Stuck pipe anomaly early warning model based on improved random forest algorithms

With the ever increase in drilling depth, the drilling progress has become increasingly complex, and stuck pipe incidents have frequently emerged as a key factor restricting the safety and efficiency of drilling operations. Traditional approaches struggle to promptly identify and effectively warn stuck pipe risks. In recent years, the rise of machine learning algorithms has provided new avenues for the early prediction and prevention of stuck pipe incidents. In this study, a drilling operation condition recognition model was established based on the variation patterns of drilling engineering parameters, providing an operational basis for stuck pipe risk analysis. By analyzing the characteristic parameter variations across stuck pipe anomaly intervals and introducing a moving window mechanism, a computation method for stuck pipe risk coefficient was proposed. Combined with machine learning algorithms, a novel stuck pipe risk early warning model was developed. Comparative results of performance prediction show that the proposed improved random forest model achieves significant advantages in accuracy and lead time, with an average prediction accuracy of 86.61% and the ability to deliver warnings 6 to 10 minutes before a stuck pipe incident occurs. An intelligent stuck pipe risk early warning system was developed based on this methodology, offering technical and software support for field applications. The findings provide a new approach for early warning of stuck pipe risks and offer new insights into data-driven stuck pipe prediction models based on machine learning algorithms.