TX 75083-3836, U.S.A., fax +1-972-952-9435. AbstractThe huge number of available data imposes the use of finest tools to be shared by several stakeholders avoiding time consumption and repetition of same tasks 1 . This paper presents the use of dynamic templates developed on well known concepts in the literature, other than those frequently used for reservoir and production monitoring. This study proposes few tools developed on different fields that have proven useful in the surveillance and monitoring phases as well as the further optimization activity.The first recommended template is a key performance tool applied to gas/oil field named 'Warning Index' (WI). This index accounts for several crucial components for a given well such as draw down, Water Cut (WC), Gas Oil Ratio (GOR). It offers a quick snapshot of well production efficiency and the most critical wells in the fields. It can be extremely helpful in the well performance tracking, in the identification of field areas and wells that require a deep depth analysis.In the optimization activity for two deep water oil and gas fields an analysis has been performed through the 'Dimensionless Productivity Index' 2 (J D ). A dynamic tool calculates this indicator for each well and compares it with the benchmark estimated for vertical no damaged well. The use of 'Dimensionless Productivity Index' template allows defining the level of damage, the effectiveness of stimulation activities, the minimum elapsed time among operations, the sudden changes in the well behaviour and performances.For an off shore gas field with high level of water production a dynamic template has been implemented in order to quickly define the critical gas rate for liquid loading. In this case the use of well head pressure regularly monitored day by day and PVT data coming from gas and water analysis allowed developing a dynamic plot that can track the critical gas rate and as a consequence define the best time for some interventions such as acid stimulation or nitrogen lifting.A further proposed template enables to evaluate the total drained area for an oil field with a huge number of producer wells. By tracking the variation of total drainage area is possible to define interference among wells, less drained area for infilling wells, effect of injection wells on producers, variation in time for a given well related to production conditions, damage or pressure support.

In recent years, Underground Gas Storage (UGS) has earned itself strategic importance as it guarantees energy sustainability in markets suffering from unpredictable supply. Storage management is a complex activity which faces the challenge of combining the variability of daily commercial client requests along with the capability of the reservoirs to deliver. Gas production and re-injection activity requires standard core competencies of the oil and gas industry, however the process is fast paced as compared to conventional hydrocarbon production activity as time scales from data analysis to decision making is reduced to hours. Stogit Spa, an Italian gas storage company managing 8 depleted gas fields in Italy has implemented an integrated system that dynamically links databases, visualization tools and reservoir/well simulators in order to assist the management process. This system called PERSEO (PErformance Reservoir StoragE Optimization) is aimed at enhancing efficiency by utilising a piece of intelligent fields application. The large amount of data from the 270+ wells of STOGIT equipped with SCADA systems monitoring real time gas rate and well head pressure, provides information for production/injection management. The challenge has been to extract the maximum information from the available database and computerize the process of updating well/reservoir performance automatically. This allows for faster monitoring, analysis and prediction of future well behaviour. A workflow was realised to transform available real time data into calibrated models in the following two steps:*Design an algorithm capable of filtering stabilised gas rates and wellhead pressures for every injection/production period to extract data representative of the well performance*Automatic updating of well performance (IPR/VLP) model and matching the algorithm filtered data with theoretical and practica 1 models (back pressure C, n equation). This paper describes the outline of the implemented PERSEO system, details of the computerized workflow along with sensitivities and the results obtained. Introduction The concept of "intelligent field" is a promising scenario for the future of oil and gas industry. What is more or less foreseen today (Gomersall 2007, Murray et al 2006, Unneland et al 2005) is the idea of creating a higher automation level in the management of a production asset. The increasing amount of available data, among which real-time subsurface sensors, and the trust in the potential of the "digital" revolution are probably the main drivers of this belief. However the kind of automation required is something really peculiar with the oil industry, where we should always admit that we don't really know what our main asset - the reservoir - looks like. The intrinsic impossibility to directly measure, to "see" the reservoir, brings the petroleum engineer/geologist in the continuous interpretation of indirect data in order to feed more or less complex models. Going from data to models and from those to decisions is the concretization of the value of the whole petroleum engineering activity. Decisions are fastened and enhanced by a value-chain able to define from data a limited number of clear scenarios.