In petroleum engineering, simulation models are used in the reservoir performance prediction and in the decision making process. These models are complex systems, typically characterized by a vast number of input parameters. Usually the physical state of the reservoir is highly uncertain, and thus the appropriate parameters of the input choices. The uncertainty analysis often proceeds by first calibrating the simulator against observed production history and then using the calibrated model to forecast future well production. Most models go through a series of iterations before being judged to give an adequate representation of the physical system. This can be a difficult task since the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time. As the uncertainty analysis is complex and time consuming; in this paper, a stochastic representation of the computer model was constructed, called an emulator, to quantify the reduction in the parameter input space due to production data over different production periods. The emulator methodology used represents a powerful and general tool in the analysis of complex physical models such as reservoir simulators. Such emulation techniques have been successfully applied across a large number of scientific disciplines. The emulator methodology was applied to evaluate the production data capacity to identify uncertain reservoir physical features over the production period for a synthetic reservoir simulation model. The synthetic model was built to represent a region of an injector and related producers. In the case studied; thousands of realizations were required to identify certain physical reservoir features. This justifies the use of emulation and shows the importance of this technique for the identification of regions of feasible input parameters. Moreover, the impact on the input space reduction due to different production periods was determined. The emulator methodology used assists in carrying out tasks that require computationally expensive objective function evaluation, such as identifying regions of feasible input parameters; making predictions for future behavior of the physical system and investigating the reservoir behavior.

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