Automated Structure Detection and Analysis in Televiewer Images

Abstract

Borehole televiewer data is an important source of data on structural and stratigraphic discontinuities in both the mining and petroleum industries. Manually picking features in downhole image logs is a labour-intensive and hence expensive task and as such is a significant bottleneck in data processing. It is also a subjective process. We present a new algorithm and workflow for automatically detecting and analysing planar structures in downhole acoustic and optical televiewer images. First, an image complexity measure highlights areas most suitable for automated structure detection. Changes in the image complexity can be used to locate geological boundaries. Second, structures are automatically detected, with each structure having an associated confidence level; users can apply a threshold to the confidence values to adjust the quality and quantity of the detected structures based on the image quality and geological complexity. Third, structures that have been detected but that do not meet the structure confidence threshold can be interactively assessed and if necessary selected. We also provide tools for rapidly picking sets of equivalent structures and reducing structures to a set of representative picks.