what is the best seismic attribute for quantitative seismic reservoir characterization
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What is the Best Seismic Attribute for Quantitative Seismic Reservoir Characterization?Dennis Cooke*1, Arcangelo Sena 2, Greg O'Donnell 3 , Tetang Muryanto 4 and Vaughn Ball 4. 1 ARCO Alaska, 2
ARCO Exploration Technology, 3 ARCO Indonesia, 4 Matador Petroleum formerly ARCO Exploration
Technology
Summary
It is possible to generate at least 30 different seismic
attributes from a given seismic data set. This presentationaddresses the question of which of those post-stack
attributes is most appropriate to use for a quantitative
seismic reservoir characterization. Our conclusion is that
an absolute impedance inversion is the best attribute in
theory, but, in practice, a relative impedance inversion ismuch more practical.
Introduction
Reservoir characterization is the process of mapping areservoir's thickness, net-to-gross ratio, pore fluid, porosity,
permeability and water saturation. Traditionally, this has
been done in a field development environment using data
from well logs. Within the past few years, it has become
possible to make some of these maps using seismicattributes when those attributes are calibrated with
available well control. The advantage of using wells and
seismic instead of just wells alone, is that the seismic data
can be used to interpolate and extrapolate between and
beyond sparse well control.
There is a multitude of different seismic attributes that can
be generated from a given seismic data set. A quick review
of one popular seismic interpretation package shows thatone can generate at least 30 different seismic attributesfrom an input seismic survey. Some of these attributes are
much better than others for reservoir characterization, but
there has not been much discussion of this in the
geophysical literature. The objective of this presentation is
to try to classify seismic attributes and show which oneswork best for reservoir characterization.
One way to organize and understand seismic attributes is to
separate them into the following four categories:
1)Qualitative attributes such as coherency - and perhaps
instantaneous phase or instantaneous frequency - are very
good for highlighting spatial patterns such as faults or
facies changes. It is difficult if not impossible to relate
these attributes directly to a logged reservoir property likeporosity or thickness, and thus these attributes are not
normally used to quantify reservoir properties.
2)Quantitative attributes: The simplest quantitative
attributes are the amplitude (of a peak or a trough) on zero
phase data, relative impedance data or absolute impedance
data. In our opinion, these three attributes (zero phase
amplitude, relative impedance and absolute impedance) are
the most useful for quantitative reservoir characterization.
3)Interval attributes are those that are used to quantify a
window of seismic data usually containing more than one
peak or through. Most seismic attributes fall into this
category. Examples of interval attributes are number ofzero crossings, average energy and dominant frequency.
These attributes are frequently used when a reservoir's
seismic reflection(s) are so discontinuous that it is
impossible 'pick' the same peak or trough on all traces. Aninterval attribute is analogous to a well log cross sectionwith a number of thin, discontinuous sands that can not be
correlated with any certainty. For this reservoir, a net-to-
gross sand ratio map is made instead of individual sand
(flow) unit thickness maps. A seismic reservoir
characterization is always improved if all peaks and troughsover the reservoir interval can be 'picked' individually and
thus have quantitative attributes extracted. If this is not
possible, the use of interval attributes is warranted.
4)AVO attributes are those that are generated using areflection's pre-stack amplitudes. Examples of pre-stack
attributes are AVO gradient, AVO intercept, near
amplitude and far amplitude. 3D pre-stack attributes have
only become available recently with the advent ofaffordable pre-stack time migrations. Pre-stack attributeshave a lot of promise, but are beyond the scope of this
presentation.
This talk will focus on the three main quantitative attributes
(zero phase, relative impedance and absolute impedance)and address their respective advantages and disadvantages.
Zero Phase Amplitudes
All seismic attributes are calculated from the final migratedzero phase dataset (or what is believed to be zero phase).
Clearly, the easiest, fastest, least expensive attribute is the
zero phase amplitude. The convolutional model and the
reflection coefficient formula show that a reflector's zero
phase amplitude can be directly related to the reservoir'simpedance. A thin-bed tuning curve model shows that zero
phase amplitude is also directly related to reservoir
thickness. Additionally, gas substitution modeling shows
that a reservoir's zero phase amplitude can be influenced by
changes in pore fluids. A solid theoretical conclusion is that
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changes in zero phase amplitude are a function of changesin reservoir impedance, thickness and pore fluid. This
conclusion has been proven by many successful
quantitative reservoir characterizations done with zero
phase amplitude.
