Sensitivity of reservoir properties to broadband seismic amplitudes can be weak, which makes interpretation ambiguous. Examples of challenging interpretation scenarios include distinguishing blocky reservoirs from fining sequences, low gas saturation from high gas saturation, and variable reservoir quality. Some of these rock and fluid changes might indicate stronger sensitivity to amplitudes over narrow frequency bands, which is a characteristic of frequency-dependent amplitude variation with offset (FAVO). We have developed a FAVO model for reservoir characterization, following a seismic scattering phenomenon through a set of isotropic elastic layers. The frequency dependency in our model comes from the time delays due to wave propagation within layers. The FAVO modeled response is a complex-valued amplitude varying with angle and frequency, and it is a function of the seismic velocities and thicknesses of individual layers, along with the conventional AVO response at all interfaces. Our FAVO seismic analysis consists of two main steps: (1) forward modeling using well logs to understand rock and fluid sensitivity to amplitudes to identify tuning frequencies with maximum amplitude excursions and (2) seismic analysis at tuning frequencies. With well-log models, we observed that the frequency-dependent tuning response is primarily dependent on the lithology stacking pattern of a reservoir; in the cases studied, the fluid and reservoir quality have secondary effects on the frequency dependence of the amplitudes. We evaluate synthetic models and field data from the Columbus Basin, Trinidad, to illustrate our frequency-dependent seismic analysis methods. For one of the sandstone reservoirs, a frequency-dependent attribute indicates better spatial resolution of the anomaly than a conventional amplitude extraction. FAVO attributes are complementary to conventional AVO attributes.

A method for generalizing the conventional amplitude variation with offset model from an isolated interface to a scattering reservoir interval is presented. The advantage of this new method is that it can provide enhanced detection of subtle reservoir and pore fluid properties. First- and second-order expressions for the reflected compressional wave energy from a specified heterogenous interval are given. These expressions are applied to two problems of interest for reservoir description. One application is discriminating low versus higher saturations of hydrocarbons, and the other is detecting the extent of vertical stratification within a reservoir. The first-order expression is used for determining hydrocarbon saturations, and the second-order expression is used for detecting the magnitude of fine-scale layering within a reservoir. Synthetic models and field data examples are used in demonstrating the applicability of the proposed method.

Identifying thinly bedded reservoirs is important in exploration, appraisal, and development. The thin-beds we are referring to are of meter scale or less. Conventional seismic attributes are not able to resolve thin-bed effects. A new frequency-dependent amplitude-versus-offset (FAVO) seismic attribute is developed for the prediction of thin-beds. It is based on seismic wave scattering theory for vertically inhomogeneous media, where the seismic response from a laterally homogeneous interval is represented by the Born series. The Born series characterizes P-P and P-SV wave reflections, transmissions, and interbed multiples within the target interval with different order terms. The newly derived attribute corresponds to the second-order term of the Born series, where P-S wave reflection and transmission energy is taken into consideration, and it is named as the second-order FAVO gradient attribute. In a thinly bedded interval where sand/shale interfaces are abundant, the P-SV wave mode conversions are numerous. This leads to a strong second-order FAVO gradient attribute response compared to that of a blocky sand, silt, or shale interval under the assumption that there is no significant rock- and fluid- property differences between the intervals. Therefore, the new seismic attribute has the potential to be a thin-bed interval indicator. We propose a strategy to estimate the new attribute using conventional P-wave seismic data. The attribute is extracted from seismic data at the frequency range at which the second-order scattering effect is the most prominent. Synthetic and field data examples from offshore Trinidad are studied to demonstrate the second-order scattering effect and the potential usage of the second-order FAVO attribute for thin-bed reservoir characterization.

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