AI Image Processing for Drilling Elevator Latching Compatibility
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Solution Overview
Problem
Current mechanical systems for preventing dropped strings in drilling operations are prone to human errors, leading to incorrect elevator latching and sizing issues, which can result in non-production time and potential well loss.
Innovation Solution
Implementing camera analytics with artificial intelligence models to analyze images of rig components, determining the operability and compatibility of elevators and latching string components, and generating instructions to ensure correct latching or initiating an interlock to prevent unsafe operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical systems and human operators are used to prevent dropped strings, then operational flexibility and adaptability are maintained, but human errors lead to incorrect elevator latching and sizing issues
Solution Approach 1:
The patent replaces manual visual inspection and mechanical verification systems with an automated computer vision system using cameras and machine learning algorithms. The system automatically captures images of elevators and string components, analyzes them using trained models, and determines compatibility without human intervention, thereby eliminating human error while maintaining operational reliability
Solution Approach 2:
The system enables self-verification by automatically comparing detected elevator and string component features against compatibility criteria. The machine learning model independently assesses whether components are compatible without requiring human operator judgment, allowing the system to self-validate component matching and prevent incompatible latching
2Measurement precision
If automated image processing with AI models is implemented, then measurement precision and detection accuracy are improved, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional integrated system where a single computer vision platform performs multiple tasks: capturing images, detecting elevator types, identifying string component types, measuring dimensions, and determining compatibility. This universal system consolidates what would otherwise require multiple separate devices and processes, managing complexity through functional integration
Solution Approach 2:
The system uses pre-trained machine learning models that have been previously trained on extensive datasets of elevator and string component images. This preliminary training action allows the models to perform accurate detection and classification during actual operation without requiring complex real-time processing, transferring the complexity burden to the offline training phase
Data Source
AI summary
A method and a system for reducing the risk of dopped string through the implementation of image processing are disclosed. The method includes obtaining a plurality of images of a rotary table, a lower elevator latching position, and an upper elevator position from a rig and analyzing the plurality of images of the rotary table, the lower elevator latching position, and the upper elevator position with an artificial intelligence (AI) model to detect a type of the elevators and a latching string component. Further, an operability of the elevators and an operability of the latching string component, and a compatibility between the latching string component and the elevators are determined. Additionally, an instruction based on the determined compatibility is generated, the instruction allowing a latching process to continue or initiating an interlock to stop the latching process.