General processing issues

The ideal dataset for AVO analysis would have the following characteristics:

zero phase data

clear continuity of reflections across the gather
adequate offset/angle sampling

good multiple and noise attenuation
flat gathers (correct moveout velocities and

algorithm)

correct relative amplitude across the gather
consistent frequency content across the gather

(no moveout stretch)

consistent scaling of each trace (high source and receiver repeatability)

no critical or post-critical energy
correct pre-stack imaging
high bandwidth.

Sometimes the evaluation of data quality is straight- forward simply from inspection of stacked data. For example, Fig. 6.1 illustrates a full stack comparison

between 3D surface streamer and ocean bottom cable (OBC) data where it is clear that the data quality of the OBC data is superior. Often, however, data effects are subtle and require pre-stack data displays for their evaluation. Figure 6.2 shows an example of residual moveout that can only effectively be appreciated in the pre-stack domain. The presence of this type of residual moveout can adversely impact the AVO gra- dient estimation.

6.2.1 Initial amplitude corrections

6.2.1.1 Divergence correction

One of the first processes applied to seismic data, which is fundamental to the general behaviour of amplitude with offset, is a scaling step to correct for the‘spherical’ divergence phenomenon, described in

Chapter 2. The effect is quite large, and is corrected for using a simple function of two-way-time (TWT) and stacking velocity. This correction is based on an approximation and is unlikely to be entirely accurate, particularly if there is anisotropy in the overburden. Anisotropy can cause significant modification of geo- metric spreading with angle (Stovas and Ursin,2009) and inadequate correction will distort the amplitude variation with offset. When using AVO for quantita- tive interpretation this effect has to be corrected prior to AVO analysis by applying some form of offset- related scaling (seeSection 6.3.3).

a)

3D Marine streamer

b)

3D OBS

Figure 6.1 Comparison of marine surface towed streamer data with ocean bottom seismic (OBS) data (after Thompson_{et al.,}2007).

3800

6

16 26

36 46

3900

4000

4100

4200

Residual

NMO

Incidence angle (degrees)

Time (ms)

Near Mid Far Ultra Far

Figure 6.2 Angle gather showing residual moveout (after Contreraset al.,2007).

6.2.1.2 Surface consistent corrections

AVO analysis is performed on a gather of traces that have a common reflection point but are acquired from different shot–receiver pairs. Clearly, the effect of shot-to-shot variation in signal strength and vari- ations in receiver sensitivity needs to be removed. In the marine case, these variations are likely to be fairly small. Air guns emit highly reproducible signals and receivers are manufactured to close tolerances, and both source and receiver are surrounded by seawater the properties of which do not usually vary laterally. In the land case, the problem is much more serious. Sources and receivers are still manufactured to give uniform performance, but coupling to the subsurface is strongly affected by the nature of the near-surface layering. Sometimes it is possible to achieve fairly uniform conditions, for example by burying geo- phones or by sweeping loose material away so that a Vibroseis source rests on solid rock, but often this is not practical and corrections need to be applied in processing. In general, seismic amplitudes are more reliable for marine than for land data.

The near-surface effects are dealt with by surface- consistent corrections (Taner and Koehler,1981). The idea is that near-surface effects associated with a par- ticular surface position remain constant regardless of the raypath involved. Thus, source strength will affect all of the traces recorded from that source, and geo- phone coupling will be the same for all traces recorded at a particular receiver station from different source positions. Unwanted surface-consistent factors can be split into three categories: source response, receiver response, and an offset-related response which takes account of directivity in source or receiver arrays. Owing to the possibility of modifying first order offset amplitude changes related to geology it is seldom advised to apply corrections for energy directivity. In most instances corrections are applied to source and receivers only. Since there are traces for many source–receiver pairs, the source and receiver responses can be calculated in a least-squares sense. The process is analogous to calculating shot and receiver statics and, in a similar way, is quite effective at dealing with short-wavelength variations.

