Velocity estimation using internal multiples

Staal, X.R.; Verschuur, D.J.

doi:10.1190/segam2012-1580.1

Cited by 20 publications

(3 citation statements)

References 9 publications

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“…Joint migration inversion (JMI) was proposed as one of the methods to overcome the above‐mentioned limitations in full‐waveform inversion (FWI) Berkhout (); Staal and Verschuur (, ); Berkhout (); Verschuur, Staal and Berkhout (). The main engine of JMI is a forward modelling called full wavefield modelling (FWMod), which is based on parameters reflectivity and propagation velocity Berkhout ().…”

Section: Theory Of Joint Migration Inversionmentioning

confidence: 99%

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Verschuur

2020

Geophysical Prospecting

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A B S T R A C TThe current time-lapse practice is to exactly repeat well-sampled acquisition geometries to mitigate acquisition effects on the time-lapse differences. In order to relax the rigid requirements on acquisition effects, we propose simultaneous joint migration inversion as an effective time-lapse tool for reservoir monitoring, which combines a joint time-lapse data processing strategy with the joint migration inversion method. Joint migration inversion is a full-wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivity and propagation velocity. Both the inversion process inside the imaging/inversion scheme and the extra illumination provided by including multiples in joint migration inversion makes the obtained velocity and reflectivity operator largely independent of the utilized acquisition geometry and, thereby, relaxes the strong requirement of non-repeatability during the monitoring. Because simultaneous joint migration inversion inverts for all datasets simultaneously and utilizes various constraints on the estimated reflectivities and velocity, the obtained time-lapse differences have much higher accuracy compared to inverting each dataset separately. It allows the baseline and monitor parameters to communicate with each other dynamically during inversion via a userdefined spatial weighting operator. In order to get more localized time-lapse velocity differences, we further extend the regular simultaneous joint migration inversion to a robust high-resolution simultaneous joint migration inversion process using the timelapse reflectivity difference as an extra constraint for the velocity estimation during inversion. This constraint makes a link between the reflectivity-and the velocity difference by exploiting the relationship between them. We demonstrate the feasibility of the proposed method with a highly realistic synthetic model based on the Grane field offshore Norway and a time-lapse field dataset from the Troll Field.

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Section: Theory Of Joint Migration Inversionmentioning

confidence: 99%

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Verschuur

2020

Geophysical Prospecting

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“…In the JMI iterations, the B-scattering term δW ± P ± j is automatically determined together with the other scattering terms (representing 'A-scattering'), making JMI a combination of closed-loop full wavefield imaging and closed-loop full wavefield tomography (Staal and Verschuur 2012). Note that δW ± gives δT ± a travel-time component.…”

Section: Velocity Estimationmentioning

confidence: 99%

“…Note that δW ± gives δT ± a travel-time component. In the JMI iterations, the B-scattering term δW ± P ± j is automatically determined together with the other scattering terms (representing 'A-scattering'), making JMI a combination of closed-loop full wavefield imaging and closed-loop full wavefield tomography (Staal and Verschuur 2012). It is important to realize that JMI does not directly estimate the velocity distribution, but it updates the local propagation operators (from W 0 to W) first.…”

Section: Velocity Estimationmentioning

confidence: 99%

Utilization of multiple scattering: the next big step forward in seismic imaging

Berkhout

2016

Geophysical Prospecting

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Surface removal and internal multiple removal are explained by recursively separating the primary and multiple responses at each depth level with the aid of wavefield prediction error filtering. This causal removal process is referred to as “data linearization.” The linearized output (primaries only) is suitable for linear migration algorithms. Next, a summary is given on the migration of full wavefields (primaries + multiples) by using the concept of secondary sources in each subsurface gridpoint. These secondary sources are two‐way and contain the gridpoint reflection and the gridpoint transmission properties. In full wavefield migration, a local inversion process replaces the traditional linear imaging conditions. Finally, Marchenko redatuming is explained by iteratively separating the full wavefield response from above a new datum and the full wavefield response from below a new datum. The redatuming output is available for linear migration (Marchenko imaging) or, even better, for full wavefield migration. Linear migration, full wavefield migration, and Marchenko imaging are compared with each other. The principal conclusion of this essay is that multiples should not be removed, but they should be utilized, yielding two major advantages: (i) illumination is enhanced, particularly in the situation of low signal‐to‐noise primaries; and (ii) both the upper side and the lower side of reflectors are imaged. It is also concluded that multiple scattering algorithms are more transparent if they are formulated in a recursive depth manner. In addition to transparency, a recursive depth algorithm has the flexibility to enrich the imaging process by inserting prior geological knowledge or by removing numerical artefacts at each depth level. Finally, it is concluded that nonlinear migration algorithms must have a closed‐loop architecture to allow successful imaging of incomplete seismic data volumes (reality of field data).

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Imaging blended vertical seismic profiling data using full-wavefield migration in the common-receiver domain

Soni

Verschuur

2015

GEOPHYSICS

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For vertical-seismic-profiling (VSP) measurements, the use of blended acquisition, with time-overlapping shot records, can greatly reduce the downtime and, thereby, provide large cost savings. For directly imaging blended VSP measurements, we have used full-wavefield migration (FWM). FWM is an inversion-based imaging scheme that enables us to use any kind of complex source wavefield to estimate the subsurface reflectivity, using all the multiples (surface and internal) in the recorded data. The multiple scattering helps in improving the illumination as well as the vertical resolution of the image. In this scheme, active deblending is not required because the imaging process itself acts as a deblending procedure. We tested the potential of FWM to image blended VSP data, using simple and complex synthetic models. We clearly determined that using the primaries, surface multiples, and internal multiples enhanced the illumination away from the well trajectory, in which blending noise was suppressed due to the inversion scheme. We observed that some blending crosstalk noise leaked into the images with large blending factors. Such noise could be further reduced with additional constraints in the involved least-squares inversion process.

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Velocity estimation using internal multiples

Cited by 20 publications

References 9 publications

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Utilization of multiple scattering: the next big step forward in seismic imaging

Imaging blended vertical seismic profiling data using full-wavefield migration in the common-receiver domain

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