Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements and interpretations. As such, these models carry an inherent degree of uncertainty. Typical uncertainty analysis techniques require many realizations and runs of the reservoir simulation model. In this day and age, as reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team, and most of the times are simply impractical. Analysis of these uncertainties and their effects on well performance using a new and efficient technique is the subject of this paper. The analysis has been performed on a giant oil field in the Middle East using a surrogate reservoir model.
The surrogate reservoir model that runs and provides results in real-time is developed to mimic the capabilities of a full field simulation model that includes one million grid blocks and takes 10 hours to run using a cluster of twelve 3.2 GHz CPUs. In order to effectively demonstrate the robustness of Surrogate Reservoir Models and their capabilities as tools that can be used for uncertainty analysis, one must demonstrate that SRMs are competent in providing reasonably accurate results for multiple realizations of the reservoir being studied. In order to demonstrate such robustness and their predictive capabilities as well as their limitations, this paper will examine the performance of the surrogate reservoir models on different geologic realizations of the static model.
In two previous SPE papers some of the aspects of the Surrogate Reservoir Models were discussed. In the first paper1 the idea of Surrogate Reservoir Models was introduced and in the second paper2 its application in quantifying uncertainties associated with a reservoir simulation study was explored.
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