The closed-loop field development (CLFD) under geological uncertainties is a general methodology that aims to optimize an objective function by successively adjust the development plan (DP) of an oil field as new information is made available by drilling and production of new wells. In this work, we propose a CLFD procedure and demonstrate its feasibility in the UNISIM-I benchmark case, based on the Namorado Field of the Brazilian Campos Basin. The CLFD cyclically uses the steps of history matching, selection of representative models and optimization of the DP in an ensemble of uncertain models, elaborated with a limited amount of initial information. For the first step, we use an ensemble-based data assimilation method to match production data and well logs obtained from a reference model that reproduces the behavior of the real reservoir. The representative models are selected based on their similarity considering dynamics and statics parameters of the reservoir. For the DP optimization, we use a genetic algorithm with nonlinear constrains over the selected models. The net present value (NPV) of the project is the objective function used to evaluate the methodology. The comparison between the NPV of the reference model using the DP generated by the methodology and an initial DP is used to measures the gains. The results show a 40.8% increase in NPV when applying the methodology compared to the initial DP. Moreover, the proposed CLFD procedure is able to improve decision making by systematically increasing the NPV of the set of uncertain models.

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