Automated system for drilling operations classification using statistical features

Abstract

Operations classification is one of the most needed tasks in the oil & gas industry. It provides the engineers with detailed information about what is happening on the rig site. In this paper we propose an approach to classify drilling operations automatically using machine learning techniques. This approach takes as input the sensors data in a specific time range, and predicts the drilling operation. Our approach is simple but effective, where for each sensor data (channel) a list of statistical features will be extracted, then features selection algorithms will be used to select the most informative features, and finally, a classifier will be trained based on these features. In this paper many feature weighting and selection algorithms were tested to find which statistical measures clearly distinguish between many different rig operations. In addition, many classification techniques were employed to find the best one in terms of accuracy and speed. Experimental evaluation with real data, from four different drilling scenarios, shows that our approach has the ability to extract and select the best features and build accurate classifiers. The performance of the classifiers was evaluated by using the cross-validation method.