Generating multiple history-matched reservoir models by stochastic sampling to quantify the uncertainty in oil recovery predictions has recently aroused interest in the industry. Coupling a stochastic sampling algorithm with a Bayesian analysis potentially allows incorporation of all sources of uncertainties including data, simulation and interpolation errors. However, the accuracy of the uncertainty estimations strongly depends on the sampling performance. In order to improve the robustness of the coupled Bayesian methodology, the factors that affect the accuracy of the estimations must be examined. This paper investigates how different sampling strategies affect the estimation of uncertainty in prediction of reservoir production. The sampling strategy involves the choice of algorithm and selection of algorithm parameters in sampling the high-dimensional parameter space. We present examples of using both the Neighbourhood Algorithm (NA) and a Genetic Algorithm (GA) to generate history-matched reservoir models for a real field case from the North Sea.
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