A Strategy for Rapid Quantification of Uncertainty in Reservoir Performance Prediction

Abstract

This paper will describe a strategy for rapid quantification of uncertainty in reservoir performance prediction. The strategy is based on a combination of streamline and conventional finite difference simulators.Our uncertainty framework uses the Neighbourhood Approximation algorithm to generate an ensemble of history match models, and has been described previously. A speedup in generating the misfit surface is essential since effective quantification of uncertainty can require thousands of reservoir model runs.Our speedup strategy for quantifying uncertainty in performance prediction involves using an approximate streamline simulator to rapidly explore the parameter space to identify good history matching regions, and to generate an approximate misfit surface. We then switch to a conventional, finite difference simulator, and selectively explore the identified parameter space regions.This paper will show results from a parallel version of the Neighbourhood Approximation algorithm on a Linux cluster, demonstrating the advantages of perfect parallelism. We show how it is possible to sample from the posterior probability distribution both to assess accuracy of the approximate misfit surface, and also to generate automatic history match models.