6.2.2 Long-wavelength overburden effects

A key issue for the interpreter to appreciate prior to amplitude interpretation is variation in amplitudes at the level of interest that may be the result of

propagation effects from shallower rock layers. For example, Fig. 6.3 shows a section from a gas field offshore Indonesia (Rudiana et al., 2008), where a shallow gas accumulation strongly attenuates seismic amplitudes below it, giving rise to an amplitude ‘shadow’. High impedance layers such as basalts and thick carbonate layers can have similar effects. The extent of amplitude shadows will vary with offset, depending on how the source and receiver paths are affected. Important factors are source–receiver dis- tance as well as the depth and lateral extent of the attenuating layer. The effects on measured AVO at a target horizon can be complicated; near traces will almost certainly be affected but it is possible that at long offsets the raypaths may fully or partially under- shoot the feature. This means that in the affected area it will not be possible to relate AVO on a deep reflector to properties of the interface concerned without carry- ing out some form of data normalisation.

For simple amplitude mapping, a possible approach is to equalise amplitudes over a long gate by means of automatic gain control (AGC). This aims to scale the seismic data so that the average RMS amplitude in a window is the same across all traces, and assumes that the average reflectivity is invariant. This is dangerous if applied over a short window, potentially destroying lateral amplitude variations of interest. If applied over a long window (e.g. 1.5–2 s TWT), however, it can remove much of the shadowing effect of the overburden without dam- aging lateral amplitude variations in a target reflector. This solution can be applied to partial offset stacks, but it will obviously affect the intercept vs gradient relationships determined from the stacks. Long gate AGC is dubious for angle stacks because the offsets (and the degree of undershooting) contributing to any one trace will vary with TWT.

1700 1600 1500 1400 1300 1200 Time (ms)

Figure 6.3 Example of amplitude shadow caused by shallow gas (after Rudianaet al.,2008).

A different solution would be to normalise the amplitude map at target level by dividing it by the amplitude at a reflector above or below the target that is not expected, on geological grounds, to show lateral variability. The reference horizon needs to be fairly close in TWT to the target so that raypaths through the attenuating layer are the same for both. None of these corrections can be applied with any confidence when shadowing is severe. In such a case it might be better to recognise that it is simply not possible to make a reliable interpretation of amplitude data.

6.2.3 Multiple removal

Most seismic datasets require some processing to remove multiples. A common method is Radon demultiple, first introduced by Thorson and Claerb- out (1985), which discriminates between primaries and multiples on the basis of moveout behaviour (i.e velocity differences arising from differences in ray- paths). After applying moveout corrections to a CDP gather, the primaries will be flat and the multiples will in general show residual moveout. The Radon trans- form maps the data into a time-curvature domain, in which the primaries will have near-zero curvature compared to multiples which will have larger curva- ture (e.g. Kabir and Marfurt, 1999). Unfortunately, owing to the limited offset range available, the change in frequency content and amplitudes varying with

offset, there is often partial overlap between primaries and multiples in the transformed data. This can lead to residual multiple energy being left in the data, especially on the near offsets, after muting the mul- tiples. A partial solution is provided by the high- resolution Radon transform which aims to find the sparsest representation of the input in the Radon domain (Fig. 6.4), leading to less smear and thus less of a problem with the near offsets. Residual multiple energy on the near offsets is potentially a problem for the offset scaling comparisons that will be described shortly; it would not be correct to scale down the amplitude of the near traces if their higher than expected amplitude was due solely to residual mul- tiple energy.

Alternative techniques of multiple elimination are available, such as ‘surface-related multiple elim- ination’ (SRME) (e.g. Dragoset et al., 2010). This technique predicts surface-related multiples by a convolution process and does not require a velocity field for its application. Once predicted, the mul- tiples are removed by a filtering process called‘adap- tive subtraction’, which attempts to match the modelled multiples to the data (Guitton and Verschuur,2004). Unfortunately, the sparse distribu- tion of sources and receivers available in real datasets does not generally conform to the dense surface sampling required by the algorithm to predict mul- tiples accurately, but good results can be obtained by

