A Methodology To Reduce Uncertainty Constrained to Observed Data

Abstract

Summary This paper presents a new method to deal with uncertainty mitigation by using observed data, integrating the uncertainty analysis and the history-matching processes. The proposed methods are robust and easy to use, and offer an alternative to traditional history-matching methods. The main characteristic of the method is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The main objective is the integration of uncertainty analysis with history matching, providing a natural manner to make predictions under reduced uncertainty. Three methods are proposed: (1) probability redistribution, (2) elimination of attribute levels, and (3) redefinition of attribute values. To test the results of the proposed approach, we investigated three reservoir examples. The first one is a synthetic and simple case; the second one is a synthetic but realistic case; and the third one is a real reservoir from the Campos basin of Brazil. The results presented in the paper show that it is possible to conduct an integrated study of uncertainty analysis and history matching. The main contribution of this work is to present a practical way to increase the reliability of prediction through reservoir-simulation models that incorporate uncertainty analysis in the history period and provide reliable reservoir-simulation models for prediction forecast.