Automated Segmentation of Resistivity Image Logs Using Wavelet Transform

Abstract

We describe a wavelet-transform-based method for automated segmentation of resistivity image logs that takes into account the apparent dip in the data and addresses the problem of discriminating lithofacies boundaries from noise and intrafacies variations. Our method can be applied to borehole measurements in general, but might have an advantage when applied to resistivity image logs as it addresses explicitly the large variability in facies segments recorded with a high-resolution multiple-sensor tool. We have developed an algorithm based on this method that might outperform other existing segmentation methods in the cases of low to moderate dip. We made a detailed comparison of the segmentation from our method with the one done by a geologist to delineate different lithofacies blocks in a well drilled in a deepwater depositional environment. Our results show considerable success rates in reproducing the geologically defined lithofacies boundaries, and the generality of our procedure suggests it could also be applied to other depositional environments.

Exact Conditioning of Gaussian Fields on Wavelet Coefficients

“…Milne and Lark (2009) described a method to generalize the use of wavelet to irregularly spaced data. Hruka et al (2009) performed automatic segmentation of well resistivity logs on wavelet coefficients. used wavelet decomposition for dimension reduction as a prior step to classification of patterns in multipoint simulation.…”