fax 01-972-952-9435. AbstractThis paper describes an implementation of method to optimize the production in intelligent wells varying the wells inflow control valves settings using an optimization algorithm coupled to commercial flow simulators. The optimization is based on direct search methods. The optimization algorithm was coupled with two different commercial flow simulators and has been applied in two real Brazilian offshore fields to quantify the benefits of intelligent wells over a base case with conventional completion. The first field has three horizontal wells, two producers and one water injector, completed in two zones totalizing six inflow control devices. In this case, different scenarios have been analyzed varying the downhole valves type -on-off and multi-position. The results have shown that the intelligent wells scenarios increased the recovery factor and reduced the production and injection of water when compared with the base case (conventional completion). The second field has fifteen wells -nine producers with binary valves and six water injectors with sixposition valves -producing and injecting in two or three zone totalizing 39 downhole valves to be optimized. In this case, the results have shown a significant increase of the expected cumulative oil production when compared with the base case.

This paper presents an innovative upscaling methodology based on the semi-analytical simulator developed in the first part. The methodology generates a coarse grid based on strearntubes and isobars, whose upscaled properties are ac~tely calculated using the properties of each streamtube that Constihltes the coarse block. Beyond the calculation of npsca]ed static properties of the grid, the methodology also calculates upscaled relative permeability cumes, or pseudofunctions, using the semi-analytical streamtube method to perform this without requiring any significant additional time. Tests showed that the results from the streamtube coarse grid had an excellent agreement with the fine grid solution. In contrast, simulations with a coarse grid upscaled with conventional techniques failed in many situations. Although this upscaling methodology is heavily based on a fixed streamtube simulation method, it provided good results even for situations in which this streamtube simulator is not supposed to work, such as displacements with favorable mobility ratios, and for problems with gravity and compressibility.

This paper presents an innovative upscaling methodology based on the semi-analytical simulator developed in the first part. The methodology generates a coarse grid based on streamtubes and isobars, whose upscaled properties are accurately calculated using the properties of each streamtube that constitutes the coarse block. Beyond the calculation of upscaled static properties of the grid, the methodology also calculates upscaled relative permeability curves, or pseudoflinctions, using the semi-analytical streamtube method to perform this without requiring any significant additional time. Tests showed that the results from the streamtube coarse grid had an excellent agreement with the fine grid solution. In contrast, simulations with a coarse grid upscaled with conventional techniques failed in many situations. Although this upscaling methodology is heavily based on a fixed streamtube simulaflon method, it provided good results even for situations in which this streamtube simulator is not supposed to work, such as displacements with favorable mobility ratios, and for problems with gravity and compressibility. P. 361

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents the results of a study of the use of 4D seismic data in automatic history match. The study made use of commercially available tool, SIMOPT and ECLIPSE HUTS from Schlumberger, and it utilized a simulation model based in a real offshore field. The main goal of the study was to test the feasibility of the use of 4D seismic data in the history match workflow, and also, if any gain would come from the use of this kind of data compared with the traditional well production data.Production and seismic data were generated with a simulation model, then this model was modified and the softwares SIMOPT and ECLIPSE HUTS were used to fit the data generated in the previous step.The seismic data improved the workflow of the history matching process, especially by helping to choose which region of the model should be modified in order to achieve the match.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents an advanced risk analysis approach that has been applied on a Brazilian field case to manage and quantify the reservoir uncertainties during production forecasting and production scheme optimization. The oil field (named PBR) is located offshore Brazil. The PBR reservoir consists of a complex lithology, including mainly turbiditic sandstones interbedded by shales and marls. The workflow of the PBR reservoir modeling from the geostatistical lithofacies model, built using the non-stationary truncated gaussian method, to the flow model (about 24000 active cells) has been implemented in a fully integrated chain. The field was considered in this hypothetical study at its appraisal stage. Main challenge consisted in selecting an optimal water injection scheme to maximize oil production while minimizing water production and maintaining pressure over the 15 years of production, taking into account the overall major sources of uncertainties in the reservoir. Advanced risk analysis methods were used with the following objectives: -Determine the impact of the main uncertainties on the field production forecasts versus time and versus geographical locations of production and injection wells -Optimize the water injection phase to maintain pressure over time, while producing maximum oil and minimum water in a 15-years period.-Quantify the impact of main uncontrollable reservoir uncertainties on production forecasts and fluid distribution maps versus time for the optimal water injection program selected in order to identify potential risks with non-drained zones and pressure drops. At each step, either classical or optimal experimental designs were used to be able to perform an accurate analysis at a minimum cost in terms of reservoir simulation and interpretation. Results show that this innovative methodology can be successfully applied on complex real cases to quantify the risk associated with the main reservoir uncertainties during production forecasts and to optimize the water injection. In particular, this example illustrates how some innovative interpretation tools can be combined with experimental design methodology to perform advanced risk analysis for reservoir management.