Borehole Artifact Detection Using Reference Patterns for Fracture Analysis
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Solution Overview
Problem
Conventional techniques for identifying fractures in borehole data are inefficient due to image distortions and noise, leading to improper fracture detection and increased computational resource waste.
Innovation Solution
Utilizing reference data patterns to detect and label artifacts such as missing value, line, salt and pepper, stretching, and spiral patterns within borehole data, thereby generating labeled data to exclude artifacts from feature detection processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fracture detection techniques are applied to borehole data, then fracture identification is performed, but image distortions and noise cause improper detection and waste of computational resources
Solution Approach 1:
The patent applies preliminary action by detecting and labeling artifacts (missing values, lines, salt-and-pepper noise, stretching, spirals) in the borehole data before performing fracture detection. This pre-processing step identifies and excludes flawed data regions, ensuring that subsequent fracture detection algorithms operate only on clean, reliable data, thereby improving detection accuracy and reducing computational waste on compromised data.
Solution Approach 2:
The patent extracts and separates artifact regions from valid borehole data by applying reference data patterns to identify and label problematic areas. This extraction process isolates the harmful elements (artifacts) from the useful information (fracture data), allowing the fracture detection system to focus computational resources solely on clean data regions, thus resolving the contradiction between detection precision and energy consumption.
2Measurement precision
If artifact detection using reference data patterns is applied, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses copying by creating reference data patterns that represent known artifact types (missing values, lines, salt-and-pepper noise, stretching, spirals). These reference patterns are copied and applied to the borehole data through pattern matching operations. This copying approach simplifies the detection process by comparing against pre-defined templates rather than requiring complex real-time analysis, thus improving accuracy without significantly increasing system complexity.
Solution Approach 2:
The patent applies segmentation by dividing the artifact detection task into separate modules, each handling a specific artifact type (missing values, lines, salt-and-pepper noise, stretching, spirals). This segmentation allows the system to process different artifact types independently using specialized reference patterns, making the overall system more manageable and less complex while maintaining high detection accuracy for each artifact category.
Data Source
AI summary
A method includes receiving, borehole data and receiving one or more reference data patterns from a storage component. The one or more reference data patterns correspond to one or more borehole data artifacts. Further, the method includes applying the one or more reference data patterns to the borehole data to detect one or more regions in the borehole data that include a potential artifact. Even further, the method includes determining one or more artifact scores associated with the one or more regions based on a comparison between the one or more regions and the one or more reference data patterns. Even further, the method includes generating a label based on a portion of the one or more regions. The label is indicative of a respective region of the portion having an artifact score below a threshold value. Further still, the method includes generating the labeled borehole data.