This document discusses an integrated workflow for pre-stack depth migration and AVO inversion on the Penguin oil field in the North Sea. Key aspects of the integrated workflow include: 1) Conducting AVO feasibility studies early in processing to help update the velocity model; 2) Using AVO diagnostics on pre-stack data and angle stacks to identify problems and improve amplitude reliability; 3) Inverting for lithology and porosity cubes to aid reservoir characterization and modeling. The integrated approach aimed to improve efficiency and impact of the entire process from seismic processing through reservoir modeling.

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Z-99 TITLE AVO INVERSION AND PROCESSING: DEDICATION AND INTEGRATIONMARCEL
ZWAAN, YVAN CHARREYRON, DAVE BATEMANSHELL EPE 1 ALTENS FARM ROAD NIGG, ABERDEEN, AB12 3FY,
U.K.EAR99 classification
Summary
In the past processing and inversion projects were often carried out consecutively and independently from each
other. Over the last few years we have strived to change this and an effort has been made to ensure the projects
are truly integrated. To this end, we have developed diagnostics that can be carried out very early in the
processing sequence, which enables us to quickly identify problem areas in the data and make decisions on how
best to address these issues.
Also, it is known that pseudo-shale volume- (V-shale) and porosity-cubes derived via simultaneous AVO
inversion can be used to mitigate uncertainties in the static reservoir model. We will show that the “goodness-of
fit” between the seismically derived shale-volume cube and recorded logs (within the seismic bandwidth) can be
incorporated as part of the QC process.
Introduction
Over the past years inversion projects were largely detached from prior processing work and not geared up to
feed directly into the reservoir model. It was realised that the impact and the efficiency of the whole process
from processing through to the static reservoir model building would benefit from a fully integrated approach
between all the component steps. The combined Pre-Stack Depth Migration and AVO inversion over the
Penguin field was one of these fully integrated projects. This paper describes the aspects and diagnostics of this
integrated workflow.
One of the key aspects that drives the quality of the simultaneous AVO inversion results is pre-stack amplitude
reliability. Because the inversion process is based on the Aki & Richards reflectivity equation, the pre-stack data
has to satisfy – in an approximate sense - this theoretical angle-dependent amplitude behaviour.
This paper discusses the techniques that are utilised to assess the AVO behaviour on the data and their impact on
the processing sequence. We also discuss the inversion result, tying this to the requirements of the field
development.
A Brief description of the Penguin field
The Penguin cluster was discovered back in 1974 and can be subdivided into 5 independent fields: Penguin A, B,
C, D & E. It produces from intra-Kimmeridge Magnus sands (Penguin A), Triassic sands (Penguin B) and from
a more classical Brent reservoir sequence (Penguin CD&E). The development of the field only started in Q4
2001when the latest technology
Penguins made it an economically viable
proposition. The field is produced
via a 65 km long flow-line tied-back
to the Brent Charlie platform
located some 50km south of the
Penguin E field. This study
concentrates on the geologically
similar Penguins C, D (light oil
bearing) and E (gas condensates
bearing) fields located
approximately 11000 ft below
Figure 1 Location and outline of the Penguin cluster. surface. Average reservoir sand
thickness varies between 130 and
225 ft, average porosity is 15% with an average Net-to-Gross ratio around 75%. The Etive sands overlay the
Rannoch sequence and together they constitute the main productive intervals, with reservoir quality generally
degrading from top to base. Since lateral and vertical variability in reservoir quality is expected, the main scope
of the inversion project is to define the extent of the good reservoir layers.
EAGE 66th – W5 - What pre-stack data and processing do we need for reservoir characterization —
Paris, France, 6 June 2004

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Project planning and inversion feasibility
Because of the field’s structural complexity, it was decided that a Pre-Stack Depth Migration (PreSDM) was to
be carried out over the Penguin cluster. In the planning of the processing and inversion sequence, the AVO
feasibility step was started at the beginning of the PreSDM velocity model updating cycle, in order to be able to
impact the final migration result prior to inversion. Therefore the feasibility study and the velocity model
updating were carried out in parallel.
