Quantification of uncertainties associated with reservoir performance simulation
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This research presents a method to quantify uncertainty associated with reservoir performance prediction after history match by conditioning black oil with compositional simulation. Two test cases were investigated. In the first test case, a black oil history matched model of a natural depleted volatile oil reservoir was used to predict reservoir performance. The same reservoir was simulated with compositional model and the model used to forecast reservoir performance. The difference between black oil and compositional models predicted cumulative oil production were evaluated using an objective function algorithm. To minimize the objective function, the black oil and compositional simulation reservoir descriptions were equally perturbed to generate few multiple realizations. These new realizations were used to predict oil recovery and their forecast optimized. Non-linear analysis of the optimization results was used to quantify the range of uncertainty associated with the predicted cumulative oil production. Similarly, a second test case was studied whereby, the same volatile reservoir was produced under water-alternate-gas injection scheme. As in the first test case, it is shown how optimization followed by non-linear analysis of both the black oil and compositional simulation predictions can be used to assess uncertainty in reservoir performance forecast. It is well known that the disadvantage of the black oil is its inability to simulate comprehensive reservoir fluid compositional data. To eliminate this limitation in reservoir performance prediction, this research presents a technique that is based on conditioning black oil output with compositional simulation in order to better account for fluid phase behavior and reservoir description influence on reservoir performance.