Drill Bit Wear Grading Using Real-Time Visual Analytics
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Solution Overview
Problem
Existing drill bit grading methods are subjective and time-consuming, leading to inconsistent evaluations and delayed decision-making regarding drill bit replacement.
Innovation Solution
An automated process using visual analytics and real-time analysis of tool data from sensors to determine the wear condition of downhole tools, including drill bits, by comparing pre and post-operation data to correlate with industry standards and identify causes of wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual observation and grading is used, then the process is simple to implement, but the assessment is subjective and time-consuming
Solution Approach 1:
The patent replaces manual visual observation with an automated image acquisition system using cameras and sensors. The mechanical system of manual grading is substituted with an automated optical measurement system that captures images of the drill bit and processes them through software algorithms to determine wear conditions objectively and rapidly.
Solution Approach 2:
The patent creates a digital copy (image) of the drill bit surface instead of requiring physical manual inspection. The image acquisition system captures the visual characteristics of the bit, and software analyzes these copies to determine wear, eliminating the need for repeated manual handling and subjective visual assessment.
2Loss of time
If manual grading is used, then the equipment requirement is minimal, but the decision-making is delayed
Solution Approach 1:
The system performs preliminary image capture and automated analysis immediately when the drill bit is removed from the wellbore. The image acquisition and processing occur before any decision-making is needed, providing timely wear assessment data that enables rapid replacement decisions without delaying operations.
Solution Approach 2:
The automated system provides immediate feedback on drill bit wear conditions through processed image data. This real-time feedback loop allows operators to quickly assess bit status and make informed replacement decisions, eliminating the time delays associated with manual grading procedures.
3Measurement precision
If automated image acquisition is implemented, then the assessment becomes objective and timely, but the device complexity increases
Solution Approach 1:
The patent designs the image acquisition system to serve multiple functions: capturing drill bit images, analyzing wear conditions, and providing replacement recommendations. This multi-functional approach consolidates several operations into a single integrated system, reducing overall complexity compared to having separate specialized equipment for each function.
Solution Approach 2:
The patent introduces software as an intermediary between the hardware components and the final wear assessment. The software processes images from multiple sensors, applies analysis algorithms, and generates conclusions. This software intermediary layer simplifies the overall system by centralizing complex processing logic rather than requiring complex hardware interconnections.
Data Source
AI summary
The disclosure provides an automated process for determining the wear condition of a downhole tool that removes the subjectivity associated with manual observation. The automated process can advantageously evaluate a wear condition of a downhole tool using visual analytics and real-time analysis after the downhole tool has been extracted from the wellbore. An example of a method includes: (1) securing a downhole tool in a rig assembly, (2) obtaining, using sensors, surround tool data of the downhole tool in the rig assembly, wherein the surround tool data includes a first set of surround tool data obtained before a downhole operation by the downhole tool and a second set of surround tool data obtained after the downhole operation, and (3) automatically determining a wear condition of the downhole tool in real time by comparing the second set of surround tool data to the first set of surround tool data.