Short-term production optimization relying on model-based predictions over a short period (weeks to months) requires the use a near-borehole reservoir model. Such a model is usually developed and validated through standard well testing. Standard well testing has to be repeated periodically, with related loss of production. Production losses may be reduced by prolonging the interval between tests, but that may compromise the quality of information about reservoir properties, such as skin (or productivity index), which would ultimately compromise production as well. Therefore, a need exists for a methodology that maximizes both reservoir information and production simultaneously. Because these two tasks are inherently contradictory, a compromise has to be found. In this work we propose a methodology that combines well testing and production in an optimal way, resulting in overall production optimization. This methodology relies on a short-term moving-horizon optimization of an objective function that includes terms referring both to the quality of reservoir information and to production net present value. Reservoir information is captured by empirical (proxy) models that are built adaptively on-line as a result of optimal perturbations of production rates and recording of dynamic responses of related bottomhole pressures. Besides, the entire workflow can be automated. Simulations are presented that illustrate the mechanics and value of the proposed methodology.

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