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AbstractAssessing reservoir connectivity and permeability heterogeneity is essential for predicting reservoir performance. As reservoirs are three-dimensional entities, there is little value in trying to predict reservoir connectivity based on twodimensional data such as geological sections or maps. A new method for seismic volumetric visualization, complemented with outcrop analog data, was used for assessing reservoir connectivity and permeability heterogeneity of the deep-water reservoirs of Marlim Sul field, Campos Basin, Brazil.The method consists of combining reflectivity and impedance seismic volumes. For the reservoir target zone, reflectivity was used as top drape covering the whole area, with color contrasts discriminating reservoirs and nonreservoirs, whereas in the impedance volume a cutoff value was used to show only the reservoirs. Such volumetric visualization technique permits the better identification of architectural elements and the better understanding of the evolution of the depositional system. However, some limitations still remain. Before building a reservoir model, it is necessary to assess (1) the internal permeability heterogeneity of the different architectural elements, and (2) the petrophysical nature of the boundaries among the different elements. This was performed by comparing geometries and facies observed in subsurface with geometries and facies documented in outcrop analogs. The main controls on connectivity and permeability heterogeneity in deep-water reservoirs are permeability barriers and baffles, such as hemipelagic shales and marls, turbiditic drapes, debris-flow deposits, shale-clast breccia and cemented zones. Such features present a complex and varied distribution. Barrier and baffle 3D maps of the different architectural elements and of the limits among them obtained in representative outcrops were used to complement seismic information.The integration of seismic volumetric visualization with outcrop analog data, calibrated by well information and tested again production data, proved to be useful for improving reservoir models.