Automated Log Quality Monitoring Using Sensor Cross-Correlation
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Solution Overview
Problem
Existing logging techniques, such as wireline and logging while drilling (LWD), face challenges in accurately imaging the borehole wall due to erratic tool motion and poor wall contact, leading to subjective and error-prone log quality judgments, which can result in inaccurate or noisy images.
Innovation Solution
An automated log quality monitoring system using processors and memory to generate and display log quality indicators based on measurements from axially-spaced and azimuthally-spaced sensors, employing quality measures like cross-correlation, mutual information, and mean-square error, to quickly assess log quality and detect issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated log quality monitoring is implemented, then log quality assessment speed and accuracy improve, but system complexity increases
Solution Approach 1:
The patent creates a digital copy of the borehole wall image log and processes this copy through automated algorithms rather than requiring manual expert analysis. The system generates quality indicator values by comparing the image log data against expected geological patterns, enabling rapid automated assessment without physically examining the original logs.
Solution Approach 2:
The patent replaces the mechanical/manual process of expert log analysis with an automated computational system. Instead of experts manually reviewing image logs and assigning quality ratings, the system uses processors to automatically calculate quality indicator values based on mathematical algorithms that analyze log characteristics, thereby increasing productivity while managing complexity through software rather than hardware.
2Measurement precision
If multiple axially-spaced and azimuthally-spaced sensors are used, then measurement accuracy and log quality improve, but device complexity and cost increase
Solution Approach 1:
The patent divides the sensing system into multiple discrete sensor elements positioned at different axial locations and azimuthal angles around the borehole. Each sensor captures a specific portion of the borehole wall, and the system processes these segmented measurements individually before combining them into a complete image log. This segmentation enables high measurement precision while allowing the complex sensing task to be broken into manageable components.
Solution Approach 2:
The patent transitions from single-point measurements to three-dimensional spatial sampling by positioning sensors along both axial and azimuthal dimensions. This creates a multi-dimensional sensor array that captures borehole wall properties from multiple angles and depths, significantly improving measurement precision and enabling comprehensive log quality assessment through spatial redundancy.
3Adaptability or versatility
If subjective expert judgment is used for log quality assessment, then flexibility in evaluating complex geological features is maintained, but consistency and objectivity deteriorate
Solution Approach 1:
The patent transforms the subjective expert judgment process into an objective parameter-based assessment system. Instead of relying on expert intuition, the system calculates specific quality indicator values based on measurable parameters such as signal-to-noise ratio, resolution metrics, and geological pattern consistency. These quantifiable parameters provide consistent and objective quality assessment while maintaining adaptability through configurable thresholds and criteria.
Solution Approach 2:
The patent implements a feedback mechanism where the automated system continuously monitors log quality parameters and provides real-time quality indicator values. This feedback loop allows the system to adjust processing parameters and alert operators to quality issues, maintaining both objectivity through automated measurement and adaptability through responsive control based on the calculated indicators.
Data Source
AI summary
Disclosed systems and methods provide automated log quality monitoring, thereby enabling fast, on-site determination of log quality by logging engineers as well as re-assurance to interpreters faced with geologically-improbable features in the logs. Such uses can provide early detection of logging issues, increase confidence in acquired logs, reduce unnecessary duplication of effort, and improve the reputation of the logging company. In at least some embodiments, log monitoring software applies a comparison function to axially-spaced (and/or azimuthally-spaced) sensors. The comparison function can be, inter alia, cross-correlation, mutual information, mean-square error, and ratio image uniformity, each of which can be determined as a function of a sliding window position to indicate regions wherein the log quality falls below a threshold value. It is not necessary for the log sensors to be of the same type, e.g., resistivity image sensors.