A Change Point Detection Approach for Intelligent Real-Time Identification of Lost Circulation Events During Drilling Operations

Abstract

Lost circulation is a challenging aspect during drilling operations as uncontrolled flow of wellbore fluids into formation can expose rig personnel and environment to risks. Further, the time required to regain the circulation of drilling fluid typically results in unplanned Non-Productive Time (NPT) causing undesired amplified drilling cost. Thus, it is of primary importance to support drilling supervisors with accurate and effective detection tools for safe and economic drilling operations. In this framework, a novel lost circulation intelligent detection system is proposed which relies on the simultaneous identification of decreasing trends in the paddle mud flow-out and standpipe pressure signals, at constant mud flow-in rate. First, mud flow-out and standpipe pressure signals underlie cubic-spline-based smoothing step to remove background noise caused by the measurement instrument and the intrinsic variability of the drilling environment. To identify structural changes in the considered signals, a nonparametric kernel-based change point detection algorithm is employed. Finally, an alarm is raised if flow-out and standpipe pressure decreasing trends have been detected and their negative variations are below prefixed threshold values. The proposed intelligent lost circulation detection system has been verified with respect to historical field data recorded from several Eni wells located in different countries. Results show that the proposed system satisfactorily and reliably detects both partial and total lost circulation events. Further, its integration with already existing Eni NPT prediction models has led to a significant improvement in terms of events correctly triggered.