Evaluation of Gaussian approximations for data assimilation in reservoir models
In this paper we propose to numerically assess the performance of standard Gaussian approximations to probe the posterior distribution that arises from Bayesian data assimilation in petroleum reservoirs. In particular we assess the performance of (i) the linearization around the maximum a posterior estimate, (ii) the randomized maximum likelihood and (iii) standard ensemble Kalman filter-type methods. In order to fully resolve the posterior distribution we implement a state-of-the art MCMC method that scales well with respect to the dimension of the parameter space. Our implementation of the MCMC method provides the gold standard against which to assess the aforementioned Gaussian approximations. We present numerical synthetic experiments where we quantify the capability of each of the {\em ad hoc} Gaussian approximation in reproducing the mean and the variance of the posterior distribution (characterized via MCMC) associated to a data assimilation problem. The main objective of our controlled experiments is to exhibit the substantial discrepancies of the approximation properties of standard {\em ad hoc} Gaussian approximations. Numerical investigations of the type we present here will lead to greater understanding of the cost-efficient, but {\em ad hoc}, Bayesian techniques used for data assimilation in petroleum reservoirs, and hence ultimately to improved techniques with more accurate uncertainty quantification.