An automatic workflow for the quantitative evaluation of bit wear based on computer vision

As global oil exploration ventures into deeper and more complex territories, drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety. Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors (IADC), the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers' experience. Consequently, forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging. Therefore, an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency. This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database. Initially, a method for acquiring images of worn bits at the drilling site was developed. Subsequently, the wear classification and grading models based on computer vision were established to determine bit status. The wear classification model focuses on the positioning and classification of bit cutters, while the wear grading model quantifies the extent of bit wear. After that, the automatic evaluation method of the bit wear is realized. Additionally, bit wear evaluation software was designed, integrating all necessary functions to assess bit wear in accordance with IADC standards. Finally, a drilling bit database was created by integrating bit wear data, logging data, mud-logging data, and basic drilling bit data. This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites. It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits, marking the inception of intelligent analysis, design, and manufacture of drilling bits, thereby enhancing performance in challenging drilling conditions. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).