Wellsite Machine Vision for Catwalk Hazard Detection
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Solution Overview
Problem
In the oil and gas industry, there is a need to monitor and mitigate risks to human safety at wellsites, particularly around catwalk systems where equipment movement poses a hazard, and existing systems lack effective real-time monitoring and alert mechanisms to prevent accidents.
Innovation Solution
A method and system utilizing imagery data from cameras to detect movement and presence of humans relative to catwalk systems, employing machine learning models to analyze the data and issue alerts or instructions to reduce risk, such as activating alarms or instructing personnel to move away from hazardous areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring of catwalk systems is implemented using imagery data and machine learning, then safety and risk mitigation are improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces manual safety monitoring and physical intervention systems with an automated computer vision system using imagery data capture devices (cameras) and machine learning models. This substitution reduces the need for human operators to physically monitor catwalk systems while maintaining or improving safety through continuous automated surveillance and risk assessment.
Solution Approach 2:
The system enables self-service safety monitoring by automatically capturing imagery data, analyzing it through machine learning models, detecting risks, and issuing alerts without requiring constant human intervention. The catwalk system essentially monitors itself for safety hazards, reducing operational complexity while improving reliability.
2Reliability
If continuous imagery data analysis is performed to detect movement and assess risk, then human safety is improved, but use of energy and computational resources increase
Solution Approach 1:
Instead of continuously analyzing all imagery data at full computational capacity, the system employs periodic analysis triggered by specific events such as detected movement or changes in the operational state of the catwalk system. This approach maintains high safety standards while significantly reducing average computational resource consumption by focusing processing power only when necessary.
Solution Approach 2:
The system performs preliminary analysis by first detecting movement or changes in the imagery data before conducting full risk assessment. This two-stage approach allows the system to filter out non-critical events and only allocate full computational resources when actual risks are detected, optimizing the balance between safety and resource usage.
Data Source
AI summary
A method may include receiving imagery data from a wellsite that includes a catwalk system; analyzing the imagery data to detect movement with respect to the catwalk system; determining a risk to a human at the wellsite based on the detected movement; and, responsive to the determining, issuing an instruction to reduce the risk.