Failure Forensics of Shaped PDC Cutters Using Image Analysis and Deep Learning

Abstract

One of the major advances in polycrystalline diamond compact (PDC) bits in the last 10 years is the global adoption of 3D-shaped PDC cutters. By manipulating the cutter shape based on the understandings of cutter–rock interaction mechanisms, the cutting efficiency and mechanical properties of PDC cutters have been greatly improved. Ongoing innovations in 3D-shaped PDC cutter technology are critical to overcoming the more and more challenging formations in ultradeep wells, such as the 10 000-m-deep wells being drilled in China. Such an important role for 3D-shaped PDC cutters in oil and gas drilling applications necessitates a complete and effective failure analysis method. However, the current International Association of Drilling Contractors (IADC) dull grading cannot fulfill this objective. It is out of date in judging the damages to PDC bits and exhibits more limitations in addressing the unique challenges presented by complicated cutter shapes. To address this issue, an intelligent recognition model for PDC bit damage identification was developed based on the image analysis technology and the YOLOv7 algorithm. More than 10,000 dull bit images were used to train and validate this intelligent recognition model, which were collected from 363 PDC bits that suffered different degrees of damage after being used to drill 185 wells in the Sinopec Shengli Oilfield. Compared to the existing models, the developed intelligent recognition model has several notable contributions. First, the developed model is capable of recognizing the damages of various shaped PDC cutters commonly used by the global bit manufacturers, enabling a more accurate assessment of the failure behaviors of shaped cutters and their bits. The detection accuracy of the developed model exceeds 80% based on the confusion matrix. The recognition results by the developed artificial intelligence (AI) model are consistent with the actual failure modes judged by experienced drilling engineers. Second, the developed AI model provides direct qualitative identification of the failure modes and failure reasons for both cutters and PDC bits rather than the quantitative evaluation of the missing diamond layer used by IADC dull grading. Furthermore, the developed model eliminates the effect of reclaimed cutters on the AI detection results based on the implicit use of spatial cues in the YOLOv7 algorithm. The intelligent recognition model developed in this work can provide reliable and valuable guidance for the post-run evaluation, the bit selection for the next run, and the iterative optimization of bit design.