Sun, Yimin (Aramco Overseas Company) | Kim, Young Seo (Saudi Aramco) | Qu, Shan (Delft University of Technology) | Verschuur, Eric (Delft University of Technology) | Almomin, Ali (Saudi Aramco) | van Borselen, Roald (Aramco Overseas Company)
We will first point out that FWI-JMI is a better velocity model building scheme compared to FWI or JMI alone via a comparison study between FWI and JMI. Based upon this FWI-JMI velocity model, a benchmark test of ADFWM against angel gather reverse time migration (AGRTM) is further carried out, and results show that 1) ADFWM is a valid angle gather imaging scheme and 2) ADFWM is capable of providing sharper images than AGRTM due to its better exploitation of internal multiples.
Ghosts are interference events caused by the free-surface that impact the bandwidth and the resolution of the data negatively. Echo Deblending aims to remove the ghost events present in marine seismic data by separating the reflection events of interest, utilizing the realization that ghosts are produced by (virtual) secondary sources that have been generated by the free-surface. Primary and secondary source data are forward- and backward propagated using 3D wavefield extrapolation operators after which ghosts are separated by utilizing deblending techniques. In this paper, the application of 3D Echo-deblending is presented to synthetic data sets to verify and compare performance, as well as to a 3D field data set. Particular attention is paid to the implications of using sparse data in 3D wavefield extrapolation. It is demonstrated that, despite the fact that a successful 3D application only depends on the ability to extrapolate data over small vertical distances, the method still requires dense sampling, thereby relying on the ability to reconstruct missing data in the crossline direction.
Presentation Date: Wednesday, September 27, 2017
Start Time: 9:20 AM
Presentation Type: ORAL
Summary In this paper we successfully apply Robust Estimation of Primaries by Sparse Inversion (R-EPSI) on both synthetic and field 2D marine streamer datasets. The results are benchmarked against other data-driven approaches including iterative Surface-Related Multiple Elimination (SRME) and Estimation of Primaries by Sparse Inversion (EPSI). Results also show the potential of future industrial applications. Introduction Over the last several decades methods based on the wave equation that exploit multiple prediction, followed by subtraction have been developed. They can be divided into model-based methods (see e.g.
In this paper, a method is presented to compute 3D surface-related multiples using multi-component sensor data that have been acquired with streamers at variable depth. An equivalent for single sensor streamer is also presented. Results are shown for a 2D dual-sensor synthetic example, designed to highlight the potential of the method presented.
In simultaneous source acquisition, seismic data can be recorded with a temporal overlap between the shots. Better sampled data in terms of source spacing, azimuth and/or offset distributions can be obtained in a much more efficient way. These potential benefits can only be realized if the recorded data, with interfering energy from multiple sources, can be handled properly. Common practice is to apply randomized time-delays to the sources during the acquisition of the data. As a result of using randomized firing schemes, coherency measures can be utilized to actively separate the recorded data over the individual sources. In this paper an inversion-based source separation method is utilized to a shallow water data set which may have specific challenges compared to deeper water applications. We will focus a bit more on the randomized firing schemes. It is shown that optimizing these firing schemes, introducing “pseudo randomization”, instead of using random time-delays, can benefit the performance of the source separation.
The separation method is illustrated using a controlled simultaneous source experiment where a shallow water field data set is used to mimic simultaneous recorded data where two sources were located with only a small cross line distance between them (simultaneous FLIP/FLOP acquisition). Results demonstrate that it is advised to utilize “pseudo randomization” of the firing delay-times. The controlled shallow water field data example shows that good separation results are obtained.
Barnes, Simon (PGS) | van Borselen, Roald (PGS) | Salazar, Humberto (Pemex Exploration and Production) | Vàzquez, Alfredo (Pemex Exploration and Production) | Ronzón, Israel (Pemex Exploration and Production) | Martinez, Ruben (PGS)
A processing strategy for the 3D prediction and subsequent elimination of long period surface-related multiples (SRME) contaminating a 3D sparse non-orthogonal land seismic data is presented. A comparison is made between 1D and 3D multiple prediction using the Surface-related Multiple Elimination (SRME) method, showing that significant improvements can be obtained by taking into account the full 3D complexity of the subsurface. Multidimensional Fourier regularization has been proven to be a critical component of the pre-conditioning of the data applied prior to the multiple prediction and subtraction.