Cuttings beds in horizontal wells significantly affect the frictional torque and drag along the drill string; however, their quantification and modeling have been relatively underexplored. To gain deeper insights into the impact mechanisms of the cuttings bed distribution on drilling mechanics, this study establishes a model linking the cuttings bed height with variations in axial and tangential forces on the drill string through experimental investigations. By integrating this model with previously developed transient cuttings transport and torque–drag models, a coupled transient hole cleaning and drill string mechanics model is constructed. This comprehensive model simulates the dynamic distribution of cuttings along the entire well trajectory and its influence on the drill string torque and drag. The results reveal that accumulated cuttings significantly reduce the weight on bit (WOB), increase the drill string torque, and cause problems related to a high equivalent circulation density (ECD). For long horizontal sections, the key to achieving effective hole cleaning lies in optimizing the design of the tripping circulation time to ensure that all cuttings are removed from the wellbore. Using the proposed coupled model, a methodology is developed to minimize the tripping circulation time by solving optimization problems within a constrained 2D domain, providing scientific guidance for drilling operations. The findings demonstrate that dynamically managing the cuttings distribution in the wellbore can significantly mitigate issues arising from insufficient hole cleaning, thereby ensuring drilling safety and efficiency. This study provides a scientific foundation for the optimized design of long horizontal well drilling operations and highlights the critical role of cuttings management in enhancing hole cleaning performance and mitigating drilling risks.

Oil and gas drilling is a capital-intensive industry where minimizing costs is paramount. Invisible Lost Time (ILT) during drilling often causes delays and escalates expenses. Accurate rig state classification is critical for quantifying ILT and improving drilling efficiency. In this study, we introduce a novel rig state classification model based on the Class-Specific Attribute Weighted Pseudo-Dynamic Bayes (CAWPDB) algorithm. Unlike existing machine learning methods, which often underutilize domain knowledge, our approach seamlessly integrates drilling expertise by constructing a pseudo-dynamic Bayesian model informed by an expert-derived state transition matrix. To overcome the limitations of the attribute conditional-independence assumption and boost classification performance, we incorporate a class-specific attribute-weighting mechanism. Experimental results from two wells demonstrate that the model achieves a 99.985% accuracy in identifying rig states from surface sensor data, with outstanding performance even for low-frequency rig states. This research enhances ILT visualization and provides valuable insights for optimizing drilling operations and reducing costs.