A Discrete Fracture Network (DFN) model was used to simulate the results of a production test carried out in a well drilled in a tight, fractured carbonate reservoir. Several static DFN models, depicting different geological scenarios, were built based on data from well logs, core analyses, PLT surveys and structural geology studies. Each of these models underwent a validation procedure, consisting of the simulation of the production test. The comparison between the simulated results and the actual data identified the scenario whose results most closely matched the actual well behaviour. In order to compensate for the lack of geological data, an iterative loop was performed between the static model and the dynamic simulation. Constraints-added flow simulations provided new information for use in modifying the DFN model, resulting in a step-by-step updating of the static model itself. Finally, a geologically sound model accurately matching the results of the production test was obtained. The final DFN model was used to calculate the equivalent petrophysical parameters that were transferred to the corresponding region of the full field dual-porosity fluid flow model.

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An accurate reconstruction of reservoir heterogeneity is an important goal of a reservoir study. The methodology presented here is aimed at providing a complete petrophysical characterization of the reservoir, particularly when the heterogeneity cannot be adequately described using traditional methods. The first phase of the methodology is integrated petrophysical characterization. This is performed by a statistical analysis of log and core data using a 'cluster analysis' algorithm. This allows an objective classification of the reservoir components into homogeneous subsets (lithofacies), each with specific lithological and petrophysical characteristics. The second phase is simulation of the reservoir heterogeneity. Here, the 3D distribution of the lithofacies recognized at the wells is performed consistently with the sedimentological model by means of geostatistical simulations carried out using a high-resolution grid. The porosity and permeability values characterizing each lithofacies are then assigned to the simulated images to obtain a highly detailed 3D petrophysical description of the reservoir.

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