Early recognition of porosity and permeability contrasts in a reservoir haseconomic implications in reservoir management. Reservoir characterization aimsat 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 traditionallyinverted 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; Landaet 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 aninverse 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 afault 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 asgeological 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-propertydistribution and their response in a well test is evaluated with the help of anumerical 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.


Reservoir characterization includes the description of the geology and the dynamic behavior of a reservoir. Geological description involves a detailed knowledge of the hierarchical sequence of geological heterogeneities influencing reservoir properties and fluid flow characteristics in the reservoir. Engineering characterization, on the other hand, is concerned with understanding and being able to predict the dynamic behavior of these reservoir heterogeneities under specific primary, secondary and tertiary oil field development plans. Much research is being carried out on both subjects because there is still uncertainty about many aspects of reservoir heterogeneities and the role that they play in oil recovery (Weber4).

Some heterogeneity can be adequately quantified by direct measurements oncores or in boreholes. However, evaluation of the overall effects of heterogeneities on fluid behavior must be achieved with the aid of field experiments at a large enough scale or the use of models. Geological reservoir modeling can be based on deterministic or stochastic techniques, with the latter becoming widely accepted as a basis for the analysis and development of oil reservoirs.

The application of stochastic modeling techniques to reservoir characterization is often based on theoretical sedimentological parameters. Improvement in these modeling techniques depends on the input of reliablestatic and dynamic data. No single measurement, however, has enough resolution, accuracy and volume of investigation to provide all the required data for stochastic modeling. Pressure transient tests may come close, depending on the test duration. Hence, the need to research the extent at which well-testanalysis could provide further reliable information for reservoir heterogeneity characterization. The present paper reports results of well-test numerical simulations performed in order to capture the impact of reservoir heterogeneities on well-test behavior beyond what has been available to-date. The focus of the present study is on the external geometry of specific genetic units in various depositional environments.

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