Abstract
Reservoir simulation is the industry standard for reservoir management that is used in all phases of field development. As the main source of information, prediction and decision-making, the Full Field Models (FFM) is regularly updated to include the latest measurements and interpretations. A typical FFM consists of large number of grid blocks and usually takes hours for each run. This makes comprehensive analysis of the solution space and incorporation of the FFM in smart fields impractical. Surrogate Reservoir Models (SRMs) are introduced as a bridge to make Real-Time Reservoir Management possible. SRMs are replicas of FFM that can run in fractions of a second. They accurately mimic the capabilities of FFM and are used for automatic history matching, real-time optimisation, real-time decision-making and quantification of uncertainties. This paper presents the development of SRM using the state of the art Artificial Intelligence and Data Mining (AID Accepted: November 4, 2008]
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