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Azimuthal Attenuation Attributes Applied To Integrated Fractured Reservoir Characterization By Using 3-D Prestack Seismic Data


Fractures are a crucial factor controlling the well performance in reservoirs, especially with low permeability. Fractured reservoirs are highly heterogeneous. It is known that vertically aligned fractures in a reservoir can induce seismic anisotropy (HTI anisotropy). Seismic anisotropy has become a powerful tool for the characterization of fractured reservoirs. In recent years, as interest in azimuthal anisotropy analysis of seismic data has grown significantly to detect fractures, azimuthal variations in P-wave amplitude, travel-times, velocity, frequency and AVO gradient have all been shown to be diagnostic of the presence of aligned fractures, these attributes are used to detect fracture density and orientations.

Fracturing is controlled by geological and mechanical properties of reservoir rocks. Seismic attenuation has come to be recognised as potentially very sensitive to reservoir properties. This is because of its sensitivity to the saturating fluid and petro-physical properties, and in turn, due to sensitivity to changes of effective stress. Attenuation levels are also sensitive to fractures. So we present a methodology for using seismic attenuation variation with azimuth in prestack domain to estimate the anisotropy parameter, or characterize the fracture. It involves that the QVO method, an extension of the classical spectral ratio method for determining attenuation, was used to calculate the quality factor from the pre-stack gathers. In this method, seismic survey is processed to generate separate azimuthal migrations. And attenuation attributers are independently calculated for each azimuthal gathers using QVO method. Azimuthal variations in the attenuations are then quantifi ed through ellipse fitting in detecting fracture density and orientation. Furthermore, we integrated seismic, geological, and well log data using artificial neural networks in order to constrain the interpretation of seismic anisotropy. The artificial neural networks were trained on seed points from well control and anisotropy related attenuation attribute. Integrative purpose is to lessen the ambiguity for identifying fracture. Using a survey that was acquired off Liaohe oilfield, we demonstrate that this method is able to obtain density and directions of fractures. Our results show that, in our working area, the fracture orientations and density distributions are consistent with fault system and high fracture density is correlated with high producibility in the reservoir. This method provides reliable anisotropic attribute to characterize fractures.