A hybrid multiple classifier system for recognizing usual and unusual drilling events

Abstract

Up to very recently, the applications of machine learning in the oil & gas industry were limited to using a single machine learning technique to solve problems in-hand. As the complexity of the demanded tasks being increased, the single techniques proved insufficient. This gave rise to intelligent systems that are hybrids of several machine learning techniques to solve the most challenging problems. In this paper we propose a hybrid multiple classifier approach for recognizing usual and unusual drilling events. We suggest using two different information sources namely: (1) real time data collected by sensors located around the drilling rig, and (2) daily morning reports written by drilling personnel to describe the drilling process. Text mining techniques were used to analysis the daily morning reports and to extract textual features that include keywords and phrases, whereas data mining techniques were used to analysis the sensors data and extracting statistical features. Three base classifiers were trained and combined in one ensemble to obtain better predictive performance. Experimental evaluation with real data and reports shows that the ensemble outperforms the base classifiers in every experiment, and the average classification accuracy is about 90% for usual events, and about 75% for unusual events.