Oil well drilling activities recognition using a hierarchical classifier

Recognition of oil well drilling activities is a crucial task as it allows for identification of nonproductive time. Activity recognition is also important as it can be part of a complete oil well monitoring system. In this paper, we propose a novel hierarchical classifier that consists of Fuzzy Rule-based (FRB) and Random Forest (RF) Classifiers to recognize distinct oil well drilling activities in an oil well drilling process. The novel hierarchical classifier is designed by stacking the FRB and RF classifiers to achieve a classification of the numerous oil well drilling activities with high accuracy. The evaluation of the proposed method is illustrated through a case study of identifying the drilling activities in a real drilling data from oil well rig. Further recommended classifier generates an accurate report of time spent to execute the numerous drilling activities in a complete cycle of the oil well drilling process. Empirical evaluation of the real drilling data shows the efficiency and stability of the proposed method.