Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process.
This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty.
Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.