Summary. Introduction

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1 Multiple attenuation for variable-depth streamer data: from deep to shallow water Ronan Sablon*, Damien Russier, Oscar Zurita, Danny Hardouin, Bruno Gratacos, Robert Soubaras & Dechun Lin. CGGVeritas Summary Variable-depth streamer acquisition is becoming a key technique for providing wide bandwidth seismic data. Varying the receiver depth creates wide receiver ghost diversity and produces a spectacular increase in the frequency bandwidth. However, compared to conventional data, this variable-depth streamer data implies a major challenge in processing: how to deal with various receiver ghosts. The ghosts have to be preserved up to the deghosting step. Here we present the implication for the following de-multiple methods: Shallow-Water Demultiple, Tau-P deconvolution and Surface-related multiples elimination in deep and shallow water environments. Introduction Processing the variable-depth streamer acquisition has recently become possible, with a new advanced algorithm, called joint deconvolution (Soubaras, 2010). In this particular acquisition, the receiver depth regularly increases with offset, which allows a wide diversity of receiver ghosts and so increases dramatically the possible frequency bandwidth, in both low & high-frequencies sides, from 2.5 Hz to source notch. Compared to conventional flat-streamer data, processing the variable-depth streamer data implies a major change: the receiver ghosts are rigorously taken into account. In conventional processing, both source and receiver ghosts are included in a wavelet that is assumed to be consistent from offsets to offsets. On the contrary, in a variable-depth streamer dataset, the receiver ghosts change from near offsets to far offsets and so cannot be included in a wavelet. This breaks an implicit assumption of many processing steps such as surface related multiple elimination (SRME). These receiver ghosts will then be removed from the final image with a pre-stack or post-stack joint deconvolution. Of course, the receiver ghost preservation is a constraint for some programs developed for conventional processing. One of the key challenges, presented in this paper, is how to deal with de-multiples techniques and variable-depth streamer data, in both deep and shallow-water environments. Several variable-depth streamer data were acquired across the world, among which two examples will be presented, in Gulf of Mexico and Central North Sea (Figure 1). Figure 1: Variable-depth streamer acquisitions (Yellow stars). Seismic images will be shown from the circled stars De-multiple techniques with variable-depth streamer data in deep-water environment The de-multiple technique commonly used in a deep-water environment is the 2D, or 3D, surface-related multiple elimination (Berkhout and Verchuur 1997). By applying SRME on conventional data, where both source & receiver ghosts have already been included in a wavelet, the modelled multiples are close to the input data multiples. A key issue appears with a variable-depth streamer data, because of the receiver ghosts: Variable receiver depth creates visible differences in wavelet, from near to far offsets (Figure 3). By convolving traces with different wavelets, the traditional SRME method produces multiple models with mixed wavelets, really different from input data, and the differences vary from offset to offset. There are basically two ways of solving this problem: either by improving the multiple model computation, or by adjusting the wavelets with a global adaptation. In practise, the only available tool at the beginning of variable-depth streamer data processing tests was to adjust the model wavelets in common offset domain. The effectiveness of this multiples model adjustment is already significant, and has been estimated to be up to 75 % of the result we would get with a conventional data. SEG San Antonio 2011 Annual Meeting 3505

