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**Summary**

Spatial resolution is associated with the temporal resolution, but mat be limited due to “diffraction” aperture or inaccurate velocities. Velocity errors occur when data are processed to a datum in violation of the hyperbolic assumption. These errors may be very small and are assumed to be negligible, especially with CMP processing. Prestack migrations gather data from many CMP gathers, and any relative velocity errors degrade the spatial resolution. We demonstrate this spatial resolution loss and recovery using a real 2D data set that contains faulting events. In addition, the resolution of the faults may be further focused, depending on their angle of obliquity to the 2D line.

**Introduction**

High spatial resolution data for a research project was acquired in the Hussar area of Alberta Canada. The sedimentary layers in the area are relatively horizontal, with a surface elevation that had a range of 100 m. A vertical component of the data was extracted and conventionally processes with a standard prestack migration. The results were typical of the area and displayed no faulting.

The data were also processed to form common scatterpoint (CSP) gathers, prestack migration gathers that are formed without moveout correction, (Bancroft et.al. 1994 and 1998). Velocity analysis of each gather provides a unique velocity at each CMP location. Moveout correction, scaling, muting, and stacking produced a prestack migration that appeared to contain faulted structure. The 2D data was further analyzed to evaluate the obliquity of the fault planes, relative to the angle of the 2D line, by modifying the velocities. These results showed improved focusing of the fault planes, identifying the angles of obliquity. It should be noted that vertical displacement across the faults is very small, but the character of the reflection changes significantly across the fault as demonstrated in Figure 1a. Note the character change between CMP 536 at 636 at 2 sec. near the bottom of the figure. This data was processed to a maximum time of 4.0 sec. Figure 1b shows the same area, processed with a poststack finite difference migration to a maximum time of 2.0 secs.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

**Summary**

The resolution of seismic data can be significantly improved after migration. This can be achieved with a simple trace deconvolution in areas with a simple geology such as a sedimentary basin, or a more complex deconvolution if the structure is complex. There are considerable objections to this process; some are identified and discussed, then reasons for its use are presented. Two examples of deconvolution after migration are presented.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

**Summary**

Different acquisition geometries of the baseline and monitor seismic surveys produce different patterns of acquisition footprints. The resulting time lapse image shows the differences in artifacts, which may dominate the changes in the reflectivity model due to the production from or injection into the reservoirs. Synthetic data is used to show how different acquisition geometries between baseline and monitor surveys lead to different Kirchhoff migration artifacts for the same reflectivity model.

The least squares prestack Kirchhoff migration (LSPSM) is performed separately on the baseline and monitor data to attenuate these effects and provide comparable high resolution images for both pre- and poststack time lapse studies. A joint least squares Kirchhoff prestack migration (LSPSM) of both baseline and monitor data is introduced which attenuates the migration artifacts and returns high resolution LSPSM and/or time lapse images.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Bancroft, John C. (CREWES) | Guirigay, Thais (CREWES) | Isaac, Helen (CREWES)

The inversion process to recover rock properties is typically approximated with seismic migration that is a transpose process. This transpose process limits the frequency content that should be recovered. The lower and higher frequencies that are lost, can be recovered by following a migration with deconvolution.

There is opposition to applying deconvolution after migration, and we review those objections and then present two arguments to validate this proposition. The improvement in resolution is illustrate using a simple single trace spiking deconvolution. We propose that additional improvements can be achieved using a more sophisticated deconvolution that incorporates the dip of an event.

Many seismic datasets are recorded over geologic structures where lateral changes in the physical properties of the stratigraphic layers vary smoothly. For these situations, depth migration algorithms are not required and time migration imaging is known to provide a similar outcome and is more economic. In this paper, we discuss the implementation of the Full Waveform Inversion (FWI) algorithms for velocity inversion using Common Scatter Point (CSP) gathers. Since the formation of the CSP gathers are based on the Pre-Stack Kirchhoff Time Migration (PSTM), we reduce the computational effort commonly associated with depth migration.

Guirigay, Thais (CREWES) | Bancroft, John C. (CREWES) | Isaac, Helen (CREWES)

new approach is presented for estimating the velocities of converted wave data that is based on prestack migration by equivalent offset to form common conversion point gathers. These gathers are used to form an initial estimate of the converted wave velocity

Equivalent offset common conversion point gathers are formed using the P-wave and S-wave velocities and the double-square-root equation. The formation of these gathers requires approximate values for the P-wave and S-wave velocities, but after their formation, accurate velocities can be picked and the prestack migration completed with moveout correction.

Yousefzadeh, Abdolnaser (CREWES) | Bancroft, John C. (CREWES)

Kirchhoff least squares prestack migration (LSPSM) attenuates acquisition artifacts resulted from the irregularities or sparseness in the seismic data sampling and improves the image resolution. This study shows that this improvement needs an accurate subsurface velocity information. It is shown that the improvement in the resolution of the resulted LSPSM, convergence rate of the least squares conjugate gradient (LSCG) iterations, and the ability of a good data reconstruction by LSPSM are the three factors that strongly depend on the accuracy of the background velocity and can be used as effective tools for ensuring the accuracy of the velocity model.

Equivalent Offset Migration (EOM) is based on the pre-stack Kirchhoff time migration (PSTM) method. It first maps the energies of the scatter points onto an intermediate Common Scatter Point (CSP) gathers, then after successfully applying a Normal Move Out (NMO) correction will output the migrated image. Assuming negligible lateral velocity gradient, the CSP data are sorted along the normal hyperbolic paths and serve as a useful tool for velocity inversion. The scatter point response below the dipping interface is a tilted hyperbola. Using the constructed wavefront we established the relationship between the tilted and normal hyperbolae. Similar relationship is obtained by simulation of CSP responses. We improved the focusing of the separated energy in the semblance plots by removing the tilt effects. As a result, the accuracy of migration velocity inversion enhanced and the focusing of output image of time migration are improved.

array, arrival time, Artificial Intelligence, coordinate, evolutionary algorithm, Figure, geophone, hypocenter, hypocenter location, inversion, machine learning, method, Nonlinear Optimization, objective function, optimization problem, production control, production monitoring, reservoir simulation, search, source, velocity

SPE Disciplines:

Technology:

- IT > AI > Representation & Reasoning > Search (1.00)
- IT > AI > Representation & Reasoning > Optimization (1.00)