ML-based Curve Repair

When Danomics set out to incorporate machine-learning into our petrophysical workflow we had the following objectives: Increase the data available for use in petrophysical calculations Eliminate the need for users to tune correlation parameters on a zone-by-zone basis for things empirically-based correlations like the Gardener-relation Improve the quality of interpretations Read more…

Full Interpretation Video

This video shows a full interpretation from start to finish. It covers data loading, creating a new project, setting up zones, selecting a key well and the full petrophysical workflow. It’s 90 minutes long, but it should serve as a useful tutorial for anyone getting started.