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Drilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency. [ABSTRACT FROM AUTHOR]

In horizontal well operations, the prevalence of stuck pipe incidents is largely attributed to inadequate hole cleaning, underscoring the critical need for a thorough understanding of this process to mitigate non-productive time and financial losses. Increasing fluid velocity, the major drilling parameter of hole cleaning, diminishes the formation of cuttings beds in a wellbore. This study primarily centered on the mechanical displacement and removal of solid particles, employing advanced image processing techniques to elucidate the dynamic behavior of solid particles in deviated wellbores. The core objective of this study was to experimentally scrutinize the effects of a downhole clamp-on tool on fluid velocity to improve hole cleaning practices. To address this challenge, a customized flow loop was designed and constructed to accurately replicate the conditions encountered in horizontal wells. Pure water was used to demonstrate the effects of the clamp-on tool on cuttings transport for lightweight drilling fluid conditions. Strategically deployed, the clamp-on tool played a pivotal role in agitating cuttings, mitigating their accumulation at the bottom of the borehole. The tool's agitation mechanism noticeably improved cuttings removal by increasing velocity, extending the perturbation of cuttings transport in the tool's downstream flow, and reducing bedding formation. At lower flow rates, the tool led to an over fourfold increase in average particle velocity within the tool and a twofold increase after the tool. Our results demonstrate the substantial potential of mechanical assistance to address hole cleaning challenges and significantly advance horizontal well operations. [ABSTRACT FROM AUTHOR]