Wellsite Machine Vision for Human Safety Risk Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the oil and gas industry, existing systems lack effective methods to detect and mitigate risks to human safety in real-time, particularly in environments where heavy machinery and pipes are in motion, leading to potential accidents.
Innovation Solution
A system utilizing computer vision and machine learning models to analyze imagery data from multiple cameras, detect movement of equipment and humans, and issue alerts or instructions to reduce risk, ensuring safe operation by dynamically focusing resources on high-risk areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer vision and machine learning models are used to analyze imagery data from multiple cameras to detect movement and issue real-time alerts, then human safety is enhanced and accident likelihood is reduced, but system complexity and computational resource requirements increase
Solution Approach 1:
The system divides the wellsite monitoring into multiple independent camera zones, each capturing specific areas of interest. The machine learning model processes imagery data from multiple cameras separately, detecting movement in each zone independently before integrating results. This segmentation allows the complex safety monitoring task to be broken down into manageable components, reducing overall system complexity while maintaining comprehensive safety coverage.
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and safety alert generation. The machine learning model acts as an intermediary that analyzes imagery data, detects movement patterns, and determines risk levels before triggering alerts. This intermediary layer abstracts the complexity of movement detection and risk assessment, allowing the system to maintain high reliability while managing computational complexity through modular architecture.
2Measurement precision
If multiple cameras are deployed to cover all areas of the wellsite for comprehensive monitoring, then detection accuracy and safety coverage are improved, but system cost and complexity increase
Solution Approach 1:
The system applies local quality by deploying cameras strategically in specific zones where movement detection is most critical, rather than uniformly distributing cameras across the entire wellsite. The machine learning model focuses computational resources on analyzing imagery from these high-priority zones, achieving high detection accuracy in critical areas while minimizing the total number of cameras required. This approach optimizes the balance between detection accuracy and system complexity by concentrating monitoring resources where they provide maximum safety value.
Data Source
AI summary
A method may include receiving imagery data from a wellsite; analyzing the imagery data to detect movement; 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.