Robust optimization of water-flooding in oil reservoirs using risk management tools

The theory of risk provides a systematic approach to handling uncertainty with
well-defined risk and deviation measures. As the model-based economic optimization of the water-flooding process in oil reservoirs suffers from high levels of uncertainty, the concepts from the theory of risk are highly relevant. In this paper, the main focus is to offer an asymmetric risk management, i.e., to maximize the lower tail (worst cases) of the economic objective function distribution without heavily compromising the upper tail (best cases). Worst-case
robust optimization and Conditional Value-at-Risk (CVaR) risk measures are considered with geological uncertainty to improve the worst case(s). Furthermore, a deviation measure, semivariance, is also used with both geological and economic uncertainty to maximize the lower tail. The geological uncertainty is characterized by an ensemble of geological model realizations and the economic uncertainty is defined by an ensemble of varying oil price scenarios.