Uncertainty Quantification of Reservoir Performances Using Streamline Based Inversion and Distance Based Method

Abstract

For decision makings, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since an initial model constructed with limited data has high uncertainty, it is essential to integrate both static and dynamic data for reliable prediction. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching. Multiple realizations of reservoir models should be history matched. In addition, history matching is mathematically a highly ill-posed problem. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline based inversion and distance based method. First, a distance between each model is defined as the norm of differences in generalized travel time vectors. Second, they are grouped according to distances and representative models are selected instead of matching all models. Third, generalized travel time inversion is applied for integration of dynamic data and a streamline simulator is adopted as a forward simulator to take advantage of computational efficiency. It is verified that the proposed method gathers models with similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances fairly well, while reducing the amount of calculations significantly by using the representative models.