Real-time Rock Cutting Characterization using Validated Image-based Dual Shape Measurements

Abstract

Real-time characterization of drill cuttings is increasingly recognized as a valuable tool for drilling optimization, providing insights into lithology, bit wear, and rock breakage mechanisms beyond conventional sieve and laser diffraction methods, which are limited to offline analysis and lack shape data. Recent advances in image-based techniques address these limitations by enabling automated monitoring of particle size and morphology during drilling operations. This study aims to develop and validate an image-based methodology that integrates dual size and shape analysis of drill cuttings for improved real-time performance evaluation. Over 160 cutting samples from large-diameter disc cutter field trials were analyzed using both ASTM-standard sieve analysis and dynamic image analysis (DIA). A custom imaging system with controlled lighting and automated camera motion captured high- resolution images, which were processed using watershed segmentation and shape descriptors (circularity and roundness). Results showed that image-based shortest dimension measurements closely matched sieve distributions for particles >0.075 mm, while the dual shape–size classification provided actionable insights into drilling efficiency. Error analyses confirmed minimal discrepancies, except at very fine sizes. The validated approach demonstrates strong potential for integration into drilling automation, enabling optimized parameter control, reduced mechanical specific energy, and enhanced decision-making for field-scale operations.