Absolute Impedance and its Advantages
The absolute impedance attribute can be generated with
either a Seislogtype impedance inversion (one thatincludes a low frequency background model) or a model-
based inversion such, as that first described in Cooke and
Schneider (1983). There are two major motivations for
using absolute impedance for reservoir characterization:
1)The amplitudes on an absolute impedance dataset
describe the impedance of the rocks, where the amplitudes
on a zero phase dataset describe the impedance contrast
between rocks. Put another way, the impedance attribute isrelated to the geology while the zero phase attribute is
related to the derivative of the geology. The importance ofthis difference can not be overstated for the case where the
impedance of both the reservoir and the surrounding rock
are changing laterally. Consider Figure 1 which shows thedistribution of impedance for both cap rock and reservoir
rock (gas filled and oil filled) at Prudhoe Bay Field. These
distributions can be input into the reflection coefficient
formula which leads to the reflection coefficient
distributions of Figure 2. Figure 1 corresponds to absoluteimpedance data and Figure 2 would correspond to zero
phase data (without a seismic wavelet). Clearly, the ability
to discriminate between oil filled reservoir and gas filled
reservoir is enhanced in the absolute impedance case.
Figure 1: Probability density functions for the acoustic impedanceof Sadlerochit reservoir and Shublik cap rock at Prudhoe Bay
Field.
The data in Figure 1 are taken from a gas well and an oil
well. As expected, the gas sand has slower impedance that
oil sand. The cap rock impedance varies due to laterallithology changes and because it is a waste rock and
contains some oil and/or gas.
Figure2: Reflection coefficient probability distribution.
Calculated using the impedances in Figure 1 and the formula:RC = (Z2-Z1)/(Z2+Z1) where Z1 and Z2 are the impedances ofthe cap and reservoir rock.
2)The second major motivation for using absolute
impedance instead of zero phase amplitude concerns the
amplitude scale and format problem that occurs with zerophase data. Consider an undrilled gas prospect on one 3D
survey, with a second 3D survey that covers a nearby gas
discovery. With zero phase seismic data, the prospect's
amplitudes and the gas discovery's amplitudes can not be
compared (unless a similar empirical scaling has beenapplied to both). Furthermore, the gas discovery's logs can
not be compared the amplitudes on the zero phase seismic
data. When both 3Ds are converted to absolute impedance,
the seismic amplitudes can be compared to each other andto the impedance logs from the gas well.
Disadvantages of Absolute Impedance
Absolute impedance inversions can be very expensive in
terms of both money and time delays. Frequencies in theinversion above the seismic bandpass will be non-unique.
And since the input zero phase seismic data does not
contain frequencies below the seismic bandpass (which are
required for inversion), information at these frequencies
must be supplied by the processor. The work that is doneto prepare and constrain the low frequency portion of
inversions can be very subjective and interpretive. Most
often, this work on the low frequencies is not done by the
interpreter, but by others who may not communicate to theinterpreter the subjective nature of the low frequencies.
A good way to understand the problem with the low
frequencies in absolute impedance inversion is to consider
a hypothetical inversion between two wells as in Figures
3A and 3B. Wells A and B at structural highs have tight
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and thin reservoir (marked in yellow). A prospectivelocation exists between the wells, but it is not clear if the
reservoir there is better or worse than on the highs (and this
is why the inversion is being done). The inversion process
requires input of a low frequency (below seismic
bandwidth) impedance for all traces. At wells A and B,this low frequency is taken from the well control. At all
other locations, the processor must interpolate, interpret or
guess at this low frequency input. At the proposed
location, this low frequency guess could take the form of a
linear interpolation between wells A and B (shown in blackin 3A). Alternatively, the low frequencies at this location
could be modified to fit the structure of the reservoir (i.e.
shifted down to tie the yellow horizon). Additionally, the
low frequency input could be modified to fit hypothetical
depositional models. Two possible depositional models:
Depositional Model 1): The package of sediments that
surrounds the reservoir it is a predominantly fluvial system.
