Mixture Models for Sampling Conditional Facies Realizations from Multiple Training Images

Abstract

Multiple point statistics (MPS) provides a systematic approach for pattern-based simulation of geologic objects from a conceptual training image (TI). The TI encodes the higher-order spatial statistics of the expected connectivity structures through stationary patterns representing the underlying geologic features. The pattern-imitating nature of MPS simulation implies that the simulated facies inherit the spatial structure of the general features in the TI. This property makes the MPS approach very sensitive to uncertainty in the prior TI. Since TIs are constructed using uncertain data and imperfect assumptions, multiple TIs may be necessary to account for the uncertainty and full range of structural variability in facies descriptions. We present a Bayesian mixture modeling approach for adaptively sampling conditional facies from multiple uncertain TIs using a probability conditioning method (PCM). Using the PCM, we invert the flow data to obtain a facies probability map for drawing conditional facies realizations from each TI. The number of samples drawn from each TI is proportional to the weight assigned to them. The TI weights are assigned based on the predictive performance of its corresponding conditional facies realizations. We demonstrate the suitability of the proposed method using numerical experiments in fluvial formations.