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3D data-domain reflection tomography for initial velocity model building using challenging 3D seismic data

A. BAKULIN1 ,  I. SILVESTROV1 ,  D. NEKLYUDOV2 ,  K. GADYLSHIN2 ,  M. PROTASOV2
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1 EXPEC Advanced Research Center, Saudi Aramco, Building 137, Dhahran 31311, Saudi Arabia.,
2 Institute of Petroleum Geology and Geophysics SB RAS, Academician Koptyug ave. 3, 630090 Novosibirsk, Russia.,
JSE 2021, 30(5), 419–446;
Submitted: 15 May 2020 | Accepted: 15 February 2021 | Published: 1 October 2021
© 2021 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Bakulin, A., Silvestrov, I., Neklyudov, D., Gadylshin, K. and Protasov, M., 2021. 3D data-domain reflection tomography for initial velocity model building using challenging 3D seismic data. Journal of Seismic Exploration, 30: 419-446. We present a novel workflow to build a reliable initial velocity depth model from challenging seismic data. This workflow is based on automated 3D grid reflection tomography that utilizes coherent poststack and prestack reflection events in the data domain. The workflow consists of two parts: data preconditioning and nonlinear tomographic inversion. Data preconditioning is underpinned by robust data-driven prestack data enhancement in the form of 3D nonlinear beamforming. Operating directly in the data domain, we obtain robust NMO velocities and pick main reflection events on stacked time images. Ray-based tomographic inversion fits prestack traveltimes approximated by hyperbolae using the engine of standard grid reflection tomography. Powerful prestack enhancement, combined with regularization of observed traveltimes by hyperbolae, delivers a robust and computationally efficient approach to reconstructing the velocity depth model directly in the data domain during the early stages of seismic processing. The new approach enables iterative depth processing critical for low signal-to-noise ratio data such as land seismic with small field arrays or single sensors. We present the tomographic workflow details and showcase the method’s capabilities using synthetic and real data examples.

Keywords
reflection tomography
inverse problem
land seismic data
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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