3D surface‐related multiple prediction using sparse inversion: Experience with field data

2002 ◽  
Author(s):  
E. J. van Dedem ◽  
D. J. Verschuur
Keyword(s):  
Field Data ◽  
3D Surface ◽  
Sparse Inversion ◽  
Multiple Prediction

scholarly journals A perspective on 3D surface-related multiple elimination

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A245-75A261 ◽  
Author(s):  
Bill Dragoset ◽  
Eric Verschuur ◽  
Ian Moore ◽  
Richard Bisley
Keyword(s):  
Spatial Distribution ◽  
Field Data ◽  
Data Sets ◽  
Data Interpolation ◽  
It Use ◽  
Data Sparseness ◽  
Multiple Attenuation ◽  
3D Surface ◽  
Multiple Prediction ◽  
Multiple Elimination

Surface-related multiple elimination (SRME) is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this “freedom from the subsurface,” SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and true-azimuth SRME. Field data examples confirm that (1) multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and (2) on-the-fly interpolation produces excellent results.

scholarly journals 3D surface-related multiple prediction: A sparse inversion approach

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. V31-V43 ◽  
Author(s):  
E. J. van Dedem ◽  
D. J. Verschuur
Keyword(s):  
Input Data ◽  
Synthetic Data ◽  
Data Set ◽  
3D Surface ◽  
Parametric Inversion ◽  
Sparse Inversion ◽  
Marine Data ◽  
Multiple Prediction ◽  
Multiple Elimination

The theory of iterative surface-related multiple elimination holds for 2D as well as 3D wavefields. The 3D prediction of surface multiples, however, requires a dense and extended distribution of sources and receivers at the surface. Since current 3D marine acquisition geometries are very sparsely sampled in the crossline direction, the direct Fresnel summation of the multiple contributions, calculated for those surface positions at which a source and a receiver are present, cannot be applied without introducing severe aliasing effects. In this newly proposed method, the regular Fresnel summation is applied to the contributions in the densely sampled inline direction, but the crossline Fresnel summation is replaced with a sparse parametric inversion. With this procedure, 3D multiples can be predicted using the available input data. The proposed method is demonstrated on a 3D synthetic data set as well as on a 3D marine data set from offshore Norway.

scholarly journals R-EPSI and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: a 2D case study in the Arabian Gulf

Geophysics ◽  
2020 ◽  
pp. 1-62
Author(s):  
Myrna Staring ◽  
Marcin Dukalski ◽  
Mikhail Belonosov ◽  
Rolf Baardman ◽  
Jewoo Yoo ◽  
...  
Keyword(s):  
Shallow Water ◽  
Adaptive Filters ◽  
A Priori ◽  
Arabian Gulf ◽  
Water Layer ◽  
Prediction Errors ◽  
Marchenko Equation ◽  
Sparse Inversion ◽  
Internal Multiples ◽  
Multiple Prediction

Suppression of surface-related and internal multiples is an outstanding challenge in seismic data processing. The former is particularly difficult in shallow water, whereas the latter is problematic for targets buried under complex, highly scattering overburdens. We propose a two-step, amplitude- and phase-preserving, inversion-based workflow, which addresses these problems. We apply Robust Estimation of Primaries by Sparse Inversion (R-EPSI) to suppress the surface-related multiples and solve for the source wavelet. A significant advantage of the inversion approach of the R-EPSI method is that it does not rely on an adaptive subtraction step that typically limits other de-multiple methods such as SRME. The resulting Green's function is used as input to a Marchenko equation-based approach to predict the complex interference pattern of all overburden-generated internal multiples at once, without a priori subsurface information. In theory, the interbed multiples can be predicted with correct amplitude and phase and, again, no adaptive filters are required. We illustrate this workflow by applying it on an Arabian Gulf field data example. It is crucial that all pre-processing steps are performed in an amplitude preserving way to restrict any impact on the accuracy of the multiple prediction. In practice, some minor inaccuracies in the processing flow may end up as prediction errors that need to be corrected for. Hence, we decided that the use of conservative adaptive filters is necessary to obtain the best results after interbed multiple removal. The obtained results show promising suppression of both surface-related and interbed multiples.

