IADC Dull Code Upgrade: Photometric Classification and Quantification of the New Dull Codes

Abstract

In the world of drilling, the drill bit dull condition contains our best forensic evidence of the drilling assembly's interaction with the formation. Dull grading forensics is the first place to look to identify drilling dysfunction yet commonly overlooked or misunderstood by operators. The drill bit dull condition can be leveraged to learn about the formation, drilling dynamics and drilling practices (Watson et. al. 2022). The IADC bit dull grading classification system received its most recent revision in 1992 and currently consists of an average inner and outer dull grade severity, rated from 0 – 8 with a major and other dull characteristic along with a reason pulled. These grades can be used to make critical operational and bit design decisions to overcome drilling challenges thereby improving performance and allowing drilling teams to drill consistently further and faster. The oil and gas industry is becoming more reliant on digitally enabled applications to improve performance through big data, machine learning and automation, but at the time of this paper, the critical IADC dull grading system has remained the same. It is still a crude and subjective characterization of the complex drill bit dull condition. A key challenge with the current classification system and industry standard grading technique is that it is highly dependent on the person grading the bit. Personal subjectivity and lack of training can result in key forensic evidence being overlooked that otherwise could have aided in understanding the root cause of drilling dysfunction. A cross disciplinary committee of subject matter experts (SME's) from operators, drill bit providers, cutter manufacturers, and digital solution providers have convened to define and introduce a new standard dull grading system as replacement for the current outdated IADC dull grading. The new dull grading system will allow for an objective cutter-by-cutter dull grading to be stored with relevant drilling data with reduced subjectivity and enhanced accuracy. With recent advancements in mobile phone hardware and applications, a solution was developed that delivers high quality, cutter-by-cutter dull grading automatically and connecting with drilling meta data from a drilling records database containing over 1.8 million well records with over 5 million bottom-hole assembly (BHA) runs. It leverages videos with machine learning combined with an algorithm to deliver cutter specific, major dull characteristics of a scanned bit. This high quality photographic digital dull information is incorporated into workflows allowing for rapid improvement in cutting structure and cutter development lifecycle timelines leading to rapid improvements in drilling performance for operators.