The objective of reservoir management is to maximize a key performance indicator (net present value in this study) at a minimum cost. A typical approach includes engineering analysis, followed by the economic value of the technical study. In general, operators are inclined to spend more effort on the engineering side to the detriment of the economic area, leading to unbalanced and occasionally suboptimal results. Moreover, most of the optimization methods used for production scheduling focus on a given recovery phase, or medium-term strategy, as opposed to an integrated solution that allocates resources from discovery to field abandonment. This thesis addresses the optimization of a reservoir under both technical and economic constraints. In particular, the method presented introduces a life cycle maximization approach to establish the best exploitation strategy throughout the life of the project. Deterministic studies are combined with stochastic modeling and risk analysis to assess decision making under uncertainty. To demonstrate the validity of the model, this document offers two case studies and the optimal times associated with each recovery phase. In contrast with traditional depletion strategies, where the optimization is done myopically by maximizing the net present value at each recovery phase, our results suggest that time is dramatically reduced when the net present value is optimized globally by maximizing the NPV for the life of the project. Furthermore, the sensitivity analysis proves that the original oil in place and non-engineering parameters such as the price of oil are the most influential variables. The case studies clearly show the greater economic efficiency of this life cycle approach, confirming the potential of this optimization technique for practical reservoir management. / text