Agradecimentos: The authors would like to thank UNISIM, DE-FEM, CEPETRO, PETROBRAS, STATOIL, ANP, and Foundation CMG for supporting this work, and CMG for software licenses

In oil industry, decisions related to field development take into consideration scenarios that involve many uncertainties and high investments. Thus, a comprehensive decision analysis process is necessary to select the production strategy that maximizes field performance considering these...

In oil industry, decisions related to field development take into consideration scenarios that involve many uncertainties and high investments. Thus, a comprehensive decision analysis process is necessary to select the production strategy that maximizes field performance considering these uncertainties. For the selection of a production strategy, two main groups of optimization variables may be considered: design and control variables. The design variables relate to the development of the field, and cannot be altered after the implementation of the strategy (e.g. capacity of the platform, number and position of wells). On the contrary, control variables relate to the management of the field, and can be altered daily by the companies (e.g. production and injection rates). However, even with a robust production strategy selection process, unexpected or undesirable events can occur and decrease the economic efficiency of the project. The objective of this work is to evaluate the use of control variables when undesirable events occur after the implementation of a production strategy, to improve the economic performance of the project. Two production strategies are used: one that uses only water flooding and other one prepared for polymer flooding. The simulation model is based on a heterogeneous heavy oil offshore field. Two approaches (undesirable events) are considered in this work: (1) polymer degradation and (2) a geologic model that is different than the expected one. The results show that the economic performance can be improved greatly by simply adjusting the control variables for the described situations. Moreover, the gain in the polymer flooding case is higher than for water flooding, because of the higher number of optimization variables, such as polymer solution concentration and slug size, giving more flexibility to this kind of project

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