Stuck pipe prediction from rare events in oil drilling operations

Stuck-pipe phenomena are relatively rare in drilling operations in the oil & gas industry, but can have disastrous economic consequences, causing costly time delays and sometimes even the loss of expensive machinery. In this work, we develop an event-based prediction model that relates the occurrence of precursor events to the stuckpipe phenomena. To this aim, the detectors of various types of precursor events that typically anticipate stuckpipe occurrences are first designed based on the available mudlog data. A Hidden Markov Model (HMM) is then developed to relate these precursor events to actual drilling problems, producing different levels of alarm, with the ultimate goal of predicting stuck pipes. The model has been tested on a dataset of wells with different characteristics, showing positive results.