Drill Bit Cutter Mapping for Wear Grading and Lifespan Prediction
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Solution Overview
Problem
Drill bit cutters made from super-hard, wear-resistant materials like PDCs on tungsten carbide substrates still experience wear and degradation during drilling, leading to inefficiencies and the need for frequent replacements.
Innovation Solution
A machine learning-based system that analyzes images of the drill bit to identify and map context-specific cutter information, including grading values and surface parameters, to specific cutters in a 3D model, enabling more accurate predictions of cutter performance and lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If super-hard, wear-resistant materials like PDC on tungsten carbide substrate are used for cutters, then cutter strength and wear resistance are improved, but cutter lifespan is still limited due to wear and degradation during drilling
Solution Approach 1:
The system performs preliminary analysis of cutter wear and degradation by capturing images during drilling operations and using machine learning models to predict remaining cutter lifespan. This allows proactive scheduling of bit rotation and replacement before complete wear occurs, optimizing the utilization of high-strength cutters while preventing inefficient continued use of degraded cutters
Solution Approach 2:
The system implements continuous feedback by monitoring cutter condition through image capture and analysis during drilling operations. The machine learning models process real-time images to assess wear and degradation, providing feedback that informs decisions about when to rotate or replace drill bits, thereby maximizing cutter lifespan while maintaining drilling efficiency
2Productivity
If frequent inspection and replacement of cutters is performed, then drilling efficiency is maintained, but loss of time and operational productivity decrease
Solution Approach 1:
The system performs preliminary assessment of cutter wear during drilling operations using image capture and machine learning analysis. By predicting remaining cutter lifespan before complete wear occurs, the system allows drilling operations to continue efficiently without premature interruptions for inspection or replacement
Solution Approach 2:
The system implements automated self-service by capturing images and using machine learning models to autonomously assess cutter wear and predict lifespan. This eliminates the need for manual inspection and provides automated recommendations for bit rotation or replacement, reducing operational downtime while maintaining drilling efficiency
3Measurement precision
If manual inspection and assessment of cutter wear is performed, then cutter condition can be evaluated, but measurement precision and consistency are insufficient
Solution Approach 1:
The system replaces manual mechanical inspection with an automated machine learning-based image analysis system. The machine learning models process images of cutters to objectively assess wear and degradation, providing consistent and precise measurements that eliminate the variability and subjectivity inherent in manual inspection methods
Solution Approach 2:
The system creates digital copies of cutters through image capture and uses machine learning models to analyze these copies. This allows detailed assessment of cutter wear without physical contact or complex measurement devices, achieving high measurement precision through computational analysis of visual data
Data Source
AI summary
Disclosed herein are a method and apparatus for cutter analysis and mapping. In one embodiment, a computer implemented method comprises acquiring a training set of images, wherein the training set of images comprise a set of images of cutters; and training a machine learning system using the training set of images, wherein the machine learning system comprises a set of weights corresponding to one or more nodes of a neural network of the machine learning system, and wherein the machine learning system provides an output representing whether an image includes a visual representation of a cutter.