Estimation of Q from surface-seismic reflection data in data space and image space |

First, attenuation is important for characterizing rock and fluid properties, e.g., saturation, porosity, permeability, and viscosity, because attenuation is more sensitive than velocity to some of these properties. For example, Q can serve as a lithology discriminator in a frontier area with sparse well control (Dasgupta and Clark, 1998). In addition, since the magnitude of the attenuation is directly related to petrophysical parameters, Q analysis provides a potential tool for reservoir characterization; it can help determine the contents (e.g. gas saturation) of a reservoir, map fracture azimuth to target reservoir development, and monitor the mobility of reservoir fluids to optimize the injection process.

Second, if ones knew the absorption properties of the subsurface, they could include them in seismic data processing (deconvolution, stacking, migration, inverse Q filtering, etc.) and get sharper images and higher resolutions. Attenuation estimation could also be used to better interpret the effects of AVO and anisotropy, which have offset-dependent signatures. Furthermore, full waveform inversion can achieve improved accuracy by incorporating attenuation into the initial model.

Third, attenuation estimates are useful in seismic acquisition design. Knowing the level of seismic attenuation in the survey-planning stage helps determine how much signal may reach the target, and enables the optimization of acquisition parameters.

However, it is difficult to estimate seismic attenuation. First, it is hard to distinguish intrinsic attenuation and scattering attenuation. In this study, I concentrate on the intrinsic attenuation and try to reduce the influence of the scattering attenuation. In the situation when the scattering attenuation cannot be ignored, the estimated attenuation is a combination of intrinsic and scattering effects. The other most important reason is that amplitudes are easily affected by many factors, such as geometric spreading, source and receiver coupling, radiation patterns, transmission/reflection effects, focusing effects, PS conversion, etc. In this paper, I first review existing methods of estimating Q and illustrate how they account for these contaminating factors. Then, according to the advantages and drawbacks of these conventional Q measurements, I propose a new method of Q estimation from surface seismic data and test it on a 2D synthetic model.

Estimation of Q from surface-seismic reflection data in data space and image space |

2012-05-10