Accurate approximations of Green’s functions retrieved from the correlations of ambient noise require a homogeneous distribution of random and uncorrelated noise sources. In the real world, the existence of highly coherent, strong directional noise generated by ships, earthquakes, and other human activities can result in biases in the ambient-noise crosscorrelations (NCCs). We have developed an adapted eigenvalue-based filter to attenuate the interference of strong directional sources. The filter is based on the statistical model of the sample covariance matrix and can separate different components of the data covariance matrix in the eigenvalue spectrum. To improve the effectiveness and make it adaptable for different data sets, a weight is introduced to the filter. Then, the NCCs can be calculated directly from the filtered data covariance matrix. This approach is applied to a 1.02 h data set of ambient noise recorded by a permanent reservoir monitoring receiver array installed on the seabed. The power spectral density indicates that the noise recordings were contaminated by strong directional noise over nearly half of the whole observation period. Beamforming and crosscorrelation results indicate that the interference still exists even after applying traditional temporal and spectral normalization techniques, whereas the adapted eigenvalue-based filter can significantly attenuate it and help to obtain improved crosscorrelations. The approach makes it possible to retrieve reliable approximations of Green’s functions over a much shorter recording time.
Production-induced geomechanical stress changes cause velocity changes in the overburden that might be detected as 4D seismic time shifts. The strength of the velocity changes depends on the degree of pressure changes and the elastic properties of the reservoir and overburden layers. Even small velocity changes (less than 1%) might accumulate into detectable seismic time shifts at the top reservoir, since the overburden thickness typically ranges from one to several kilometers. Reservoir pressure changes inducing seismic time shifts are observed in the overburden of the Snorre, Heidrun, and Statfjord fields, all located on the Norwegian Continental Shelf. The strong correlation between overburden time shifts, geomechanics, and reservoir pressure changes is used to indicate undrained areas and transmissibility across faults, which is useful information for increased oil recovery, well planning, and reservoir model updating. 4D geomechanical models are built with input from simulated reservoir pressures. Geomechanical strain and velocity changes are linked through a “dilation” factor, R. The Snorre, Heidrun, and Statfjord fields indicate an average R value of about 15 for the overburden, when combining modeled vertical strain with observed seismic time shifts. However, this study also shows strong vertical variation in R, implying that R might be layer dependent. For the Statfjord Field, seabed subsidence measurements from gravity and GPS monitoring are used to calibrate the geomechanical model. The Snorre Field results show that both reservoir pressure depletion and pressure buildup can be identified by the use of overburden time shifts. The properties of the reservoir formations and surrounding layers of the investigated fields are typical for many fields on the Norwegian Continental Shelf. This implies that pressure-induced time shifts might be expected for many producing fields, not only chalk or high-pressure, high-temperature reservoirs but also sandstone reservoirs close to hydrostatic pressure.
Ambient noise carries abundant subsurface structure information and attracts ever-increasing attention in the past decades. However, there are lots of interference factors in the ambient noise in the real world, making the noise difficult to be utilized in seismic interferometry. The paper performs shear-wave tomography on a very short recording of ocean ambient noise with interference. An adapted eigenvalue-based filter is adopted as a pre-processing method to deal with the strong, directional interference problem. Beamforming and the noise crosscorrelation analyses show that the filter works well on the noise recorded by the array. Directional energy is significantly suppressed and the background diffuse component of the noise is relatively enhanced. The shear-wave tomography shows a 4-layer subsurface structure of the area covered by the array, with relatively homogeneous distribution of the shear-wave velocity values in the top three layers and a complicated structure in the bottom layer. Moreover, 3 high-velocity zones can be recognized in the bottom layer. The result is compared with several other tomography results using different methods and data. It demonstrates that, although the ambient noise used in this paper is very short and severely contaminated, a reasonable tomography result can be obtained by applying the adapted eigenvalue-based filter. Since it is the first application of the adapted eigenvalue-based filter in seismic tomography using ambient noise, the paper proves the effectiveness of this technique and shows the potential of the technique in ambient noise processing and passive seismic interferometry.
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