2 The remaining problem with this method is that, in some cases, the multiples model adjustments are somewhat difficult. As a consequence, a new SRME sequence is being developed for variable-depth streamer data. The goal is again to create a multiples model as accurately as possible. The SRME was adapted to be in similar conditions as for conventional processing, and compute a ghost-free multiples model, which is then re-ghosted with a deterministic operator. This sequence has been tested with different datasets and produces better results than a traditional SRME (Figures 4 to 7). Once the multiples model matches perfectly with the input data, the multiples model adjustment is even more accurate and efficient. De-multiple techniques with variable-depth streamer data in shallow-water environment For conventional data processing in shallow water environments (< 150m), SRME method is known not to be well adapted for short-period multiples reflections: due to the lack of near-offsets, the recorded water-bottom reflections, used by SRME, are often not good enough or missing. For variable-depth streamer data, other methods have to be tested: Tau-P deconvolution and shallow water demultiple (SWD) (Hung et al, 2010). In a conventional data processing sequence, the predictive deconvolution in Tau-P domain is frequently used for attenuating shallow-water multiples. For variable-depth streamer data, this method could also be applied in both shot and receiver domain. The risk here is to affect receiver ghosts with a periodicity close to that of the water layer. For variable-depth streamer data, this method could also be applied: the key point is to keep a gap long enough to preserve the receiver ghost (Figure 2) The shallow water de-multiple method uses the water layer related multiples from the data in order to reconstruct the missing water bottom primary reflections, and then use it to compute a short-period multiples model. These de-multiple methods were tested on a Variable-depth streamer 2D line in Central North Sea, and then applied on a small 3D volume in the same area (Figure 9 and 10) Different trials were done by combining different tools and the best result was finally achieved by combining Shallow water de-multiple, predictive deconvolution in Tau-P domain (in both shot and receiver because, in this case, the water bottom is a very strong reflector), and SRME (Figure 8): The water-layer multiples are handled by SWD & Tau-P predictive deconvolution, and SRME tackles freesurface multiples that have longer periods. In this case, the water bottom has to be muted prior to generate the SRME model. Conclusions Processing variable-depth streamer data introduces new challenges, mainly due to the receiver ghosts which have to be preserved and used in the joint deconvolution for the final image. A key challenge is how to deal with de-multiples techniques and receiver ghost preservation. These three examples show that the following de-multiples methods: Shallow-water de-multiple, Tau-P predictive deconvolution and SRME, can significantly attenuate multiples with results at least equivalent to those obtained on conventional data. However, all these techniques were not developed for variable-depth streamer data, and algorithmic modifications, especially with SRME, are currently being developed to handle the receiver ghosts. Acknowledgements Figure 2: Autocorrelation of a variable-depth streamer shot gather on a window : offset m, Time 0-3s, showing the ghosts varying along the offset. By applying a predictive deconvolution on such a data, a long gap has to be chosen to preserve the receiver ghosts. We thank CGGVeritas for permission to publish this paper, Salvador Rodriguez and Robert Dowle for coordinating the variable-depth streamer test campaigns and Vera Romano for help in testing SWD. SEG San Antonio 2011 Annual Meeting 3506

3 Figure 3: Multiples wavelet variations on three offset planes corresponding to 8, 19 & 30 meters- cable depths. Figure 4: from left to right : Input offset plane corresponding to 12 meters- cable depth, Multiples model generated with conventional 2D SRME sequence & Multiples model generated with the new 2D SRME sequence. Figure 5: From left to right : Input offset plane corresponding to 12 meters- cable depth, Result with adaptive subtraction of the multiples model generated with the conventional 2D SRME & Result with adaptive subtraction of the multiples model generated with the new 2D SRME. For right comparison, the same adaptive subtraction is applied in both case. Figure 6: Different 2D SRME sequences on Gulf of Mexico dataset. (Pre-stack time migration with post-stack joint deconvolution). From Left to Right : meters offset stack with conventional 2D SRME, meters offset stack with new 2D SRME & Difference. With such a deep-water dataset, the stack effect is already powerful to attenuate the multiples: that is why the differences between the two SRME sequences are easier to distinguish on a meters offset stack. Figure 7: Different 2D SRME sequences on Gulf of Mexico dataset. (Pre-stack time migration with post-stack joint deconvolution). From left to Right: Result with conventional 2D SRME, Result with new 2D SRME and difference. SEG San Antonio 2011 Annual Meeting 3507

4 Figure 8: Different demultiples results on Central North sea 2D Dataset (Pre-stack time migrations with post-stack joint deconvolution). From left to right: Predictive deconvolution in both shot & receivers, Shallow water de-multiple followed by the previous predictive deconvolution in shot & receivers, Shallow water de-multiple with predictive deconvolution followed by 2D SRME. Figure 9: Central North Sea 3D Conventional dataset, PSTM result: Time slice at 236 ms Figure 10: Central North Sea 3D Variable-depth streamer dataset: PSTM with post-stack joint deconvolution result: Time slice at 236 ms. The de-multiple sequence applied on this 3D volume is: Shallow water de-multiple followed by predictive deconvolution in shots and receivers. (Provisional processing without data regularization) SEG San Antonio 2011 Annual Meeting 3508

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2011 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Berkhout, A. J., and D. J. Verschuur, 1997, Estimation of multiple scattering by iterative inversion, Part I: Theoretical considerations: Geophysics, 62, , doi: / Hung, B., K. Yang, J. Zhou, Y. Guo, and Q. L. Xia, 2010, Surface multiple attenuation in sea beachshallow water, case study on data from the Bohai Sea: 80th Annual International Meeting, SEG, Expanded Abstracts, Soubaras, R., 2010, Deghosting by joint deconvolution of a migration and a mirror migration: 80th Annual International Meeting, SEG, Expanded Abstracts, SEG San Antonio 2011 Annual Meeting 3509