Abstract
This paper presents the design and analysis of a database containing more than 24,000 polycrystalline diamond compact (PDC) cutter scrape tests performed using a pressurized experimental laboratory setup. This unique data set contains high-frequency (2 kHz) measurements of the three-axis forces acting on the cutter in various rocks, cutter types and sizes, cutter orientations, depths-of-cut (DOC), and confining pressures. The database can be accessed, visualized, sorted, classified, and analyzed in detail, offering substantial opportunities to increase the knowledge of the rock-cutting process by PDC cutters. Using this database, the authors initially analyzed the rock type, cutter orientation, and DOC effects on the forces acting on the cutter. By using signal processing techniques, algorithms were developed to extract features from the force data. For example, each test was scored based on the rock chipping that occurred during cutting, which manifests itself as sawtooth patterns in the force data. These data allowed for qualitative evaluation of the effects of confining pressure and DOC on the chipping. In addition, the rock types most prone to chipping were identified. This massive dataset allowed for statistical quantification of the forces acting on the cutters, which resulted in new ways for modeling the rock-cutting process, which is inherently stochastic. Furthermore, it is also possible to quantify the efficiency improvement with different cutter shapes when cutting different rocks, which assists in selecting the correct cutter shape for the targeted application to improve drilling performance. In summary, this extensive database provides novel understandings of the rock-cutting process, which are fundamental to the efficiency of the drilling operations, drilling dynamics modeling, and drilling tool development.
