This paper presents an integrated methodology (using versatile history matching software) for constraining 3D stochastic reservoir models to well data and production history and its application to a real case (a tight-gas reservoir in the Southern North Sea).The approach is to formulate the history matching problem as a minimization problem where the objective function to be minimized is the squared difference of the real production data values and the corresponding simulated data. To resolve this inverse problem, we use gradient based optimization techniques to update the entire simulation workflow, thanks to advanced parametrization techniques. The set of model parameters characterizes the stochastic geological model (including the diagenetic modeling) and the fluid flow simulator, which allows the facies distributions (and their associated petrophysical properties), diagenetic overprint and well properties (such as skin) to be modified until an acceptable match is obtained.The parametrization technique is coupled to constrained optimization algorithms to ensure that model parameters remain within realistic bounds of the geological settings and ensures that the geologically derived stochastic properties of the initial reservoir are preserved throughout the history matching process.This application shows how the combination of a versatile history matching tool with efficient parametrization techniques can improve the quality and efficiency of a geologically driven history matching process. The speed of this process will be valuable when analyzing a set of history matched models allowing a better assessment to be made of the risk and uncertainty associated with predictions of future performance.
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