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Application of AI to Drill Bit Forensics

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Course Credit: 0.15 CEU, 1.5 PDH

In the absence of downhole sensors, the drill bit is one of the best indicators of downhole conditions. Quick evaluation of a drill bit after it has been pulled out, can help optimize drilling parameter and bit selection for subsequent bit runs. Traditionally the evaluation of drill bits is performed by the crew at the rig site and (or) drill bit subject matter experts who are typically off site. Such evaluations tend be to be subjective and are often biased depending on the background of the person doing the evaluation.

AI applied to object recognition and image processing has developed to a level of sophistication whereby the task of drill bit forensics can be fully automated – and a lot of the subjectivity removed from the process. Researchers at The University of Texas at Austin have in recent past developed algorithms to perform drill bit forensics directly from bit images captured on mobile phones. This is a four step process involving the recognition of the cutters on the drill bit, the grading of the cutters, detecting the location of the cutters on the drill bit and finally providing an overall evaluation of the bit. This webinar will go into details of the approach pursued, and will provide guidance on what we can expect in the near future.

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Course Chapters

  • 1Application of AI to Drill Bit Forensics - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Dr. Pradeep Ashok