5.6 4.0 1000 2000 Offset (m) Time (s) 3000 4000 4.4 4.8 5.2 5.6 4.0 1000 2000 Offset (m) Time (s) 3000 4000 4.4 4.8 5.2 5.6 4.0 1000 2000 Offset (m) Time (s) 3000 4000 4.4 4.8 5.2

a)

b)

c)

Figure 6.4 (a) CMP gather after moveout correction, with strong multiples; (b) result after standard Radon demultiple: although the multiples have been largely removed there is residual multiple energy at near offsets which will affect the calculated AVO; (c) as (b) but now using the high-resolution Radon transform: the residual multiple energy at near offsets is much reduced (redrawn from Verschuur,2007, with permission from the CSEG Recorder).

various interpolation schemes, removing multiples without distorting relative amplitude variation across the offset range. Well synthetics, generated with and without inclusion of multiples, are a useful tool to check for the presence of residual multiples or for damage to primary reflections by over-aggressive application of demultiple techniques. In order to model seabed multiples a water layer would need to be inserted into the model.

6.2.4 Migration

In all but the simplest subsurface geometry (i.e. hori- zontal reflectors at all levels), pre-stack migration is needed for detailed AVO work. Without migration, the traces of a common midpoint (CMP) gather will contain data reflected from different subsurface loca- tions and these will not in general be positioned directly below the common midpoint. The purpose of migration is to process the signal collected from many surface locations so that the gathers contain traces with reflection points vertically below the source–receiver midpoint, focusing energy and increasing lateral resolution. There are many different algorithms in use for pre-stack migration, and the choice of method depends on the complexity of the structure of both target and overburden. Jones (2010) provides a useful review and discussion. For AVO, an issue is the amplitude fidelity in the migrated gather. This is partly determined by the type of algorithm used, but also by its detailed implementation; where possible, this needs to be discussed with the seismic processor in the context of the intended use of the gathers.

6.2.5 Moveout correction

Given the importance for AVO analysis of time registration of reflectors across the gather, accurate moveout correction is necessary. For an isolated interface, this means that the reflector should be flat across the gather enabling, for example, the calcula- tion of a reasonably accurate AVO gradient at the same TWT on every trace.

6.2.5.1 Problems in residual moveout identification

Whilst flat gathers are generally a requirement for AVO, evaluating the ‘flatness’ of gathers may not always be straightforward. For example, if there are multiple interfaces in a stack of thin beds, then inter- ference effects (tuning) will change across the gather

as the individual interface reflection coefficients change with angle, and individual peaks and troughs will not necessarily line up exactly horizontally. Class IIp AVO effects, with a polarity flip at some inter- mediate offset, will similarly not give rise to a neat alignment across the offset range. In these cases, the apparent residual moveout should be the same as that seen in a well synthetic. In practice, the synthetic gives a general idea of whether apparent residual moveout is an issue, but does not give much assistance in calculating a detailed residual moveout correction (Fig. 6.5).

6.2.5.2 Higher order moveout corrections

In some cases, trying to flatten reflections across the gather using a hyperbolic equation may distort the moveout correction, particularly at mid and far offsets. In such cases the first refinement to be con- sidered is a more accurate allowance for vertical vel- ocity heterogeneity by including higher order terms in the moveout equation. The simple hyperbolic moveout approximation is

T2_X,n¼ T2₀+ X 2

V2_rms, ð6:1Þ where T0 is the travel time at zero offset and Vrmsis the RMS average velocity between the surface and the base of the nth layer. This is derived from a horizon- tally layered earth model, in which the travel time for a reflection from the base of the nth layer at a source– receiver offset x is given by (Taner and Koehler,

1969):

T2_X,n¼ c1+c2X2+c3X4+c4X6+   , ð6:2Þ where the coefficients c depend on the layer thick- nesses and the velocities in each layer.

Thus, a more accurate approximation is obtained in most cases by retaining the term in X4.

