Clustering aggregation model for statistical forecasting of multiphase flow problems

Reservoir simulations often require statistical predictions to quantify production uncertainty or assess potential risks. Most existing uncertainty quantification procedures aim to decompose the input random field into independent random variables if the correlation scale is small compared to the domain size. In this work, we develop a K-means-based aggregation model, for efficiently estimating multiphase flow performance in multiple geological realizations. This approach performs a number of single-phase flow simulations and uses K-means clustering to select only a few representatives on which multiphase flow simulations are performed. In addition, an empirical model is then employed to describe the relationship between the single-phase solution and the multiphase solution using these representatives. Finally, the multiphase solution in all realizations can be easily predicted using empirical models. The method is applicable to both 2D and 3D synthetic models and has been shown to perform well in the trusted interval of productivity, and probability distribution as indicated by the cumulative density function. It is able to capture a large number of ensemble statistical realizations of Monte Carlo simulation results with significantly reduced computational cost.