The ultimate goal of reservoir modeling is to obtain a model of the reservoir that is able to predict future flow performance. Achieving this challenging goal requires the model to honor all available static (well log, geological information and 3D seismic) and dynamic (4D seismic and production) data. This paper introduces a general methodology and workflow for reservoir modeling that integrates data from multiple and diverse sources, using a probabilistic approach addressing the possible inconsistency and/or redundancy between various data sources. The goal of the workflow is to model an unknown A (facies/petrophysical property) using data from different sources D1, D2, …, Dn. The workflow requires modeling the information content of each data source as a spatial distribution model termed P(A|Di). Next, all P(A|Di) are combined into a joint conditional P(A|D1,D2,D3) from which reservoir models are drawn using sequential simulation. The procedure followed to modeling the information content of each data source as a spatial probability distribution model depends on the data source. Geological information about A is made quantitative through a training image, a 3D geological analog representation of the subsurface heterogeneity. Multiple-point geostatistics translates training image information into local conditional probabilities of the unknown variable A. 3D seismic data is translated to a spatial probability distribution using a calibration between well logs and the 3D seismic data itself. Production and 4D seismic data are translated using and iterative procedure termed the probability perturbation method. Using Journel's tau model, a model for data redundancy, the spatial probability distributions are combined into a joint probability model used for sequential simulation. This paper shows that the reservoir model obtained using this approach honors both static and dynamic data simultaneously while explicitly accounting for data redundancy.


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