Proposal

Developed or mature oil fields can be considered as sets of reservoir that present production strategy already established and implemented, generally presenting production in the decline phase. Changes in geologic characterization or economic or technologic scenarios can demand changes of the production strategy, requiring the application of an optimization process. The optimization process of developed or mature oil fields presents different characteristics from fields in beginning of production, mainly regarding flexibility for changes. The existence of production data allows the improvement of the geological characterization of the reservoir and the reduction of the uncertainties allowing a more detailed optimization procedure.

The objective of this work is to develop an automated optimization methodology combining the utilization of three important tools: traditional simulation, streamline simulation and quality map. The traditional simulation is used to give reliability to the process. The streamline simulation and quality map are supporting tools employed to give more reliability and to bring up insights about the optimization process and speedup to the process, mainly in the control of water injection and production and in the possible allocation of news wells.

The proposed methodology had been applied to an offshore field with water injection. The results show that the efficient control of fluid flow and the better understanding of the overall process provided by the supporting tools allow the determination of production strategy that improved the economic recovery of the developed or mature field studied. Additionally, the developed methodology allows the definition and the organization of the possible changes, making feasible the automation of part the process.

Introduction

The definition of a production strategy of oil fields comprises several study phases. In the beginning of the field life the data are scarce, what reflects in a great uncertainty and in the one adoption of an approach initial production strategy, without great refinement of the solution. After the choice of the initial strategy, appears the necessity of refinement since the uncertainties diminish in function of the biggest amount of data, what reflects in the best detailing of the field model to be simulated. In the third phase occurs the optimization of production strategy of the developed or mature oil fields. In this stage, the reservoir knowledge increases, and more geological are available, reducing the involved uncertainties in the optimization process.

The strategy optimization is a complex procedure that demands the analysis of the producertion and injector wells behavior to know which wells can be modified and the analysis of a great number of variable that influence the process, as for example, geological and fluids properties, production and pressure data. Developed and mature oil fields optimization presents minor flexibility because although the used models are more realistic, there are more restrictions of changes after the total or partial implementation of the initial strategy. Therefore with perforated wells and in operation, modifications must be made of different form to present economic viability. This complexity increases with the difficulty in to apply mathematical optimization methods in function of the great computational effort required in the modeling process. This difficulty becomes necessary the use of support tools as the streamline simulation and the quality map, that allow to define additional objective functions that classify the wells and determine which regions of the reservoir present mobile residual oil to be recovered.

The streamline simulation is used to study the fluid flow pattern in the field, determining the efficiency of injector wells and distribution of injection costs for producertion wells. The quality map is used to define regions of the reservoir with mobile residual oil, defining regions for perforation and completion of producertion wells.

The conventional simulation (based on finite difference) is used to do the main simulations to the tests that verify the use of the support tools. All the economic analysis is made based on conventional simulations output.

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