Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Abstract

Optimal well placement is critical to oil and gas field development. Typical workflows involve procedures to place a new well or a group of new wells in a reservoir in order to maximize some pre-defined reservoir performance metric. However, there are two main drawbacks with these traditional optimization approaches: First, the impact of geological uncertainty is often neglected or there may be no framework to include geological uncertainty. Second, traditional optimization techniques normally cannot meet the requirement of optimizing two or more conflicting objectives simultaneously—this may be useful when maximizing oil recovery while also minimizing water production. Consequently, in recent years, multiple objective optimization to obtain robust solutions that minimize the decision risk under geological uncertainty becomes a topic of renewed interest. Therefore, in this work, we develop a new work flow for well placement optimization while considering geological uncertainty in reservoir models. In general, when considering geological uncertainty, the primary goal is to maximize the mean net present value (NPV) over all realizations. However, restricting the search to simply maximizing the mean NPV may be inappropriate or inadequate for decision-making. A more reasonable choice is to maximize the mean NPV while minimizing the spread of the optimal NPV’s obtained for each realization. Therefore, in this work, we apply multi-objective optimization techniques to maximize the mean and minimize the variance of NPV values over all geological realizations to provide robust well placement solutions for decision-makers to select according to their risk attitude towards field development plans.