Basic formulae for multifocusing stack

Berkovitch, A.; Gelchinsky, B.; Keydar, S.

doi:10.3997/2214-4609.201410089

Cited by 22 publications

(6 citation statements)

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“…On the other hand, Tygel et al (1997) reveal equation (1)'s close relationship to the multifocusing operator of Berkovitch et al (1994).…”

Section: Crs Stacking Operatormentioning

confidence: 95%

“…Real data examples of the CRS stack and other related macromodel independent zerooffset (ZO) simulation methods involving different formulas (but the same three attributes α, R NIP , and R N ) such as multifocusing (Gelchinsky, 1989;Berkovitch et al, 1994;Landa et al, 1999) or polystack (de Bazelaire, 1988;de Bazelaire and Viallix, 1992;Thore et al, 1994;Höcht et al, 1999) were presented at the indicated workshop. More detailed investigations of the CRS stack and its performance on real 2-D seismic datasets can be found in Mann et al (1999) and Müller (1999).…”

Section: Introductionmentioning

confidence: 99%

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Common‐reflection‐surface stack: Image and attributes

et al. 2001

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The common-reflection-surface stack provides a zerooffset simulation from seismic multicoverage reflection data. Whereas conventional reflection imaging methods (e.g. the NMO/dip moveout/stack or prestack migration) require a sufficiently accurate macrovelocity model to yield appropriate results, the common-reflectionsurface (CRS) stack does not depend on a macrovelocity model.We apply the CRS stack to a 2-D synthetic seismic multicoverage dataset. We show that it not only provides a high-quality simulated zero-offset section but also three important kinematic wavefield attribute sections, which can be used to derive the 2-D macrovelocity model. We compare the multicoverage-data-derived attributes with the model-derived attributes computed by forward modeling. We thus confirm the validity of the theory and of the data-derived attributes.For 2-D acquisition, the CRS stack leads to a stacking surface depending on three search parameters. The optimum stacking surface needs to be determined for each point of the simulated zero-offset section. For a given primary reflection, these are the emergence angle α of the zero-offset ray, as well as two radii of wavefront curvatures R N and R NIP . They all are associated with two hypothetical waves: the so-called normal wave and the normal-incidence-point wave. We also address the problem of determining an optimal parameter triplet (α, R NIP , R N ) in order to construct the sample value (i.e., the CRS stack value) for each point in the desired simulated zero-offset section. This optimal triplet is expected to determine for each point the best stacking surface that can be fitted to the multicoverage primary reflection events.To make the CRS stack attractive in terms of computational costs, a suitable strategy is described to determine the optimal parameter triplets for all points of the simulated zero-offset section. For the implementation of the CRS stack, we make use of the hyperbolic second-order Taylor expansion of the stacking surface. This representation is not only suitable to handle irregular multicoverage acquisition geometries but also enables us to introduce simple and efficient search strategies for the parameter triple. In specific subsets of the multicoverage data (e.g., in the common-midpoint gathers or the zero-offset section), the chosen representation only depends on one or two independent parameters, respectively.

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“…On the other hand, Tygel et al (1997) reveal equation (1)'s close relationship to the multifocusing operator of Berkovitch et al (1994).…”

Section: Crs Stacking Operatormentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Common‐reflection‐surface stack: Image and attributes

et al. 2001

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“…The MultiFocusing method (MF) was proposed by Berkovitch et al (1994) and it consists of constructing a zero-offset section wherein each trace of this section is computed from prestack traces arbitrarily located around an imaging position. The moveout correction does not require knowledge about the subsurface and is valid for arbitrary observation geometry.…”

Section: Moveout Multifocusing Correction For Diffracted Wavesmentioning

confidence: 99%

Using Multi-focusing 3D Diffraction Imaging to Predict Fracture Corridors/Swarms in the Bazhenov Formation

Landa¹,

Rauch-Davies²,

Berkovitch³

et al. 2013

London 2013, 75th Eage Conference en Exhibition Incorporating SPE Europec

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Unconventional reservoirs are becoming more and more conventional but successful drilling within these reservoirs reservoirs has a unique set of problems. Most wells are drilled horizontally through the reservoir rock and the fracking technology is applied to generate permeabilty and produce hydrocarbons. The pre-drill knowledge of natural fracture swarms and small offset faults is very important as these geological elements can interfere with the drilling and fracking process and influence the production rate. Seismic resolution from conventional reflection imaging is generally not sufficient to resolve these small scale rock properties.Diffracted waves are events that are produced by the scattering of a wave after it meets a discontinuity such as fracture swarms, small amplitude faults and karsts that cause local sharp changes in the geometrical or lithological characteristics. A method for diffraction imaging that is based on coherent summation of diffracted waves was applied to a 3D data set over an unconventional oil reservoir. An integrated study that includes well information, diffraction energy and seismic attributes showcases the usefulness of diffraction events to predict fracture swarms within the Bazhenov formation, which is a black shale in West Siberia.

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“…The representation of the stacking surface can be chosen in several ways. Depending on the derivation of the traveltime approximation, either geometrical or by means of the ray propagator formalism of paraxial ray theory, one obtains double-square-root expressions like in Multifocusing (Berkovitch et al, 1994;Landa et al, 1999) or different kinds of quadrics of parabolic and hyperbolic form (Schleicher et al, 1993;Höcht et al, 1999). Although some of these expressions are exact for very simple situations, it is not evident which of these alternatives generally leads to the best possible results.…”

Section: Basic Conceptsmentioning

confidence: 99%

Data-Driven Imaging with Second-Order Traveltime Approximations

Mann¹,

Zhang²

2003

EAGE/SEG Summer Research Workshop - Processing and Imaging of Seismic Data

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Starting with the general concept of second-order traveltime approximations for seismic reflection imaging, we discuss the number of determinable parameters, i. e., spatial derivatives of the traveltime, for different acquisition geometries, e. g., narrow azimuth 3-D marine data or full azimuth 3-D data, and processing approaches ranging from the most general multi-parameter problem down to the wellknown single-parameter approach used in the commonmidpoint stack. With the help of the near-surface velocity (or both, P and S wave velocities in case of converted waves), these derivatives can be related to spatial properties of various kinds of hypothetical wavefronts observed at the acquisition surface. This constitutes the basis for a number of recently introduced data-driven imaging methods that differ in the derivation and representation of their traveltime approximations, but not in their general principles. Our aim is to present the current state of the art of one of the available implementations, the Common-Reflection-Surface Stack, followed by two application examples that demonstrate the advantages of the formulation in terms of wavefront properties.

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Basic formulae for multifocusing stack

Cited by 22 publications

References 0 publications

Common‐reflection‐surface stack: Image and attributes

Common‐reflection‐surface stack: Image and attributes

Using Multi-focusing 3D Diffraction Imaging to Predict Fracture Corridors/Swarms in the Bazhenov Formation

Data-Driven Imaging with Second-Order Traveltime Approximations

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