At the start of the project - in parallel to the seismic data processing - P- and S-sonic logs and density logs were
edited, and corrected for borehole invasion effects. Then, Gassmann fluid substitution was performed and the
resulting brine, oil and gas(-condensate) bearing logs were used to model AVO synthetic seismic from which it
became apparent that no reliable hydrocarbon indicator was likely to be found. However, cross-plot analysis
including reservoir-sand and overlying shale sequences showed that there was scope for lithology separation in
the Ip-Is domain. Therefore the main target of the inversion workflow became facies identification and
subsequently identification of high porosity sand units.
Avo Diagnostics
Two types of AVO diagnostics were carried out, and both methods will be described here in more detail. The
first method is a diagnostic applied to pre-stack data, which are in this case the common image gathers obtained
from Pre-Stack Depth Migration. The second
diagnostic is a sub-stack diagnostic, applied to the near
mid and far angle stacks.
The first method, pertaining to pre-stack common
image gathers identifies problems with the fit of the
two-term Aki and Richards equation to the amplitudes
of this pre-stack data: A(θ ) = L + M sin 2 θ .
The above two terms are commonly known as intercept
and gradient. This two-term equation is fitted to the
events on common image gathers. (The velocities
employed in the migration yield the time variant offset
versus angle relations.) Subsequently, for each angle, a
Figure 2 RMS of the Near Mid and Far error cubes Obtained by “synthetic” amplitude is computed from the above
a gated measurement over the top Brent horizon equation, which can then be subtracted from the
observed seismic amplitude. In this manner an “error”
value can be obtained, for every time sample, at every angle (cf. “Making AVO Sections More Robust” by
Andrew Walden, BP, 52nd EAGE Meeting Copenhagen, 1990). This error is squared and is then summed for
each sub-stack angle range to obtain an average error pertaining to the near, mid and far angle ranges,
respectively. Note that in this manner we have obtained three cubes of data (for the near, mid and far angle
ranges) that contains an average error over each of the angle range for every time sample. After taking the square
root, the rms-error can be viewed either as a volume, or alternatively rms-error horizons can be extracted, e.g. in
time gates around key horizons. In a schematic view the error computation can work like this:
Amplitude vs. error vs. angle
angle
Figure 2 shows the rms-
Figure 3
error maps computed
Image gathers Image
from the near mid and
far “error” cubes,
respectively. These
maps have been
obtained from a
windowed measure-
ment along the top
reservoir horizon (see
Fig. 3, yellow marker
indicates the top Brent
pick) with the blue
colour indicating high
error. These maps
provide a quick tool to
Figure 3 Image Gathers (left) with indications of multiples, and residual move out
Stacked image gathers (right) with X-unconformity (red), top Brent (yellow) and top Dunlin (green)

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locate the areas of high error, allowing the common image gathers and their corresponding stack to be inspected
to identify the potential cause of these large misfits. The common image gathers and a migrated stack are
displayed in Fig. 3, to illustrate the usefulness of this diagnostic. Some of the gathers indeed show problematic
behaviour (arrows in Fig. 3). Residual
move-out is also visible, but that is
not yet important at this stage, as this
first depth migration only uses an
initial velocity model. By contrast,
much more important are the
suspected multiples over the reservoir
section.
This diagnostic allowed us to identify
very early in the processing the
requirement that a further multiple
removal application on the final
volume migrated output would be
necessary. The products on input to
the simultaneous AVO inversion are
the near, mid and far angle stacks.
The standard pre-inversion processing
procedure comprises the alignment of
Figure 4 Near Mid and Far stacks. The amplitudes become stronger the different angle stacks and a
from near to mid, and then drop again from the mid to far stack. This area was spectral shaping of the near and far
identified by means of the sign-flip diagnostic. stacks towards the spectral character
of the mid angle stack.
For this data, the spectral balancing was preceded by two multiple removal steps, a (pre-stack) tau-p decon-
volution over the reservoir section, and a further post-stack multiple removal deeper down on each of the angle
stacks so as not to affect the amplitude behaviour over the reservoir. After this processing stage, the second type
of AVO diagnostic can be run. This is a post-stack diagnostic that consists in a repeated fit of the two-term Aki
and Richards equation to the sub-stacks. Firstly, a fit to the near and mid sub-stacks delivers the first set of
intercept and gradient values, followed by a fit to the near and the far, that delivers a second set of intercept and
gradient values. It is generally known that the computed gradient values will display much lower signal to noise
levels than the intercept (which indeed only shows small variation in both fits), but on the other hand, a high
level of accuracy of the gradient term is not required for the diagnostic computed here. Actually the only aspect
that we are really interested in is a change of sign of “large” gradient terms:
M 2 − M1
S = 200sign( M 1 ) sign( M 2 ) .
