Zoltan Sylvester, Ph.D.
Bureau of Economic Geology
Jackson School of Geosciences
The University of Texas at Austin
While automated stratigraphic correlation techniques are widely used in workflows for interpreting seismic reflection data, geophysical well logs and other time series data are still largely correlated manually. This is a time consuming and painstaking process, despite the fact that datasets with a dense well coverage are prime targets for automation using machine learning approaches. In this talk, I first want to revisit and visualize the basic ideas about how time is recorded - or not recorded - in sedimentary deposits and then show how chronostratigraphic diagrams can be used as key elements of an automated workflow for correlating geophysical well logs. We have written a software package called ‘ChronoLog’ to apply this workflow to datasets with relatively simple stratigraphy; in such settings, the results tend to match or outperform manual interpretations. Further work will focus on improving correlation results when major unconformities or onlap surfaces are present.