Automated Drill Bit Damage Classification Using Neural Networks
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Solution Overview
Problem
Current methods for analyzing damage to drill bits and bottom hole assemblies (BHAs) are subjective, time-consuming, and costly, lacking automated techniques for identifying damage and its causes, which limits data analytics and widespread adoption in the oilfield industry.
Innovation Solution
A system utilizing supervised learning models and neural networks to identify the location, extent, type, and cause of damage to drill bits and BHAs from images, generating graphical outputs and enabling automated diagnosis and decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field personnel or trained drilling engineers manually classify drill bit and BHA damage, then diagnostic accuracy can be achieved, but the process becomes subjective and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated machine learning system. A neural network model trained on labeled drill bit and BHA images automatically classifies damage types, locations, and severity, eliminating subjective human interpretation while maintaining high accuracy and enabling rapid analysis of multiple images simultaneously.
Solution Approach 2:
The system enables self-service through automated damage classification without requiring trained drilling engineers or field personnel. The machine learning model independently analyzes images, generates damage reports, and provides diagnostic recommendations, making the forensics capability accessible to remote projects without specialized expertise.
2Productivity
If automated techniques are implemented, then analysis time is reduced, but the complexity of the system increases
Solution Approach 1:
The patent uses digital image copies of drill bits and BHAs as input for automated analysis. The machine learning model processes these digital representations rather than requiring physical inspection, enabling rapid replication and analysis of multiple images without additional hardware complexity beyond standard imaging equipment.
Solution Approach 2:
The system transforms physical damage characteristics into digital image parameters that can be processed by the neural network. By converting visual features into numerical data representations, the system enables automated classification while managing complexity through standard machine learning infrastructure rather than complex physical measurement systems.
3Measurement precision
If detailed damage analysis is performed, then diagnostic quality improves, but the cost becomes prohibitive for remote projects
Solution Approach 1:
The patent replaces expensive manual expert analysis with a cost-effective machine learning system. The automated model provides consistent, high-quality damage classification without requiring trained drilling engineers or field personnel, making detailed forensics analysis economically viable for remote projects with limited budgets and expertise.
Solution Approach 2:
The system enables remote projects to perform self-service damage analysis without external expert intervention. The machine learning model provides autonomous diagnostic capability, eliminating the need to hire specialized personnel or send samples to centralized facilities, thereby reducing implementation and operational costs while maintaining analysis quality.
Data Source
AI summary
A method for characterizing damage to bits or bottom hole assemblies can include identifying, via a supervised learning model, a location, an extent, a type, a consistency, or any combination thereof of damage to a bit or a bottom hole assembly from an image of the bit or the bottom hole assembly. A graphical output is generated based on the damage to the at least one component of the bit or the bottom hole assembly.