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Automatic Computer Generated Dull Bit Grading

Currently, oilfield personnel analyze and dull grade drill bits through visual inspection which can be subjective. The goal of this project is to build a web application that automates drill bit dull grading in order to improve consistency and accuracy. To perform the dull grading, the system calculates the damage of individual drill bit cutters using digital images as input. By performing an automated process, the system will provide a higher level of reliability, consistency, and accuracy. The uniform storage and retrieval of data will also contribute to the enhancement of the current grading process as it provides consistency among critical information. Our solution includes a machine-learning model trained to extract images of individual cutters as well as image processing techniques to calculate the wear of each cutter.

Team Members: 

Seonghyon Baek

Jared Cormier

Eric Graves

Aditya Prodduturu

Baker Hughes, a GE Company