Drill Sticking Prediction Based on Modal Decomposition and Physical Constraint Model of Near-Bit Data

Within the spectrum of complex downhole operational challenges, pipe sticking incidents emerge as one of the most prevalent and costly drilling complications. These incidents characteristically develop through progressive deterioration rather than abrupt failure, with discernible precursor signals typically manifesting as anomalous patterns in critical drilling parameters (torque fluctuations, drag anomalies, deviations in standpipe pressure). Consequently, early detection of these signals plays a pivotal role in mitigating pipe sticking occurrences. To systematically investigate the characteristic signatures pipe sticking events, this study employs two modal decomposition methods to extract salient features from near-bit downhole data. Conventional pipe sticking prediction methodologies exhibit three predominant limitations: rule-based systems suffer from poor generalizability, physics-based models demonstrate low computational efficiency, and data-driven techniques lack physical interpretability. To overcome these constraints, this study innovatively proposes a physically constrained prediction framework that integrates Variational Mode Decomposition (VMD) with near-bit measurement data. Experimental results demonstrate the superior predictive capability of the proposed VMD-based, near-bit data-physical constraint model. Based on a comprehensive evaluation using six benchmark models, the proposed approach achieves optimal performance, with an R2 metric of approximately 0.9, significantly outperforming existing algorithms. When deployed in actual drilling operations, this model exhibits robust early detection of pipe sticking precursors, enabling proactive intervention. The practical implementation of this framework facilitates timely corrective actions, thereby substantially reducing the incidence of downhole pipe sticking events and enhancing operational safety.