M1 + M 2
Figure 4 illustrates this gradient difference map with an arrow indicating an area where the gradient sign-flip
occurs (when S is negative). The near, mid and far angle stacks are also shown, with the area of the gradient sign
change indicated by the ellipses over the sections.
The conclusion that can be drawn from this post-stack
diagnostic is based on the fact that these identified
problem areas are very limited in extent. Because these
“noisy” areas don’t seem to represent an extensive
problem the overall conclusion made from these
diagnostics is that an AVO inversion would provide
reliable and sensible results.
Further QC’s were carried out on the stacks prior to the
inversion. These diagnostics assessed: data alignment,
multiple removal, and the spectral balancing. An AVO
inversion with Jason software was performed,
producing P- and S-impedance cubes. Based on the
Figure 5 Well logs (green) and V-shale cube (red) compared at the
142S1 and 141S2 wells rock-physics model, a shale-fraction cube and a porosity
cube were derived from these Ip and Is volumes.
Inversion results
Raw inversion products, as well as derived V-shale and porosity cubes were QC-ed against well measurements.
In order to assess AVO information only, both cubes and well logs were filtered back to the seismic bandwidth.
The “goodness of fit” between band-limited logs and derived cubes is observed to be generally good as
EAGE 66th – W5 - What pre-stack data and processing do we need for reservoir characterization —
Paris, France, 6 June 2004

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illustrated in Figure 5. Furthermore, in
order to assess the potential added value
of the inversion products, they were
compared with the existing reservoir
model. In comparing porosity maps (see
Fig.6), it can be seen that average trends
are very similar, but the seismically
derived products may deliver additional
information that is not yet captured in the
current model. These results still need
further evaluation before they could be
used to constrain higher resolution
lithology and porosity cubes of the static
reservoir model. An alternative manner
to evaluate inversion results consists in
looking at the horizontal wells that were
not included in the low-frequency
Figure 6 Porosity column from the reservoir model (left) compared with the one of the
inverted cube (right). The inversion result sows more detail at several locations. inversion background model. In this
respect, the C2 and D1 production wells
were not incorporated in the inversion
workflow, and therefore they represent
good reliable blind tests.
As shown in Figure 7, the C2 well
encountered a thin up-thrown shale block
within the reservoir interval, that had
never been spotted on reflection seismic,
but which was correctly indicated on the
V-shale section of the inverted result.
Similarly, when compared to the D1 logs,
the V-shale cube derived from the
inversion showed a good match. This
included the identification of a sand body
at the toe of the well that was poorer
quality than expected from the reflectivity
data.
Due to the varying thickness of the
overlaying Humber group (Kimmeridge
and Heather shales), the top Brent pick
Figure 7 The horizontal C2 well (not indicated) encountered an up-thrown shale block
in the reservoir section. The vertical 211/13-2 well (indicated) shows a very thin cannot easily be interpreted accurately on
Kimmeridge section of approximately 30 ft. The V-shale cube from the inversion ties reflectivity data, as it can be masked by
the well log very well over the reservoir section. the side-lobe energy from the much
stronger contrast at Base Cretaceous
unconformity level. Because of the broader bandwidth of the inversion result that tends to minimize tuning
effects, the resultant cubes also offer the possibility for refining top-reservoir interpretation for increased
volumetric accuracy.
Conclusion
The two AVO diagnostics, a pre-stack and a post-stack AVO diagnostic, discussed in this paper have proven to
be successful during this integrated project, and have impacted the processing sequence to optimize the inversion
result. Subsequently, we showed that the inversion cubes exhibit some very positive features that have been
confirmed by “blind well” results. Further evaluation of the inversion-data is needed before it – or part of it is
included in the reservoir model. Finally, the AVO diagnostics presented here have made an important
contribution to the integration of the several components of this combined PreSDM - AVO inversion project.
Acknowledgements:
The authors would like to thank Exxon-Mobil and Shell EP Europe for their kind permission to publish this
material. Moreover, we want to thank several of our colleagues who contributed to the development of the AVO
diagnostics, Peter Ashton, Greg Hester, Henk Tijhof and Peter Rowbotham. Furthermore, we want to mention in
particular Alexander Sementsov and Richard Shipp for their work on the inversion and PreSDM, respectively.