Model-based economic optimization of the water-flooding process in oil reservoirs suffers from high levels of uncertainty. The achievable economic objective is highly uncertain due to the varying economic conditions and the limited knowledge of the reservoir model parameters. For improving robustness, different approaches, e.g., mean or mean-variance optimization have been proposed. One of the drawbacks of the mean-variance approach is the symmetric nature of the variance and hence the reduction of the best cases. In this work, we focus only on the lower tail, i.e., the worst-case(s) and aims to maximize the lower tail of the economic objective function without heavily compromising the best cases. Concepts from robust optimization (max-min approach) and the theory of risk (a risk averse mean-CVaR approach) are considered to offer an asymmetric shaping of the objective function distribution with respect to the given uncertainty. A scenario-based approach is used, where an ensemble of oil price scenarios characterizes the economic uncertainty.