Evaluation of Monte Carlo Methods for Assessing Uncertainty

Abstract

Summary Uncertainty in future reservoir performance is usually evaluated from the simulated performance of a small number of reservoir models. Unfortunately, most of the methods for generating reservoir models conditional to production data are known to create a distribution of realizations that is only approximately correct. In this paper, we evaluate the ability of the various sampling methods to correctly assess the uncertainty in reservoir predictions by comparing the distribution of realizations with a standard distribution from a Markov chain Monte Carlo method. The ensemble of realizations from five sampling algorithms for a synthetic, 1D, single-phase flow problem were compared in order to establish the best algorithm under controlled conditions. Five thousand realizations were generated from each of the approximate sampling algorithms. The distributions of realizations from the approximate methods were compared to the distributions from the exact methods. In general, the method of randomized maximum likelihood performed better than other approximate methods.