guiding principal, Paradise enables interpreters to generate and analyze multiple seismic attributes simultaneously. Through Principal Component Analysis (PCA), interpreters can identify the attributes which contribute the most to the data volumes.  Using those attributes, unsupervised neural networks learn, then classify the data based on a defined topology, or specific number of neurons, in the Self-Organizing Map (SOM) process. Using the results, interpreters can: 

 

  • Identify thin beds below traditional seismic resolution
  • Discriminate DHIs in conventional resources
  • Locate fracture trends and sweet spots in shale plays
  • Highlight changes in pore pressure related facies
  • Reveal geologic and stratigraphic features
  • Visualize thin beds and facies below seismic resolution

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Identifying DHIs using SOM
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