Damage and failure analysis of rock drilling bits (Figure 1) is a prerequisite to identification and mitigation of performance limiters, and subsequent process re-design and optimization while drilling oil, gas, geothermal and other wells. Damage analysis is also crucial to financial assessment for service providers and operators. Proper and consistent identification of the wear and failure modes of rock drilling bits is hindered by inconsistent image acquisition and analysis of the same. Currently, images are acquired and drill bit damage is classified manually using the IADC grading guidelines. The process is time-consuming, costly, and highly dependent on human perception.
Figure 1. Intact (a) and damaged (b) drill bits
The Research and Design Team at the University of Texas with Prof. Eric van Oort have created a device (Figure 2) and associated method that automates the acquisition and analysis of images of drill bits for documentation, warranty, forensic, or other purposes. The device is designed to be used on or near a drilling rig, in close physical and temporal proximity to the use of the bit. The device enables automatic image capturing and consistent analysis through the use of machine learning and other repeatable computational methods.
Figure 2. Basic schematic (without cameras)
Stage of development
Lab prototype. This technology is available for non-exclusive licensing. We are looking for partners to develop this device into a commercial product.
IP position: US Patent Application