OTC 16945 True 3D Data-driven Multiple Removal ... - PGS

OTC 3
where only two multiple contributions are available to predict
3D multiples for the outer streamers.
It should be noted that for a sailline spacing larger than twice
the streamer separation, the amount of contributions can be
increased artificially by applying some lateral shifts. In this
way, one source of a neighboring streamer will act as
secondary source for the two closest streamers.
It is also remarked that the findings are independent of the
actual streamer spacing as long as the sail line spacing is an
integer multiple of the streamer spacing to obtain a repeatable
pattern. The number of contributions will remain the same;
only the crossline offsets and aperture will change
accordingly.
Finally, for dual source configurations, increasing the spacing
between two sources may lead to a significant increase in both
the aperture and the number of multiple contributions.
However, this only applies to the hypothetical (and extremely
uneconomical) case where the streamer spacing equals the sail
line spacing. By putting both sources on one side of the
streamers, the number of multiple contributions is reduced
considerably. The contributions become very non-symmetric,
which will be non-ideal for any 3D SRME implementation.
The impact of acquisition on the prediction of 3D multiples
In this subsection, the impact of the distribution of multiple
contributions on the ability to predict 3D multiples is
investigated. Because of the simplicity of the model used,
insight is obtained in the general capabilities of interpolation
and/or reconstruction methods to predict 3D multiples when
only sparse data are available.
In this example, the subsurface model consists of one plane
layer dipping 4 degrees in the crossline direction only. Data
were modeled for a dual source 16-streamer configuration
with 50 meter streamer separation, with 200 meter and 400
meter sailline separation respectively. Note that for this
streamer configuration, 400 meter sail line spacing gives
conventional sub-surface coverage with no duplication in the
crossline direction. Figure 5a shows the ideal crossline
multiple contribution gather for a source and receiver
combination with 0 meter inline offset and 12.5 meter
crossline offset. Figures 5b-c show, for the same source and
receiver combination, the 7 and 3 crossline multiple
contributions available for a 200 and 400 meter sailline
separation respectively. Figure 6a shows a common shot
gather for the inner streamer, and Fig. 6b shows the
corresponding multiples, predicted using a 2D SRME
approach. Figure 6c shows the ideal 3D multiples, and Figs.
6d-e show the reconstructed multiples from the 200 and the
400 meter sailline configuration respectively. Note that for
both configurations, the 3D multiples were reconstructed
satisfactory. Figures 7 and 8 shows the same results for the
outer streamer. Note from Fig. 7b-c that the 5 contributions
from the 200 meter sailline configuration (spaced at –362.5, –
212.5, –162.5, –12.5 and 37.5 meters) are more equally
distributed in the crossline direction than the 3 contributions
from the 400 meter sailline configuation (spaced at –362.5, –
12.5 and 37.5 meters). From Fig. 8b it can be seen that the 2D
multiples are predicted too late in time. Also note that the
results for the 200 meter sailline separation, shown in Figure
8d, are significantly better then for the 400 meter separation
shown in Figure 8e. This is due both to the increased number
of contributions available (5 contributions for the 200 meter
sailline separation versus 3 for the 400 meter separation), and
the better distribution of the multiple contributions over the
total aperture from the 200 meter configuration. However, for
the data with 400 meter sailline separation, the second
multiple is predicted much better than with 2D SRME, and
although the wavelet of the first multiple is distorted, the
timing is good. Therefore we would still expect 3D SRME to
perform better than 2D SRME, even for the outer streamer.
Recommendations for acquisition
In 3D data-driven multiple removal, the multiple contributions
(defined as locations where both receiver and source data are
available) are the only data available to predict the 3D
multiples. Hence, the distribution of multiple contributions
will govern the quality of the final predicted 3D multiple trace.
Results from synthetic modeling indicate that a denser grid of
multiple contributions leads to a better prediction of the 3D
multiples.
During marine acquisition, the distribution of the crossline
multiple contributions can be optimized for the application of
3D SRME by
• Shooting overlap (by increasing the number of streamers
while keeping the sailline spacing constant);
• Decreasing the sailline spacing;
• Keeping the sailline spacing an integer multiplication of
the streamer spacing;
• Increasing the spread length
For a more complex subsurface, it is therefore recommended
to create a dense grid of multiple contributions with sufficient
aperture to reconstruct the full complexity of the 3D multiples
successfully.
A processing strategy for 3D SRME
This section describes an efficient processing sequence to
apply 3D SRME in a production environment. A three-step
sequence is proposed to predict and remove 3D multiples for
each sail line using only the recorded data around the output
streamer. Even though the proposed sequence will benefit
from any form of acquisition optimization, the strategy is
designed to handle 3D data sets that were acquired from
conventional marine acquisition. The three steps involved are
discussed in the following three subsections.
Step 1: Pre-processing
The pre-processing sequence starts with an analysis of the
navigation data to investigate the positioning of the source and
receivers. When irregularities (due to feathering and other
positioning errors, such as drop-outs) are severe, wavefield
regularization should be applied as part of the pre-processing
sequence to position sources and receivers onto a regular pre-