In the development of oil fields, many parameters have to be selected in optimization procedures, demanding large computational effort, especially when uncertainties are considered. Therefore, it is common to separate design variables (G1) and well control variables (G2) optimization in a hierarchical process, but this may yield suboptimal results. We propose a well control analysis under uncertainties to verify whether G2 can be optimized separately in the development phase of petroleum fields. We also investigate the impact of G2 optimization on economic returns when platforms limit the production and for more pessimistic production costs. We perform reactive and proactive G2 optimization under uncertainties on a Benchmark Model (UNISIM-I-D, based on Namorado field) in three cases. In Case I, we use previously optimized strategies for G1, while G2 were controlled in a simplified way considering geological and economic uncertainties. In Case II we investigate the G2 optimization based on expected monetary value (EMV) for a restricted platform. Case III adopts the same restriction of Case II, but considers a pessimistic economic scenario. We consider five procedures for G2 optimization: (P1) longterm bottom-hole pressure control; (P2) short-term rates control; (P4) long-term rates control; (P4) well shut-in time control; and (P5) a combination of these procedures. In Case I, the EMV low percentage increase after G2 optimization indicates that a hierarchical process can be used in similar problems. Yet, G2 should be optimized during the lifetime of the field to increase EMV without additional cost. Depending on the representative model (RM) that best characterizes the field, there are potential further gains of almost 200 million USD for one of the scenarios studied. We achieved an increase of EMV around 8% for Case II. This suggests that G2 have a greater economic return when the platform restricts the production and injection for the field and G1 were not previously optimized. Case III showed even higher EMV gain (34%) indicating the importance of considering the economic scenario when defining the G2 strategy for the field. To reduce the search space in optimization problems, conventional techniques focus on optimizing design variables, underestimating the influence of well control management on G1. Moreover, some works do not include uncertainties and operational constraints. The results of this work indicate that G1 and G2 can be optimized hierarchically for a situation in which platforms do not constraint production or injection (Case I), since G2 variables have lower influence. This conclusion is not valid for restricted platforms and a pessimistic economic scenario (as in Case II and III) where G2 have higher influence in the results.

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