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AbstractEffective history matching of real fields requires the resolution of two outstanding problems. First, a conflict may exist between the production data and the existing geological model built solely from static information. In resolving this problem one must relate the inherent multi-scale nature of production data to petrophysical properties of the reservoir at the proper scale. Second, during model updates, geological consistency must be maintained by honoring the prior geologic information. The geoscientist has to choose what prior information is well know (hence fixed), and what is uncertain (hence modifiable).In many instances, the type of geostatistical algorithm is fixed, while key prior geostatistical parameters should be perturbed (e.g. facies proportions, petrophysical properties trends, variogram parameters, and random seed).We propose a new methodology that addresses these problems. First, streamlines are used to relate the production data to petrophysical properties at multiple scales. A combination of geostatistical tools (locally varying mean and probability perturbation method) are then used to jointly map multi-scale corrections back to the geological model through changes of the prior geostatistical parameters. The mapping reconciles the fixed prior geologic information with the production data. The geological model is then explicitly recreated by re-running the geostatistical simulation. This approach differs from other history matching techniques where the petrophysical properties of each grid block are modified directly. While a successful history match may be obtained, the resulting model may be inconsistent with important prior information, hence retaining little predictive power.The methodology is demonstrated by applying it to history matching a giant Saudi Arabian carbonate oil reservoir. This reservoir has over 500 producers and over 50 years of historical data with dramatically changing field conditions. In the past, several attempts were made to manually history matching this reservoir. The process was found to be extremely time consuming, involving dramatic local permeability changes which are often not supported by geological data. By applying the new approach, rather than correcting permeability manually, the corrections supplied by the streamlines were used to constrain the geostatistical algorithms, thereby ensuring a consistent geological scenario at every iteration. * 1 c . The new LVM is passed back to the geostatistical algorithm, and the permeability field is rebuilt with a new trend accounting for the water breakthrough data.