Computer Vision Drilling Safety and Real-Time Well Plan Control
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Solution Overview
Problem
Current drilling technologies face challenges in managing the complexity and safety of oilfield operations, particularly in directional drilling, leading to costly errors and potential long-term well output reduction due to human decision-making limitations and the need for improved supervision and control.
Innovation Solution
A computer vision system and method that utilizes sensor data and computer-aided decision-making to update well plans in real-time, incorporating stratigraphic information and heat maps to guide the drilling process, enabling semi-automatic or automatic control of drilling equipment for enhanced precision and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual operations and human decision-making are used in drilling operations, then operational flexibility and adaptability are maintained, but human error increases and safety decreases
Solution Approach 1:
The patent replaces manual human operations with automated computer vision systems and control algorithms. The system uses machine learning models to detect drilling equipment states, monitor personnel positions, and control drilling parameters automatically, substituting human decision-making with algorithm-based automation to reduce human error while maintaining operational reliability
Solution Approach 2:
The patent introduces computer vision systems and sensor networks as intermediaries between the drilling environment and control decisions. These intermediaries capture visual and sensor data, process it through analysis algorithms, and translate it into actionable control commands, creating a layered architecture that improves safety without requiring direct human intervention in all operations
2Manufacturing precision
If real-time automated control systems are implemented, then drilling accuracy and safety improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex drilling control system into modular functional components: computer vision modules for equipment and personnel detection, sensor data acquisition modules, machine learning analysis modules, and control execution modules. Each module performs a specific function and can be independently optimized, managed, and maintained, reducing overall system complexity while enabling real-time automated control for precise drilling path accuracy
3Measurement precision
If computer vision systems are used to monitor drilling operations, then operational safety and accuracy improve, but computational processing requirements and data handling complexity increase
Solution Approach 1:
The patent implements pre-trained machine learning models that are prepared in advance for specific drilling equipment and scenarios. These models are trained offline on extensive datasets and deployed to perform real-time inference during drilling operations. The preliminary training and model preparation enable rapid, accurate detection of equipment locations and states during operations without requiring complex real-time data processing, reducing computational overhead while maintaining high measurement precision
Data Source
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AI summary
Computer vision drilling systems and methods may be used with a drilling rig. The computer vision systems and methods may be used during drilling of a well to monitor the drilling equipment and personnel on the drilling site to provide safer drilling operations. The results from the computer vision drilling system may be used to cause corrective actions to be performed if a safety condition arises. In addition, computer vision systems and methods are provided to automatically monitor the drilling site and drilling operations, such as by tallying pipe in the drill string and by monitoring equipment for anomalous drilling conditions, and automatically taking corrective action as may be needed.