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Facies Classification

Integration of Facies Classification into the Interpretation Suite 

A unique way to gain a deeper understanding of data through facies analysis.

Seismic facies analysis provides a complementary and advanced method for seismic attribute analysis, especially when amplitude behavior is not the main driver for analyzing geological settings or reservoir condition changes related to lithology variations, fluid content changes or thickness variations. The comprehensive suite of classification approaches embedded in Emerson's SeisEarth™ software solution enables better delineation and detailed analysis of prospects. 

This approach can simply be based on analysis of the seismic signal, to provide qualitative information about an interval of interest.  For example, when used for reservoir characterization purposes, seismic facies classification can be applied in an exploration context, as it provides images that contribute to the interpretation of geological settings, augmenting information supplied by amplitude maps.

Of course, when we start drilling a prospect, direct measurements of the subsurface are made available to us in the form of well logs, and the ability to calibrate the facies models to well data is mandatory.  Emerson E&P Software offers two different methods for facies-to-well calibration:

  • By supervising facies volume creation with facies logs using a unique DNNA approach to seismic classification of both poststack and prestack seismic data 
  • By supervising waveform classification with 2D modeling (e.g. wedge modeling)

Facies Classification Benefits:

  • Streamlined workflow in the interpretation platform.
  • Faster, more accurate calibration between facies volumes and facies logs.
  • Increases geophysical meaning of facies results (“connecting the dots”).
  • Easy parameter testing and sharing.
  • A smaller learning curve thanks to the workflow approach.
  • Integration of QC steps into workflows promotes data validation within the data generation process.



Winner of Hart Energy’s Meritorious Awards for Engineering Innovation (MEAs) in Exploration/Geoscience

Click here to learn more about Rock Type classification.

The Rock Type Classification workflow uses a brand new algorithm to generate rock type volumes calibrated to facies logs using a probabilistic approach, to assess uncertainty in rock type distribution.  Users have the option to access the Rock Type Classification workflow directly within the interpretation platform.  The probabilistic approach results in less guesswork when quantifying uncertainty. Results are interactively displayed in a 2D and 3D visualization system for in-depth analysis and downstream usage, for instance in geobody-picking workflows and well planning.

Rock Type Classification Benefits:

  • Enables better risk management and well planning optimization
  • Brings new potential about seismic data reliability when predicting reservoir facies away from wells, especially when referring to prestack data, which carry more information with any type of seismic attributes.
  • Allows the faster creation of predictive analyses of the subsurface while still maintaining accuracy, thus helping to accelerate the decision-making process when determining the drilling location.


Waveform Classification uses the same proven neural network method as used in Stratimagic for classifying seismic wave shapes. The implementation of this methodology as a plug-in to SeisEarth provides access to quality control tools as well as advanced wave shape constraining from the 2D Wedge Modeling utility, to map the impact of changes in reservoir conditions on amplitude data.  For example, users may map thickness variations or fluid effects on seismic data by constraining seismic facies mapping with synthetic traces representing those effects. With such an embedded workflow, the geoscientist can perform the calibration of waveform classification to different scenarios, and play the “what if” game to evaluate prospects.  

In a 4D analysis context, waveform classification models help to compare the seismic response between two different seismic vintages. This provides an easy way to analyze how the the seismic response evolves through time, as a result of hydrocarbon production and/or fluid injection.


Map Classification uses the same proven neural network method as used in Stratimagic to perform facies classification of maps. The methodology, provided as a plug-in to SeisEarth, enables access to an interval attribute workflow chained with map classification, the output of which (several maps) can be used as input for map classification. 


Attribute clustering is an unsupervised classification method which extends Machine Learning beyond facies analysis to other uses, including: 

  • Structural delineation from multiple seismic attributes
  • AVO analysis from prestack data and AVO attributes
  • Outlier detection