Decisions conserning development and depletion of petroleum reservoirs must be made under uncertainty in the decision supporting information. The incomplete knowledge of the reservoir charactistics contributes significantly to this uncertainty. In addition, and this is particularly true for improved oil recovery (IOR) projects, the uncertainty in the operational environment makes hard to confirm and substantiate research efforts.

To address these issues a multicriterion model is introduced. The model is coupled with a reservoir simulator and is used to estimate the applicability and efficiency of various field development strategies.

The multicriterion model used here applies the methods of interval theory and fuzzy set logic. It permits the integration of geophysical data, the potential efficiency of various IOR methods and other resource requirements. A pre-modelling multicriterion analysis is used to optimize field scale reservoir simulations for IOR methods. Particular field case scenarios show how the reservoir description information and IOR experience and knowledge can be used for decision making support. This work demonstrates that a first-order screening decision making system combined with a field scale reservoir model can be used to further optimize the recovery while reducing the costs and uncertainty in the decision making.

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