عنوان مقاله [English]
Large percentages of world hydrocarbon reservoirs are fractured reservoir and fracture distribution in these reservoirs are not distributed randomly. Fracture distribution in fractured reservoir depends on the combination of structures, lithology, reservoir thickness, fault, and other fracture controllers. For accurate fracture modeling, these factors should be integrated with well data. For this purpose, fracture density of three wells in Marun oil field is used. Fracture related seismic attributes such as instantaneous frequency, curvature, and dip is extracted from seismic data. Geological and petrophysical features, which control fracture density, are modeled by the integration of seismic and well data. By the integration of these factors and fracture density in wells, fracture density is modeled by artificial neural network. In this study, the continuous fracture model is properly modeled and the results demonstrate 82% correlation with well data. This continuous fracture model has a relatively good relation with transmissibility map and the transmissibility map satisfies high fracture density in the south limb of anticline. Most fracture densities are located in the southern limb of anticline and this seems to be suitable region for field development.