Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager

TL;DR

This paper introduces a novel depth matching method for oil-based mud microresistivity images using ShapeDTW, which improves alignment accuracy by preserving structural features during the process.

Contribution

The paper proposes a ShapeDTW-based depth matching approach that effectively aligns borehole images with complex textures and local variations, enhancing existing methods.

Findings

Achieves precise alignment of complex textured images

Handles depth shifts and local scaling effectively

Provides a flexible framework for feature extension

Abstract

In well logging operations using the oil-based mud (OBM) microresistivity imager, which employs an interleaved design with upper and lower pad sets, depth misalignment issues persist between the pad images even after velocity correction. This paper presents a depth matching method for borehole images based on the Shape Dynamic Time Warping (ShapeDTW) algorithm. The method extracts local shape features to construct a morphologically sensitive distance matrix, better preserving structural similarity between sequences during alignment. We implement this by employing a combined feature set of the one-dimensional Histogram of Oriented Gradients (HOG1D) and the original signal as the shape descriptor. Field test examples demonstrate that our method achieves precise alignment for images with complex textures, depth shifts, or local scaling. Furthermore, it provides a flexible framework for feature extension, allowing the integration of other descriptors tailored to specific geological features.