A Simultaneous Bayesian Estimation of Channelized Facies and Reservoir Properties under Prior Uncertainty

Zhao, Yu (University of Tulsa) | Forouzanfar, Fahim (University of Tulsa)

OnePetro 

Abstract

In this work, a Bayesian data assimilation methodology for simultaneous estimation of channelized facies and petrophysical properties (e.g., permeability fields) is explored. Based on the work of Zhao et al. (2016a,b), common basis DCT is used for the parameterization of facies fields in order to achieve model feature extraction and reduce the inverse problem dimensionality. An iterative ensemble smoother method along with a post-processing technique are employed to simultaneously update the parameterized facies model, i.e., DCT coefficients, and the permeability values within each facies in order to match the reservoir production data. Two synthetic examples are designed and investigated to evaluate the performance of the proposed history matching workflow under different types of prior uncertainty. One example is a 2D three-facies reservoir with sinuous channels and the other example involves a 3D three-facies five-layer reservoir with two different geological zones. The computational results indicate that the posterior realizations calibrated by the proposed workflow are able to correctly estimate the key geological features and permeability distributions of the true model with good data match results. It is known that the reliability of prior models is essential in solving dynamic inverse problems for subsurface characterization. However, the prior realizations are usually obtained using data from various sources with different level of uncertainty which results in great challenges in the history matching process. Thus in this paper, we investigate several particular cases regarding different prior uncertainties which include fluvial channels conditioned to uncertain hard data information or generated by diverse geological continuity models. The proposed methodology presents desirable robustness against these prior uncertainties that occur frequently in the practical applications.