Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model

Stuck pipe phenomena can have disastrous effects on drilling performance, with outcomes that range from time delays to loss of expensive machinery. In this work, we provide three methods for the prediction stuck pipe. The first method targets detection friction coefficient which represent trend second probability using ANN (artificial neural network). last model establishes a comprehensive indicator based and fuzzy mathematics give more accurate results show best is one predict events F1 0.98 FAR (false alarm rate) 1%. Preliminary experimental available dataset indicate use proposed help mitigate issue.