RESUMEN

Pipe sticking is a major problem that can result in significant downtime, lost productivity, and increased costs. The aim of this study is to develop an accurate and effective predictive model for pipe sticking due to wellbore uncleanliness using a range of classification algorithms. In this research work, the drilling data from two different reservoirs in India was pre-processed and eviscerated to ensure that it was suitable to process using classification algorithms. The data collected from two different reservoirs in India were analyzed using different machine learning algorithms to tackle the persistent challenges of pipe sticking during oil drilling operations. These algorithms were compared and evaluated based on their performance. The research finding indicates that the ensemble classifier algorithm performs better than a single classifier algorithm. It shows high generalization ability with an average accuracy of around 90 %. In addition to this, the ensemble classifier algorithm possesses good classification performance, and provides immunity from noisy data, offering strong support for real-time detection to prevent pipe sticking thereby reducing costly downtime.