Deep Learning for Real-Time Casing Connection Quality Scoring
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Solution Overview
Problem
Existing drilling systems are reactive to poor casing connection quality, relying on skilled operators for identification, which is impractical and often results in time-consuming and costly repairs due to failed connections.
Innovation Solution
A deep learning model is trained to analyze torque-turns patterns to identify casing connection quality, allowing for real-time classification of joint connections and preventing the installation of poor quality connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled operators manually identify poor casing connection quality, then detection accuracy may be maintained, but the system becomes impractical and time-consuming
Solution Approach 1:
The patent replaces the mechanical/manual inspection system with an automated electronic system. Sensors collect torque and rotation data during casing connection, and a machine learning model automatically analyzes this data to classify connection quality, eliminating the need for manual operator inspection while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the casing connection system to automatically assess its own quality. The automated classification system provides real-time feedback on connection quality without requiring external operator intervention, making the system self-diagnosing and self-evaluating.
2Productivity
If poor quality connections are not identified until after installation, then installation speed is maintained, but repair costs and time increase significantly
Solution Approach 1:
The system performs preliminary assessment of connection quality during the installation process itself. By classifying connection quality in real-time before the casing is fully set and cemented, the system enables early detection and immediate correction of poor connections, preventing the need for costly post-installation repairs.
Solution Approach 2:
The system implements continuous feedback during the casing connection process. Sensors monitor torque and rotation parameters in real-time, and the machine learning model provides immediate classification feedback, allowing operators to adjust the connection process dynamically to ensure quality standards are met.
3Productivity
If automated systems are implemented to detect connection quality, then productivity improves, but system complexity increases
Solution Approach 1:
The system uses universal sensors that collect multiple parameters (torque, rotation, temperature) simultaneously for various assessment purposes. The machine learning model serves multiple functions including real-time classification, trend analysis, and predictive maintenance, reducing the need for separate specialized systems.
Solution Approach 2:
The patent introduces a data processing intermediary layer that bridges the simple sensor measurements and the complex classification decisions. The machine learning model acts as an intermediary that processes raw sensor data into meaningful quality assessments, shielding the complexity from both the physical sensing layer and the decision-making layer.
Data Source
AI summary
A casing installation manager may obtain a training dataset including a plurality of torque-turns datasets for a plurality of casing joint connections. Each of the plurality of torque-turns datasets may include a joint quality score for an associated casing joint connection of the plurality of casing joint connections. A casing installation manager may train, using the training dataset, a deep learning model to generate a new joint quality score for a new torque-turns dataset.