Although modern reservoir management requires integrated production optimization for the productioninjection operation systems (PIOS), a variety of methods have been applied to implement "local" rather than "global" optimization. There have been no systematic methods to optimize the PIOS by adjusting the injection and/or production rates at a field-scale level. This paper presents an efficient global optimization technique to adjust the PIOS for hydrocarbon reservoirs. A generalized production performance model is developed for flowing and artificial lift methods and has been applied to more than forty oil fields. This generalized model can be coupled with reservoir models to implement optimum control of field-wide production and injection rates at different development stages. Simulated annealing algorithm is employed to optimize the PIOS by integrating a reservoir model with performance models which consider multiple injectors and producers. Such an integrated technique can optimize the PIOS in a fixed well-group and/or a field under different constraints. It has been applied to a water-alternating-gas miscible flooding reservoir over five years. The field performance shows that the reservoir pressure is maintained above the minimum miscible pressure and that the injection and production performance is kept in an optimum range. In addition, the field water-cut remains at zero water-cut stage and the gas-oil-ratio is slightly higher than the original value. The injection and production rates are properly adjusted in the field operations so that the reservoir life is extended and the oil recovery is improved.


Integrated reservoir management has been increasingly applied to maximize economical recovery of oil and gas (1–3). Although the production-injection operation systems (PIOS), which consists of producers, reservoirs, injectors and surface facilities, is the key knot of the integrated reservoir management, each component is usually considered individually in both design and operations. Physically, each engineering function models and optimizes its component of the systems on the basis of "local" other than "global" criteria (4). After an oil field is put into production, it will be transformed from a static system into a dynamic one. Thus there is a need to execute global optimization for the PIOS by dynamically integrating all the components in the whole system.

The optimum control of fluid movement in a reservoir is the focus and challenge in the development of petroleum resources (2,3,5,6). From the viewpoint of cybernetics, the underground reservoir is a system that can be controlled properly. The injected fluids, including gas and water, are considered as the input, whereas the produced fluids are treated as the output of the system. Furthermore, it is a challenging task to solve fieldoperating problems and develop the field-wide production potential by integrating the injection and production with reservoir performance (7). In practice, the injection and production rates can be adjusted in individual wells for the PIOS, though field development is essentially a dynamic process. So far there have been no systematic methods to optimize the PIOS by adjusting the injection and/or production rates at a fieldscale level.

Economic optimization of the PIOS is an ultimate goal of the reservoir management.

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