A fully compositional Integrated Asset Model (IAM) has been built for a giant gas-condensate field. The field is a complex retrograde gas-condensate reservoir with a hydrocarbon column up to 1750m in height. The fluid composition varies significantly with depth, ranging from a gas condensate to under-saturated oil. Production is centred on three processing facilities which are variously constrained by gas processing, gas compression & oil stabilisation capacities and overall export levels. There are some 100 producers and 15 gas injectors presently active in the field, with new wells and facilities planned as part of future development. IAM's for the total production system have been gaining in popularity for applications such as FEED studies, field development planning and optimisation. Their complexity has grown with the need to have fully compositional models, which are particularly important for gas condensate fields, where accurate fluid description is required for predicting condensate recovery and injection gas composition. Development of this IAM has required close cooperation between reservoir, production and process engineers since each of the component models - a 3D reservoir simulation model, production & injection surface network models and a process model for the three production units – are complex in their own right. The IAM model honours the well, network, and facilities constraints, taking into account interdependence between the different elements of the system. The IAM provides the capability to manage scheduled field events (well re-routing, plant maintenance, field uptime, etc) and optimizing field liquid production. This work offers valuable insights for more accurate assessments while evaluating different field exploitation strategies.

Daily field surveillance is of mandatory importance because it allows tackling production issues, improving reservoir knowledge and then to maximize recovery. However, some practical challenges of data handling increase time consumption and reduce the efficiency and the appeal of routine reservoir surveillance. In addition, high frequency information opens the possibility to control and optimize field production in real-time but they cannot be effectively managed by ad-hoc spreadsheet solutions and require a more integrated approach. This paper is focused on the lessons learnt from the eni experience in developing and applying intelligent automated surveillance system in several fields worldwide, reviewing the main challenges to address for a successful initiative. In essence, the eni automated surveillance system consists in a technology framework to orchestrate five key components: data, processes, workflow automation, smart surveillance and people. It is highly focused on savings time and efforts in routine reservoir activities while improving quality of analysis. Through automated field surveillance it has been possible to standardize analysis of well and field behaviour by transforming crude data into valuable information in an automated fashion. This methodology is gaining more acceptance and popularity by different disciplines. It has been deployed in several eni complex fields, ranging from fractured carbonate oil reservoir to high viscosity oil field, developed with a horizontal injector/producer pattern. This system brings the possibility to monitor and analyze well operations for production optimization, create a complete set of alarms for monitoring critical well parameters, running automated and sending on time notifications. The automated surveillance is highly focused on savings time and efforts in routine activities. It improves quality of analysis of well and field behaviour by automatically transforming raw data into valuable information. The impact of the system will be discussed with examples of tangible improvements in performances. The main component to a successful implementation is the proper synergy among the IT infrastructure, the engineering expertise, the right technology and finally the right people and organizational commitment. Introduction In the last years, sensors, gauges and meters have been installed on well heads and downhole/surface equipments in order to collect different kind of measurements at high frequency. The resulting wealth of information may promote an improved awareness of wells and reservoir, moving beyond a reactive approach towards advanced alerting and controlling process automation. The integration of the various digital surface and subsurface technologies in the E&P processes is being continuously improved over the last years and it has been given different names as Smart Fields [Kapteijn, 2002; Bogart 2004; Potters, 2005], Intelligent Field [Sengul, 2002; Silva, 2005; Al-Dossary, 2009], Integrated Field [Serbini, 2009] and Integrated Operations [Landgren, 2006; Ringstad, 2007]. Generally speaking, Integrated Operations can be defined as new work processes which uses data to improve the collaboration between disciplines to achieve safer, better and faster decisions. Although Integrated Operations spans all the activities in an asset (e.g. from drilling to maintenance), this paper is focused on how these new processes can improve field/well surveillance activities, stressing the benefits that can be expected from the point of view of the petroleum/reservoir engineering. The first section is dedicated to a brief description of general features of Automated Surveillance projects, also called "Integrated Surveillance System" or ISS in this paper. The sections from two to four report the experiences of Reservoir teams in three Eni assets where the ISS has been deployed. In the last section, conclusions will be discussed.