A method for reduction of uncertainties in reservoir model using observed data: application to a complex case

The aim of this paper is to show a methodology to reduce uncertainties in complex reservoir models using observed dynamic data. The basic idea is the use of the difference between observed and simulated data to constrain the probability redistribution of the uncertain attributes, reducing the spread of the posterior distribution and, as a consequence, reducing the dispersion of the reservoir response and mitigating risk. To capture the influence between attributes and reservoir responses, an influence matrix is proposed. The method deals with discrete and continuous attributes, which permits an adequate representation of the several types of uncertainties. The methodology was applied to a complex case and promising results are shown.