Technical Abstracts

3D Model-based Water-layer Demultiple (MWD) is a recently-developed technique for attenuating water-layer-related multiples for marine streamer data. In particular, MWD targets the challenge of multiple attenuation in shallow water where Surface-Related Multiple Elimination (SRME) struggles. MWD works well in shallow water data by avoiding the crosstalk influence and preserving the spectrum of the input data. In this paper, we demonstrate that the application of MWD is not limited to streamer data, but can also be extended to Ocean Bottom Seismic (OBS) data. For OBS data, MWD can remove water-layer-related multiples and receiver ghost in one step. Therefore it removes the necessity of PZ summation for receiver ghost attenuation.

Estimation of surface-consistent residual statics on large wide-azimuth Middle-East data using a Monte-Carlo approach is a challenge. This non-linear method that uses Simulated Annealing to compute large magnitude statics is characterized by its efficiency but also by a high computation cost. However, by using advanced methods like High Performance Computing, the computation cost of this algorithm can be drastically reduced. This paper demonstrates that a non-linear approach to estimate large magnitude statics is now possible with a reasonable turn-around without chunking the statics computation into several swaths. Different methods are used in computing surface-consistent residual statics. Most of these methods are based on the use of the cross-correlation functions and solution of linear system equations at a local minimum. A non-linear approach, however, that uses the Simulated Annealing concept coupled with a Monte-Carlo technique allows computing surface-consistent residual statics at the global minimum. The Monte-Carlo approach we chose to implement uses a cost function that is based on the coherence of the stack with some robust criteria to stabilize the results (Le Meur and Merrer, 2006). Data access is the main bottleneck when non-linear methods based on Simulated Annealing are used on large Wide-Azimuth data. At each simulation step, stations must be visited in a random order. For each station, several associated collections (shots or receivers and CMP) are used to compute the cost function. This means that the input pre-stack dataset has to be read several hundred times in a random order! Indeed, for a given simulation step, residual static corrections cannot be computed independently because stations overlap each other in the CMP domain, ie, the algorithm cannot be massively parallelized in stations domain. Processing Wide-Azimuth surveys in a reasonable turn-around time was a real challenge for that method. This extremely high computation cost could be decreased only by using an advanced High Performance Computing solution. We made it possible to perform the method efficiently and without chunking the input data by swaths by implementing a careful fine-grained multi-core parallelism as well as some software optimization. These High Performance Computing techniques allowed us to optimize and minimize the data access time and therefore speed up the efficiency of the non-linear inversion. In this paper, we illustrate the efficiency of this stochastic approach on different 3D Wide-Azimuth surveys from the Middle-East.

The paper presents a case history of multi-azimuth 3D PSDM processing. The two overlapping surveys to be processed were acquired at an angle of 105 degrees from each other, and the data exhibit strong HTI and VTI effect, pointing to the presence of orthorhombic anisotropy. We show how the orthorhombic velocity model was created and how the final images benefited from the orthorhombic PSDM.

The application of a new ray-based multi-layer approach for reflection tomography overcomes many of the limitations of tomographic methods typically employed on PSDM projects. Multi-layer tomography, utilizing a new hybrid model representation, allows geological boundaries to be accurately represented and repositioned by map migration within the inversion. This allows all units in the model to be updated simultaneously without the need for a layer stripping workflow.

The application of a new ray-based multi-layer approach for reflection tomography overcomes many of the limitations of tomographic methods typically employed on PSDM projects. Global tomography is unable to accurately represent major velocity and anisotropy contrasts within the framework of a cell-based model representation and will not correctly reposition layer boundaries during the inversion process. Multi-layer tomography, utilizing a new hybrid model representation, allows layer boundaries to be accurately represented and repositioned by map migration within the inversion. This allows all units in a complex layered model to be updated simultaneously.

4D monitoring processing focuses on traveltime and amplitude variations in order to monitor subsurface properties evolution. Using source and receiver antennae with double beamforming (DBF) processing, we propose to separate waves in function of 5 parameters; time, source and receiver slownesses and source and receiver azimuths. By repeating acquisition at the same place and after wave separation, we are able to monitor not only traveltime and amplitude but also the source and receiver slownesses and azimuths of both body waves and surface waves. In a second step, a method is proposed to separate variations from the reflection of interest at depth from other variations occurring in the medium.

Model-based Water-layer Demultiple (MWD) is a recently-developed method aimed at tackling the challenge of multiple attenuation in shallow water. MWD works by modeling the Green?s function of the water-bottom primary reflections based on a user-supplied water-layer model, then convolving it with the recorded data to predict water-layer-related multiples. In this paper, MWD is applied to Hibernia field data which has a water depth of around 70-90 meters. The results show that while SRME by itself has limited success, MWD is effective in attacking water-layer-related multiples. The effectiveness is attributed to the fact that MWD predicts the multiple models with correct relative amplitude and a spectrum similar to the input data?s. SRME, on the other hand, suffers in shallow-water situations, primarily due to cross-talk between multiples. Once the water-layer-related multiples are removed by MWD, SRME can then be applied to predict and eliminate other types of surface-related multiples which tend to have longer periodicity and less cross-talk. The combination of MWD and SRME is demonstrated as an effective demultiple package for shallow-water data and results in fewer residual multiples and better-preserved primaries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and, finally, higher quality images.