Azimuthal Borehole Image Inversion for Real-Time Dip Mapping
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Solution Overview
Problem
Existing drilling technologies face challenges in efficiently determining geological features such as dip angles and orientations of formation beddings during drilling operations, which are crucial for geosteering and navigating a pre-designed drilling path.
Innovation Solution
The use of sparse inversion-based methods to analyze azimuthal borehole images, employing sparse convolution and gradient hard thresholding pursuit to extract sinusoidal parameters, allowing for the determination of dip angles and orientations from resistivity or density measurements, thereby generating accurate geological models for real-time navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to determine geological features from borehole images, then measurement precision can be maintained, but productivity is reduced due to time-consuming manual analysis
Solution Approach 1:
The system performs automatic feature extraction and geological characteristic determination without requiring manual intervention. The computer executes algorithms that autonomously identify formation beddings, calculate dip angles, and generate geological models from borehole images, enabling the system to serve itself in the analysis process.
Solution Approach 2:
The patent replaces manual mechanical analysis with computational algorithms. Instead of human experts visually examining borehole images and measuring dip angles, the system uses computer-based image processing and mathematical algorithms to automatically extract geological features and determine formation characteristics.
2Reliability
If comprehensive borehole imaging is performed to ensure reliable geological data, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system extracts only the essential features needed for geological analysis from borehole images. Instead of processing all image data equally, the algorithms identify and extract key characteristics such as formation bedding patterns, dip angles, and structural features, discarding redundant information to reduce computational burden.
Solution Approach 2:
The analysis focuses on specific local regions of the borehole image where geological features are most prominent or relevant. The system applies different processing strategies to different zones, concentrating computational resources on areas with significant geological information while reducing analysis in less critical regions.
Data Source
AI summary
Systems and methods for interpreting one or more borehole features are provided herein. The method can include deploying an azimuthal borehole measurement tool into a borehole, obtaining at least one azimuthal borehole image, generating a synthetic image by sparse convolution of a weight function and a plurality of feature kernels, determining an optimal weight function that minimizes a difference between the synthetic image and the at least one azimuthal borehole image, and determining one or more geological characteristics of the borehole based on the optimal weight function and the feature functional representation.