Planning the position of a new well is an essential task to increase hydrocarbon production. However, trying to capture the uncertainty related to the geological properties, an ensemble of geological realizations of the models must be considered. The objective can be to identify the locations of the new wells that maximize the average oil recovery or Net Present Value (NPV), for an ensemble of geological models. Also, considering an ensemble of geological models, an objective can be to minimize the standard deviation of the NPV (or cumulative oil recovery). Reducing the uncertainty on the NPV leads to reduce the risk for the placement of the new wells. These two objectives can be defined with a "mean-standard deviation" formulation of the objective function. Following the weight given to each objective, the placement of the wells is not the same. To know all the well-placement possibilities and associated risk, many optimization problems must be done. Such as procedures, require the use of optimization method and reservoirs simulations on each reservoir model. This optimization problem leads to a high number of reservoir simulation to determine the appropriate location and can be expensive in computation time. To reduce the computation time, a method consists in to substitute the reservoir simulator by a meta-model. In this paper, the use of an artificial neural network as a proxy model is considered. The objective is to propose a method to speed up the well-placement optimization process using a proxy model based on an artificial intelligence technique to replace the reservoir simulator. The proposed methodology considers an ensemble of realization and aims to provide an overview of solutions in an economic point of view inspired by portfolio optimization problems. An application of the methodology is presented for the Brugge field.

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