Early recognition of porosity and permeability contrasts in a reservoir has economic implications in reservoir management. Reservoir characterization aims at developing a detailed knowledge of the geological heterogeneities that may affect production rates and recovery factors. In the area of reservoir characterization, geological and engineering data have been traditionally inverted separately: a 3-D static reservoir model is established from static data (geology, geophysics and petrophysics) and the parameter distributions in that model are adjusted by history matching with production and other dynamic data. The underlying assumption in this process is that production data correspond to the same model as static data (Floris et al.1; Landa et al.2). This may or may not be the case. Hence, the requirement to first identify separately a model for each type of measurement and to verify that each model is consistent with all the others. Because identification is an inverse problem, with a non-unique solution, it is also desirable to confirm each feature of the reservoir model with different types of measurements3. For some features this is possible with today's technology. For instance, well test analysis can confirm the location of a fault identified from geophysical measurements and vice-versa. Other features, on the other hand, require additional development.

The present work looks at the medium-scale architectural elements that have been formed in different depositional environments,, with the objective of reconciling geological and well testing modeling in order to increase the confidence in the evaluation of fault sealing, sandstone bodies geometry and lithofacies distribution. In this approach, the heterogeneities are regarded as geological objects characterized by geometrical parameters as well as by petrophysical properties. Synthetic genetic-type objects are generated by stochastically modeling their external geometries and internal rock-property distribution and their response in a well test is evaluated with the help of a numerical well test simulator. The systematic evaluation of the well testing responses of various geological objects yields a new source of reliable information for reservoir characterization.

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