The first hydraulically operated completion was installed in Australia in 2004 (Guatelli et al 2004). Since then, a number of intelligent completions have been installed in offshore Australia. The remoteness of offshore Australia, particularly in the Timor Sea area, means intervention vessels are not readily available and well interventions are costly operations. For this reason, intelligent completion is considered to be an attractive alternative, by providing a down-hole solution to actively manage the reservoir production life and delay potential water breakthrough. The Kitan oil field is remotely located in the Joint Petroleum Development Area (JPDA) between East Timor and Australia. The Kitan oil field production facilities consist of three vertical producing wells, subsea flowlines, risers, and one Floating Production Storage and Offloading (FPSO) facility. The wells were completed with an intelligent design and cleaned up using a rig before the FPSO arrived on location. The intelligent completion design consists of two multi-stage hydraulic down-hole Flow Control Valves (FCVs) and three Down-Hole Gauges (DHGs) to independently control and monitor two different production zones. The FCVs have a total of 8 positions (fully opened, fully closed and 6 intermediate choke positions). It is planned to close the lower FCV to shut off water production from the lower zone while the upper FCV remains fully opened over the field life. The different FCV choke positions were utilized during the field startup and during the early stages of production while the water cut was still low, to overcome unforeseen technical limitations in the production system, and to optimize hydrocarbon production. This paper describes various aspects of the Kitan oil field intelligent well completion from design through installation and field startup to early stage of production operations, and includes technical problems encountered during the field startup as well as lessons learnt.

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Over the last 3 years, Eni has developed an integrated platform to gather subsurface data and make them available to the final users across the company. The Platform is structured in four data domains including the well data, which is the focus of this abstract. In the data model, the well master and architectural data assume paramount importance since they are upstream of the value chain and represent the aggregator of all the data recorded in the well. A large amount of data coming from all Eni Affiliates and operative sites is produced daily. To gather, perform Quality Controls and ingest them, Eni has implemented a governed workflow to ensure data is made available to the final users through the platform in an efficient and transparent way. The workflow aims to ingest the well master and well geometrical data directly from the well site defining roles all along the data transmission chain, with the ultimate objective to ensure the proper data quality once they reach the cross-functional subsurface data platform. It is no less important the timely availability of such data to guarantee a prompt association with geological and drilling log data. Given the increased amount of data acquired from disparate data sources, different functions and locations, Business Intelligence tools have been designed to monitor the workflow combining data for an easier data insight. To manage such complex network, dedicated dashboards allow all the users involved to visualize the status of the processes through specific KPIs, thus optimizing communication and reducing the human effort required. The capability to manage, validate and quickly interpret data will determine the competitive advantage among Energy Companies in the next future. Eni targets to excel in the everchanging business environment leveraging the new century asset: the data. The presented approach, combined with the diffused data culture initiatives, promotes a collaborative environment, and increase awareness on data importance across the value chain.

The evolution of the energy market requires companies to increase their operating efficiency, leveraging on collaborative environment and existing assets, including Data. A new focus on data governance and integration is needed to maximize the value of data and ensure "real-time" efficient response. The decoupling of data from applications enables organization by domain and data type in one cross-functional data hub. This scheme is independent from the scope of the activity and will therefore maintain its validity when dealing with new business requiring subsurface data utilization. The integrated data platform will feed advanced digital tools capable to control the risks, optimize performance and reduce emissions associated with the operations. Eni is putting this idea into practice with a new data infrastructure which is integrated across all the subsurface disciplines (G&G, Exploration, Upstream Laboratories, Reservoir and Well Operations departments). In this paper, the example of real time data exploitation will be discussed. Real time data workflow was first established in well operations for operational supervision and later developed for real time performance optimization, through the introduction of predictive analytics. Its latest evolution in the broader subsurface domain encompasses the application of AI to operations geology processes and the extension to all operated activities. This approach will equally support new company goals, such as decarbonization, increasing performance of subsurface activities related to underground storage of CO2 in depleted reservoirs.

In this paper, we introduce a new technology permanently installed on the well completion and addressed to a real time reservoir fluid mapping through time-lapse electric/electromagnetic tomography while producing and/or injecting. Our technology consists of electrodes and coils installed on the casing/liner in the borehole/reservoir section of the well. We measure the variations of the electromagnetic fields caused by changes of the fluid distribution in a wide range of distances from the well, from few meters up to hundreds meters. The data acquired by our technology are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a Machine Learning platform equipped with a complete suite of classification/prediction algorithms. Every time new data are acquired, they are fully integrated with the previous database, and used for decreasing the level of uncertainty about the dynamic model of the reservoir. In order to clarify the potential impact of such system on reservoir management, we apply this methodology to a synthetic data set. We discuss a simulation of a scenario where the waterfront approaches the wells during oil production. The goal of our test is to show how to combine our technology with Machine Learning to make robust predictions about the water table variations around the production wells.