Application of AI to Drill Bit Forensics

Recorded On: 08/18/2021

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.

This webinar is categorized under the Drilling technical discipline

All content contained within this webinar is copyrighted by Dr. Pradeep Ashok and its use and/or reproduction outside the portal requires express permission from Dr. Pradeep Ashok.

For more information on this topic, please check out the SPE suggested reading links below. Here you will find topic-related books, publications, and papers for purchase in the SPE Bookstore.

OnePetro Papers:

Drill Bit Failure Forensics using 2D Bit Images Captured at the Rig Site

Drill Bit Damage Assessment Using Image Analysis and Deep Learning as an Alternative to Traditional IADC Dull Grading

Dr. Pradeep Ashok

Mechanical Engineering and a Senior Research Scientist, The University of Texas (UT) at Austin

Pradeep Ashok is a PhD in Mechanical Engineering and a Senior Research Scientist in the Rig Automation and Performance Improvement in Drilling (RAPID) Group at The University of Texas (UT) at Austin. Previously he was the Program Manager and Chief Scientist of the Robotics Research Group at UT, where he managed automation projects funded by ONR, NASA, DARPA, John Deere, Union Pacific and Intuitive Surgical. He has published more than 45 papers in the field of drilling on topics ranging from sensor data validation, tortuosity index, directional drilling optimization, managed pressure drilling, advanced hydraulics, big data analysis including storyboarding and spiderbots, high frequency data analysis, cuttings transport sensor, X ray based densitometer, automated tripping, automated kick detection, drilling dysfunction analysis, real-time washout detection, CBM of top drives, change management, real-time torque and drag modeling, etc. He manages the data analytics program in RAPID that works towards developing data analytics skills in future workforce. He actively volunteers his time for the industry and is a committee member in the SPE ATCE and the SPE/IADC Drilling conferences. He is an active participant in the Open Subsurface Data Universe (OSDU) effort that is currently ongoing. He is also the CTO of Intellicess, an oil and gas services software company he co-founded in 2010.

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Application of AI to Drill Bit Forensics
08/18/2021 at 10:00 AM (EDT)   |  90 minutes
08/18/2021 at 10:00 AM (EDT)   |  90 minutes
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Live and Archive Viewing: 0.15 CEU/1.5 PDH credits and certificate available
Live and Archive Viewing: 0.15 CEU/1.5 PDH credits and certificate available