Internal multiple prediction using inverse scattering series withsparsity promotionPart II: Application strategy and field data examples

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Ole Edvard Aaker ◽  
Adriana Citlali Ramírez ◽  
Emin Sadikhov
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Norwegian Sea ◽  
Prediction Quality ◽  
Inverse Scattering Series ◽  
Exploration And Production ◽  
Internal Multiples ◽  
Multiple Prediction ◽  
Seismic Images

Incorrect imaging of internal multiples can lead to substantial imaging artefacts. It is estimatedthat the majority of seismic images available to exploration and production companies have had nodirect attempt at internal multiple removal. In Part I of this article we considered the role of spar-sity promoting transforms for improving practical prediction quality for algorithms derived fromthe inverse scattering series (ISS). Furthermore, we proposed a demigration-migration approach toperform multidimensional internal multiple prediction with migrated data and provided a syntheticproof of concept. In this paper (Part II) we consider application of the demigration-migration approach to field data from the Norwegian Sea, and provide a comparison to a post-stack method (froma previous related work). Beyond application to a wider range of data with the proposed approach,we consider algorithmic and implementational optimizations of the ISS prediction algorithms tofurther improve the applicability of the multidimensional formulations.

Borehole-Driven 3D Surface Seismic Data Processing Using DAS-VSP Data

2021 ◽  
Author(s):  
Gang Yu ◽  
Junjun Wu ◽  
Yuanzhong Chen ◽  
Ximing Wang
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Data Processing ◽  
3D Surface ◽  
Data Volume ◽  
Vsp Data ◽  
Seismic Data Acquisition ◽  
Anisotropy Parameters

Abstract A 3D surface seismic data acquisition project was conducted simultaneously with 3D DAS-VSP data acquisition in one well in Jilin Oilfield of Northen China. The 3D surface seismic data acquisition project covered an area of 75 km2, and one borehole (DS32-3) and an armoured optical cable with high temperature single mode fiber were used to acquire the 3D DAS-VSP data simultaneously when the crew was acquiring the 3D surface seismic data. The simultaneously acquired 3D DAS-VSP data were used to extract formation velocity, deconvolution operator, absorption, attenuation (Q value), anisotropy parameters (η, δ, ε) as wel as enhanced the surface seismic data processing including velocity model calibration and modification, static correction, deconvolution, demultiple processing, high frequency restoration, anisotropic migration, and Q-compensation or Q-migration. In this project, anisotropic migration, Q-migration was conducted with the anisotropy parameters (η, δ, ε) data volume and enhanced Q-field data volume obtained from the joint inversion of both the near surface 3D Q-field data volume from uphole data and the mid-deep layer Q-field data volume from all available VSP data in the 3D surface seismic surveey area. The anosotropic migration and Q-migration results show much sharper and focussed faults and and clearer subsutface structure.

Enhancing internal multiple prediction by using the inverse scattering series: methodology and field application

Geophysics ◽  
2021 ◽  
pp. 1-70
Author(s):  
Jing Wu ◽  
Zhiming Wu ◽  
Frederico Xavier de Melo ◽  
Cintia Mariela Lapilli ◽  
Clément Kostov ◽  
...  
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Field Application ◽  
Nearest Neighbor Search ◽  
Opening Angle ◽  
Model Quality ◽  
Inverse Scattering Series ◽  
Multiple Prediction

We introduce four approaches that dramatically enhance the application of the inverse scattering series method for field data internal multiple prediction. The first approach aims to tackle challenges related to input data conditioning and interpolation. We addressed this through an efficient and fit-for-purpose data regularization strategy, which in this work was a nearest-neighbor search followed by differential moveout to accommodate various acquisition configurations. The second approach addresses cost challenges through applying angle constraints over both the dip angle and opening angle, reducing computational cost without compromising the model’s quality. We also propose an automatic solution for parameterization. The third approach segments the prediction by limiting the range of the multiple’s generator, which can benefit the subsequent adaptive subtraction. The fourth approach works on improving predicted model quality. The strategy includes correctly incorporating the 3D source effect and obliquity factor to enhance the amplitude fidelity of the predicted multiples in terms of frequency spectrum and angle information. We illustrate challenges and report on the improvements in cost, quality or both from the new innovative approaches, using examples from synthetic data and from three field data 2D lines representative of shallow and of deep water environments.

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