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

2025-12-01

AI Summary

Depth misalignment between upper and lower pad images in oil-based mud (OBM) microresistivity imaging logging degrades structural interpretability and quantitative formation evaluation. Method: This paper proposes a ShapeDTW-based depth-matching method that constructs a composite feature descriptor by fusing one-dimensional histogram of oriented gradients (HOG1D) with raw resistivity signals. Leveraging local shape features, it builds a morphology-sensitive distance matrix to robustly handle complex geological textures, nonlinear depth shifts, and local scaling—without relying on velocity-based correction. Contribution/Results: Compared with conventional velocity correction, the method significantly improves image alignment accuracy. Validation on multiple field datasets demonstrates its robustness under challenging conditions—including high noise, strong formation heterogeneity, and tool vibration—yielding enhanced image continuity and structural interpretability. This establishes a new paradigm for high-precision formation evaluation in OBM environments.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Addresses depth misalignment in OBM microresistivity imager pad images
• Uses ShapeDTW with HOG1D and signal features for alignment
• Enables precise matching for complex textures and geological features

Innovation

• ShapeDTW algorithm for depth matching
• HOG1D and signal features for shape description
• Flexible framework for feature integration