Pros and Cons of Applying Proxy-Models as a Substitute for Full Reservoir Simulations

Abstract

Abstract This paper presents a comparative study of proxy-modeling methodology (also known as surrogate modeling or metamodeling) as a computationally cheap alternative to full numerical simulation in assisted history matching, production optimization and forecasting. The study demonstrates the solution space complexity for different simulation models and the applicability of the proxy-models to mimic it. Focus is given to the practical aspects of model construction and to the limitations of which engineers should be aware. Results of stochastic optimization driven by full numerical simulation are compared to the proxy-model solutions in order to demonstrate strengths and weaknesses of each approach and determine desirable areas of application. Several simulation models of different complexity were used to demonstrate the impact of model structure, number of uncertainty parameters and type of problem on simulation model response and on efficiency of proxy-model application. The results are presented for different datasets, proxy-models and simulation model outputs to demonstrate the dependence of the approximation quality on these parameters. The dependence of the proxy-model prediction quality on sampling method, and on uncertainty domain complexity has been revealed. The efficiency of proxy-model application in history matching and production optimization was compared to stochastic optimization with full reservoir simulation. The results of this study have demonstrated that with increasing complexity of the solution space and number of uncertainties, the application of the proxy-modeling methodology is not recommended for history matching. In the history matching case, the use of full reservoir simulations, combined with stochastic search methods, is preferable and, above a certain level of complexity, the only acceptable solution. Nevertheless, proxy-modeling might be a good approach for certain production optimization projects and appropriate tool for forecasting of Hydrocarbons Initially In Place (HCIIP) and oil recovery (OR). This study suggests areas of application for proxy-models and full numerical simulation. It addresses pros and cons of both approaches in reservoir simulation and provides advice for their efficient application.