Automatic Method for Anomaly Detection while Drilling

Summary A lot of anomalies can occur and lead to failures during drilling process. It is crucial to detect these deviations from normal process as soon as possible, so engineers can analyse and decide what activities to take in order to prevent potential NPT. In this work we propose a new machine learning based approach for detection abnormal drilling behaviour in an online manner. The idea is to cluster drilling data, which is preprocessed in a very special way. Our aproach allows using all available data for training as it does not need any labeled data and incorporates both raw drilling parameters and expert knowledge, thus enhancing prediction results.