Drill Bit Wear Classification via 3D Scanning and ML
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Solution Overview
Problem
The oil and gas industry faces challenges in accurately determining and quantifying drill bit wear, leading to inefficiencies in wellbore penetration and increased costs due to subjective and time-consuming manual grading processes.
Innovation Solution
A method involving 3D or 2D scanning of drill bits to generate precise wear data by aligning scanned images with CAD models, allowing for automated measurement of discrete wear parts and material loss, and utilizing machine learning for classification and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection and quantification of drill bit wear is performed using the IADC dull grading system, then wear classification can be established, but the process becomes time-consuming, subjective, and inaccurate
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical scanning system. A 3D scanner captures detailed geometry of the drill bit, and software automatically analyzes wear patterns, eliminating the need for manual visual inspection and quantification while improving both accuracy and speed.
Solution Approach 2:
The patent creates a digital 3D copy of the drill bit through scanning. This digital replica allows for repeated, consistent analysis without the variability of manual inspection. The scanned data can be measured multiple times with identical results, eliminating subjectivity while preserving all wear information.
2Reliability
If manual wear evaluation is performed by skilled personnel, then wear data can be documented, but the process is highly subjective and rarely repeatable
Solution Approach 1:
The patent replaces human judgment with automated optical measurement systems. The 3D scanner and analysis software provide objective, consistent measurements that are not influenced by operator skill levels or subjectivity, ensuring high repeatability across different evaluations.
Solution Approach 2:
The system performs self-measurement and self-analysis of wear patterns. The automated software independently processes the scanned data, identifies wear features, and generates classification results without requiring skilled personnel intervention, thereby eliminating variability in evaluation.
3Productivity
If drill bits are removed from operation when dull, then bit performance degrades are prevented, but drilling efficiency decreases due to frequent bit changes
Solution Approach 1:
The system performs preliminary wear assessment by scanning and analyzing drill bits in real-time or near-real-time during operations. This allows operators to determine optimal bit replacement timing based on actual wear data rather than predetermined intervals, maximizing bit utilization while maintaining performance.
Solution Approach 2:
The patent implements a feedback loop where wear data from scanned bits is analyzed and used to inform future drilling decisions. This continuous monitoring and analysis enables data-driven decisions about bit replacement timing, optimizing the balance between maintaining performance and maximizing productivity.
Data Source
AI summary
Methods and systems for well tool wear classification system are provided. A wear classifier tool is configured to classify wear of a scanned well tool using a machine learning engine. Computer-readable memory stores a training dataset and a trained ML model. The training data set includes scanned image data and associated labels representative of classification types of failure. The trained ML model has a neural network. The wear classifier tool can output data identifying a failure mode of the scanned well tool based on classification of input by the machine learning engine. A database is configured to stored historical data on scanner type, patterns of scanner cutting elements, sensor type, and age and usage conditions. A scanning system includes a camera and a three-dimensional (3D) scanner configured to scan a drill bit.