6.2.5.3 Anisotropy

Another complication for gather flattening is anisotropy. When fine scale layering (i.e. transverse isotropy, seeChapter 5) becomes important it is not possible to flatten gathers beyond an incidence angle of about 30° with standard moveout corrections. Alkhalifa (1997) addressed the effect of transverse isotropy (VTI) on the seismic travel time. For a hori- zontal interface in a homogeneous VTI medium then the travel time can be described by:

115

T2_x¼ T2₀+ X 2 V2 nmo  2ηX4 V2 nmo T20V2nmo+ 1+2ð ηÞX2
 , ð6:3Þ

where η is given in terms of Thomsen’s anisotropy parameters (seeChapter 5) by

η ¼ε  δ_1+2δ ð6:4Þ and Vnmo¼ VP0 ffiffiffiffiffiffiffiffiffiffi 1+2δ p , ð6:5Þ

where Vp0is the P wave vertical velocity.

In time processingη is usually derived from maxi- mising stack semblance after initial velocity estima- tion (e.g. Toldi et al., 1999). Figure 6.6 shows an example where the AVO response of gas sands is seen at offsets large enough to require anisotropic moveout corrections.

In principle, a combination of time and depth migration can provide information on the anisotropic parametersδ and ε that can modify the AVO response

(Chapter 5). For example, δ can be estimated itera- tively from the misties between a depth migrated image and the true depth as measured in a well (Jones,

2010), andε can then be estimated from the combin- ation of η and δ. In practice, the ε and δ functions derived from seismic are usually quite generalised and not usually applicable to the seismic amplitude mod- elling of specific target horizons. There may also be ambiguity regarding how much of theη estimation is simply accommodating the effects of vertical velocity gradients. The reader is referred to an excellent treat- ment on velocity issues in migration by Jones (2010).

6.2.6 Final scaling

The application of modern pre-stack migration and demultiple techniques usually results in pre-stack gathers that have reasonable amplitude scaling with offset. In some instances, however, particularly in land data, an evaluation of the moveout corrected gathers reveals gross scaling differences between sets of traces

50ms

a)

Zoeppritz gather

b)

Seismic gather

0º 55º

Figure 6.5 Synthetic and corresponding seismic gather. The synthetic shows a Class IIp AVO response in the upper part, in broad agreement with the real data; in the lower part the reflections are nearly horizontal in the synthetic but show residual moveout in the real data.

across the gather.Figure 6.7ashows an example from a gas reservoir where the nearest traces have ampli- tudes higher than the rest of the gather. The modelled response from wells at the top of the gas sand is Class III but the amplitude imbalance in the gather is dis- torting the AVO giving it a Class IV appearance. In this case a simple application of automatic gain control (AGC) over a large window (Fig. 6.7b) served to establish the Class III response. Note how reducing the AGC window can have the effect of reducing the AVO gradient (Fig. 6.7c). This use of AGC should rarely be necessary for modern data, but it is still likely that some adjustments to the gain across the offset range will be needed to ensure consistency of AVO with well synthetics (seeSection 6.3.3).

6.2.7 Angle gathers and angle stacks

If possible, it is usually best to carry out an AVO study starting from angle gathers. This gives total flexibility in choosing what angle range to use. In addition, it is

easier to assess the presence of multiples and other types of coherent noise by looking at the gathers rather than at angle stacks. For interpretation, angle stacks are commonly generated as they benefit from the S:N improvement of stacking whilst retaining the main features of the amplitude vs. angle behaviour.

The choice of angle range in each angle stack is determined by a balance between several factors:
it is difficult for the interpreter to work with more

than three angle stacks, for lack of space on the screen to display them side by side;

if the number of angle stacks is small, a fairly large number of pre-stack traces contribute to each stack trace, with useful noise reduction;
on the other hand, a small number of stack

volumes implies summing over quite a large angle range, with consequent smear in the AVO response.

In deciding which angle ranges to stack, the first step is to look at the gathers and define the useful

MODEL

Ray-Trace

NMO

CDP Gather

Anistropic

NMO

CDP Gather

Common

NMO

LOGS

1000ft offset per trace

_{Extended with}

Anisotropy

In document Simm & Bacon (2014) - Seismic Amplitude. an Interpreters Handbook (Page 123-128)