Abstract: A drill bit includes a bit body, one or more blades forming part of the bit body, a plurality of cutter pockets defined in the one or more blades, each cutter pocket providing a floor and an orifice defined in the floor, and a plurality of cutters secured to the one or more blades at the plurality of cutter pockets. Each cutter includes a substrate, a diamond table secured to the substrate at an interface, and a retention feature extending from a bottom surface of the substrate and sized to be received within the orifice of a corresponding one of the plurality of cutter pockets.

Abstract: Methods and systems for well tool wear classification system are provided. A wear classifier tool is configured to classify wear of a scanned well tool using a machine learning engine. Computer-readable memory stores a training dataset and a trained ML model. The training data set includes scanned image data and associated labels representative of classification types of failure. The trained ML model has a neural network. The wear classifier tool can output data identifying a failure mode of the scanned well tool based on classification of input by the machine learning engine. A database is configured to stored historical data on scanner type, patterns of scanner cutting elements, sensor type, and age and usage conditions. A scanning system includes a camera and a three-dimensional (3D) scanner configured to scan a drill bit.

Abstract: A method of quantifying wear data on a well tool includes scanning a used well tool with a scanner and thereby generating a scanned file of the used well tool, the scanner being in communication with a computer system, aligning the scanned file with an original solid model file of the used well tool and thereby obtaining and displaying an overlay output, wherein the solid model file is stored on the computer system and identifies individual wear parts of the used well tool, creating and placing a digital feature on one or more of the individual wear parts displayed on the overlay output, and calculating deviation between the solid model file and the scanned file at each digital feature of the one or more individual wear parts and thereby determining material removed from the individual wear parts of the used well tool.

Abstract: A method of quantifying wear data on a well tool includes identifying the well tool based on a solid model file and a data file corresponding to the well tool, wherein the solid model and data files are stored on a computer system and separately identify wear parts of the well tool, scanning the well tool with a scanner and thereby generating a scanned file of the well tool, the scanner being in communication with the computer system, aligning the scanned file with the solid model file to obtain an overlay output, creating digital features on the wear parts of the scanned file, and calculating deviation between the solid model file and the scanned file at the digital features for the wear parts and thereby determining material removed from the wear parts of the well tool.

Abstract: A method of quantifying wear data on a well tool includes identifying the well tool based on a solid model file and a data file corresponding to the well tool, wherein the solid model and data files are stored on a computer system and separately identify wear parts of the well tool, scanning the well tool with a scanner and thereby generating a scanned file of the well tool, the scanner being in communication with the computer system, aligning the scanned file with the solid model file to obtain an overlay output, creating digital features on the wear parts of the scanned file, and calculating deviation between the solid model file and the scanned file at the digital features for the wear parts and thereby determining material removed from the wear parts of the well tool.