In an era of reduced profit margin and high market uncertainty, more than ever it is important to meet operational excellence as a key factor for business sustainability. This is common to most technical applications, but it is particularly true for the drilling operations, where considerable investments and associated risks are involved. During last four years, as part of its digital transformation process, Eni has equipped itself with several digital tools for the diagnosis and the monitoring of drilling and completion operations. Goals and reached benefits can be summarized in risk reduction, operational efficiency and performance optimization. Based on a wide case history started in 2019, a Digital Drilling Package was developed for operations support, from the design to the construction phase. Three main tools are now available to be applied to the most complex wells, either stand-alone or in parallel, covering drilling operations non-productive time (NPT) prediction, performance advanced analytics and real time simulations. This last simulation tool was deployed for the first time in late 2020 on some wells and is now being included in the engineering and operation workflows. Attacking operational NPT and invisible lost time with the aim to increase safety and to reach the technical limit is not only a matter of processing tools. It requires a deep integration with headquarter (HQ), geographical units and field locations, with the definition of a strong data management infrastructure. This paper describes Eni's experience both on-site and in office, showing how the portability and integration of big data systems, suitable data lake architectures and human factor synergies can create effectiveness at all levels. An Africa Offshore field case history is reported to show how predictive and data analytics modelling and tools interact. In addition, the way in which these tools have been managed to support optimum decision-making processes is highlighted. Next development steps will target an even higher level of integration of all available digital tools to have a single diagnostic approach based on univocal dashboards and in-house data server infrastructures.

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.