Since there is much uncertainty in reservoir modelling, it makes sense to start with coarse-scale models, so that a wide range of scenarios can be assessed rapidly, before focussing on fewer, more detailed models. The simplest model for reservoir analysis is the material balance equation, and this forms a good starting point for uncertainty appraisal. Although there are drawbacks with this method, such as the assumption of pressure equilibration throughout the reservoir (or compartment), there is the advantage that a minimum number of a priori assumptions are made regarding the reservoir volume and drive mechanism.<br><br>As the first stage in a top-down reservoir evaluation procedure, we have applied stochastic history matching and uncertainty analysis to a material balance problem, using a synthetic reservoir model which had aquifer influx and high rock compressibility. A truth case simulation was run and noise was added to the resulting fluid production and pressure values to generate synthetic data sets. The parameters adjusted were the volume of oil (STOIIP), the initial aquifer size and the rock compressibility. A thorough analysis of the errors was performed, including propagation of errors in the pressure data to determine their effect on the modelled production. The Neighbourhood Approximation (NA) method was used to home in on models with low misfit. Then the posterior probability distributions and their correlations were assessed using a Bayesian approach.<br><br>Results showed that the shape of the posterior probability distributions (PPDs) depended on the assumed level of the noise. In particular, they indicated that, if the amount of noise is not assessed correctly, the position of the maximum likelihood value may be estimated incorrectly.<br>


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