3-D acoustic impedance estimation

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Seismic Data Analysis
Series Investigations in Geophysics
Author Öz Yilmaz
DOI http://dx.doi.org/10.1190/1.9781560801580
ISBN ISBN 978-1-56080-094-1
Store SEG Online Store

As for the AVO attributes, when derived from 3-D seismic data, an acoustic impedance attribute volume can be used to infer lithology or porosity within a reservoir zone. We shall analyze a land 3-D data set for acoustic impedance estimation. Data specifications for the 3-D seismic data are listed in Table 11-5. The fold of coverage over the survey area is fairly uniform (Figure 11.3-18). In addition to the seismic data, sonic and density logs from a well from within the survey area were used in estimating and removing the constant and linear phase associated with the residual wavelet in the time-migrated CMP stacked data prior to amplitude inversion.

Note the high level of ground-roll energy on the selected shot records with the display gain shown in Figure 11.3-19. The noise characteristics vary from shot to shot. The processing sequence included the following steps:

  1. Apply t2-scaling to compensate for geometric spreading. Figure 11.3-20a shows a raw shot record and Figure 11.3-20b shows the same record after the application of t2-scaling.
  2. Perform spiking deconvolution (Figure 11.3-21a).
  3. Note from the spectral estimate shown in Figure 11.3-22 that deconvolution alone does not flatten the spectrum. Apply time-variant spectral whitening (Figure 11.3-21b) to attain a flat spectrum within the signal passband (Figure 11.3-22d) and attenuate the ground-roll energy.
  4. Sort the data into common-cell gathers and perform velocity analysis at coarse grid spacing.
  5. Create a 3-D stacking velocity field and apply NMO correction to the cell gathers.
  6. Apply residual statics corrections (processing of 3-D seismic data) and inverse NMO correction using the velocity field from step (e).
  7. Perform velocity analysis at tight grid spacing and create a new 3-D velocity field.
  8. Apply NMO correction to the cell gathers from step (f), mute and stack.
  9. Perform poststack deconvolution and a wide band-pass filter.
  10. Apply f − x deconvolution (linear uncorrelated noise attenuation) to attenuate random noise uncorrelated from trace to trace.
  11. Finally, perform 3-D poststack phase-shift migration. Note from Figure 11.3-23 that the processing sequence described above yields the spectral shape of the data that we wish to input to poststack amplitude inversion — a broadband spectrum with nearly a flat passband.
Table 11-5. Data specifications for the 3-D seismic data used in acoustic impedance estimation.
Survey size 27.3 km2
Inline dimension 7.35 km
Crossline dimension 3.75 km
Receiver line spacing 60 m
Bin size 30 × 30 m
Number of inlines 124
Number of crosslines 245
Number of bins 30,380
Fold of coverage 16
Sampling interval 2 ms

Figure 11.3-24 shows selected inlines from the image volume derived from 3-D poststack time migration. Following the principle of parsimony in processing for AVO analysis and acoustic impedance estimation, no DMO correction was deemed necessary in the present case study since the subsurface geology can be described by a horizontally layered earth model with no significant faulting or structural distortions.

The next phase in the analysis involves the estimation and removal of the residual phase contained in the time-migrated volume of data. Figure 11.3-25 shows a sonic and a density log measured at a well located at the intersection of inline 76 and crossline 79. Compute the acoustic impedance and the reflectivity series; then, use a zero-phase wavelet with a passband comparable to the signal bandwidth of the seismic data to compute the zero-offset, vertical-incidence synthetic seismogram also shown in Figure 11.3-25. Note from Figure 11.3-26 that the sonic log inserted into a portion of inline 76 at the well location exhibits a very good match with the time-migrated data within the reservoir level indicated by the arrow. To achieve this match, a constant time shift, which is equivalent to a linear phase shift, was applied to the sonic log. A similarly good match is observed in Figure 11.3-27 between the synthetic seismogram and the time-migrated data. To achieve this match, in addition to the constant time shift, a -90-degree phase rotation was applied to the synthetic seismogram. To remove the residual phase in the migrated data set and match it to the zero-phase synthetic seismogram, you therefore need to apply a 90-degree phase rotation to the former.

Figure 11.3-28a shows inline 76 of the image volume from 3-D poststack time migration, and Figure 11.3-28b shows the same section after the 90-degree phase rotation. The phase-rotated image volume is then used in amplitude inversion to create a sparse-spike reflectivity volume. Figure 11.3-28c shows inline 76 from the sparse-spike reflectivity volume. Finally, each sparse-spike reflectivity trace is integrated to estimate the acoustic impedance attribute (Figure 11.3-28d). Selected inlines from the acoustic impedance volume are shown in Figure 11.3-29. These inline traverses coincide with those shown in Figure 11.3-24 from the image volume derived from 3-D poststack time migration before the application of phase rotation.

The acoustic impedance volume is then interpreted to identify the spatial distribution of high-impedance and low-impedance areas at the reservoir level. First, pick the time horizon at the reservoir level from the image volume and isolate a horizon-consistent thin slab of subvolume. Then, apply transparency to the subvolume to obtain the interpretation result shown in Figure 11.3-30. The red represents the areas of high-amplitude reflections at the reservoir level. Apply the same interpretation strategy to the acoustic impedance volume and obtain a consistent result as shown in Figure 11.3-31. The orange tones represent the zones of high acoustic impedance contrast.

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