Machine Vision Cable Inspection for Wellsite Damage Detection
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Solution Overview
Problem
Existing methods for detecting cable damage in oilfield operations are unreliable and inefficient, relying on human observation which is prone to errors and resource wastage, and do not provide timely detection of cable damage, leading to potential catastrophic failures.
Innovation Solution
Implementing a system that uses machine vision with multiple cameras and machine-learning models to automatically detect cable damage by capturing and processing images of the cable, utilizing a convolutional neural network to classify frames as damaged or undamaged, and generating alerts to stop the cable spool or notify operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a human operator watches the cable to detect damage, then damage detection is performed, but the operator cannot give undivided attention for extended periods and may miss damage due to high cable speed or poor conditions
Solution Approach 1:
The patent replaces the human operator's visual inspection with an automated machine vision system comprising cameras, lighting, and image processing algorithms. This substitution eliminates human limitations such as attention span, fatigue, and environmental constraints, providing continuous reliable detection without sacrificing productivity.
Solution Approach 2:
The system enables the cable inspection process to be self-monitoring through automated capture and analysis of cable images. The machine vision system independently detects damage without requiring human intervention, allowing the cable to effectively inspect itself during operation.
2Measurement precision
If multiple cameras and machine-learning models are used to detect cable damage, then detection accuracy and timeliness improve, but system complexity increases
Solution Approach 1:
The patent divides the detection system into specialized components: multiple cameras positioned at different angles, dedicated lighting systems, region-of-interest extraction modules, and machine-learning classifiers. This segmentation allows each component to be optimized for its specific function while working together to achieve high detection accuracy.
Solution Approach 2:
The system introduces an intermediary processing layer between image capture and damage classification. The region-of-interest extractor and machine-learning model act as intermediaries that filter and analyze only relevant features, reducing computational complexity while maintaining high detection precision.
Data Source
AI summary
Systems and methods are disclosed herein for identifying cable damage using machine vision, such as on a cable being fed down a wellbore from a cable spool in an oil and gas operation. An example method can include providing a camera directed toward a cable that is winding upon, or unwinding from, a cable spool. The method can also include capturing a plurality of frames of images of the cable by the camera, such as by capturing video. The captured frames can be cropped to remove portions of the frames that do not include the cable. The method can further include processing the cropped frames using a machine-learning model. The machine-learning model can be trained using images of known cable damage as inputs so it can identify new instances of damage. The machine-learning model can further classify each cropped frame as including damage or not including damage.