Although several studies have shown that life-cycle well control strategies can significantly improve a field's economic return, the industry often relies on short-term strategies. One drawback of traditional parameterization, adopted for well control life-cycle numerical optimization, is that it often generates control strategies that yield impractical abrupt changes in production curves. Another issue, especially in cases with a large number of decision variables, is the local optima convergence related to the non-convex optimization problems. In this context, we proposed and compared four life-cycle well control parameterizations to maximize the net present value (NPV) of the field under uncertainties, which are able to mitigate both the above-mentioned problems. The first parameterization optimizes the apportionment of well rates at the beginning of the field management and well shut-in time. The other three are based on optimizing the coefficients of parametric equations (first-and second-order polynomials, and logistic equation) to guide the bottom-hole pressure (BHP) over time. We executed each parameterization five times in a deterministic reservoir scenario and compared them with well control short-term strategy that prioritizes production in wells with higher oil-water ratio and that aimed to replicate the general industry practice. In this strategy, the wells’ priority rank was updated at every 30-simulation days. Subsequently, the best parameterization was used to select the well control life-cycle strategy under reservoir uncertainties and this strategy was applied to the reference model representing a real reservoir. The results showed that all the proposed parametrizations significantly improved the NPV in comparison to the well control short-term strategy, while simultaneously ensuring a smooth well production curve. The logistic equation presented the best result among all parameterizations, as it delivered both the highest average of NPV and the smallest dispersion over the five experiment repetitions. This parameterization also produced similar results when applied under uncertainties and for the reference model. These results endorse the importance of not only relying on a short-term strategy, but also planning it for the life-cycle.