A. Abdullah M

Machine-learning-based prediction of pipe-sticking events in petroleum drilling operations using domain-informed features and class-balanced ensembles

A machine-learning framework for binary classification of pipe-sticking events using a drilling dataset containing 297 observations and 13 original drilling-related variables is developed and evaluated, representing a preliminary data-driven baseline for pipe-sticking classification rather than a field-validated deploy...