A Data-Based Approach for the Prediction of Stuck-Pipe Events in Oil Drilling Operations

Abstract

Abstract Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that may range from time delays to loss of expensive machinery. In this work, we develop three indicators based on the mudlog data, which aim to detect three different physical phenomena associated to the insurgence of a sticking. In particular, two indices target respectively the detection of translational and rotational motion issues, while the third index concerns the wellbore pressure. A statistical model that relates these features with the documented stuck-pipe events is then developed using machine learning. The resulting model takes the form of a depth-based map of the risk of incurring into a stuck-pipe, updated in real time. Preliminary experimental results on the available dataset indicate that the use of the proposed model and indicators can help mitigate the stuck-pipe issue.