From Seismic Attributes to Geological Insight Faster with Emerge GeoCluster

Your seismic attribute volumes hold more geological information than a manual first pass can reveal, especially with limited well control. Emerge GeoCluster, a new workflow in the HampsonRussell 2026.3 release, brings machine learning clustering and classification directly into Emerge so you can see what geology is present, and where, before committing to costly reservoir characterization studies.
 
In this live session, Ruth Kurian will walk through the full Emerge GeoCluster workflow and show how to:

• Select the attributes that matter using familiar Emerge multi-linear regression

• Let the data reveal its own structure with unsupervised clustering with no prior geological assumptions required

• Map clusters to real rock types using lithology or facies logs at just a few tied wells

• QC results with confusion matrices

• Estimate uncertainty, deliver most-likely-facies and probability volumes

• Build an unbiased Low Frequency Model (LFM) driven by the data rather than by interpolation between wells, giving your inversion a stronger, more geologically grounded starting point 

Whether you’re screening prospects in a new area, extending interpretation beyond your wells or building a better foundation for inversion, you’ll see how Emerge GeoCluster helps your team reduce uncertainty earlier, focus costly characterization studies where they matter most and make faster, more confident decisions about which prospects to pursue.

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