This implies that locations A and B would have
preferentially received thin, shaley over-bank deposits andthe proposed location would have received more sand.
Assuming that sands have a slower velocity than the shales
here, this depositional model implies that the proposed
location needs a low frequency input that is lower than that
found at wells A and B. This model's low frequency inputis shown in blue in Figure 3B.
Depositional Model 2): This is a predominantly shallow
water marine system and the package of sediments at the
proposed location have more shale than at A and B. Again,if the sands are slower than the shales, the proposed
location would needs a low frequency input that is faster
than found at wells A and B. This low frequency input is
shown in red in Figure 3B.
Each of these three different low frequency models are just
as correct as the others. And, if their frequency content is
below the seismic bandwidth, three separate inversions
using them would lead to three significantly different
results for the full bandwidth absolute inversion. Sinceinclusion of the low frequencies can lead to such confusion,
perhaps the best approach is to not include them at all.
This leads to an inversion that is restricted to the bandwidth
of the input seismic - also called a relative impedance
inversion.
Relative Impedance Inversion
The high cost and uncertain nature of absolute impedance
inversions are the result of including the low frequencies inthat inversion. If the low frequencies are not used, these
problems go away, but the absolute impedance inversion
becomes a relative impedance inversion.
Figure 3A. Hypothetical inversion example.
Figure 3B: Three different low frequency impedance trends for theproposed location in Figure 3A.
There are numerous ways to calculate a relative impedance
inversion from the zero phase dataset. Perhaps the simplestmethod is based on Lindseth (1979) who rewrites the
reflection coefficient formula to express impedance as the
integral - or running sum - of the reflection coefficients.
This running sum can also be expressed as a convolutional
filter where the phase spectrum is a 90 degree rotation andthe amplitude spectrum has a -6dB/octave filter. One very
easy way to generate an relative impedance dataset is to use
this 90 degree phase rotation filter.
There are two advantages to absolute inversion listedearlier: 1) geology vs. derivative of geology and 2) the
scale problem of zero phase dataset. The relative
impedance dataset does just as good of a job as the absolute
impedance on the first problem. However, on first
inspection, the relative impedance inversion appears tohave the same scale problem as the zero phase dataset it
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was generated from. This implies that relative impedancesfrom different 3D surveys and from well data can not be
quantitatively compared.
There are two ways one can address the scale problem
associated with relative impedance. The first way onlyscales a reservoir's relative impedance map and not the
entire relative impedance dataset. When doing any
reservoir characterization project where a number of wells
are available, the reservoir's impedance at the well
locations should always be cross plotted against thereservoir's well log properties. This cross-plotting step
indicates whether or not the impedances are related to the
well log properties, and if they are, the cross plot supplies
the information needed to calibrate relative impedance and
remove its scale problem. For example, if a cross plotbetween a reservoir's relative impedance and reservoir
porosity-feet shows a linear trend of the sort:
porosity-feet = A*(relative impedance) + B
then a map of the reservoir's relative impedance can be
transformed into porosity-feet by multiplying by A andadding B. This solves the relative impedance scale
problem.
Note that for an absolute impedance dataset, the inversion
step incorporates the low frequency information and 'scales'the input data to absolute impedance, but it is then rescaled
to porosity-feet with the cross-plotting. The first scale step
for absolute impedance dataset is thus redundant.
The second method to scale a relative impedance dataset isused when there are not a sufficient wells to make a cross
plot and/or the cross plot does not give a linear trend. This
method simply rescales the relative impedance data so that
its RMS amplitude for over a large user-defined depth and
map window is constant (usually = 1.0). This RMS rescaleis only valid if the earth's impedance averaged over a large
window is also constant. This scale process allows
comparison of amplitudes on the relative inversion with
relative impedance amplitudes from well models. An
example of this is shown in figure 4 which comes fromCooke and Muryanto (1999). Another quantitative tool that
is available with this type of scaling is to apply it to all the
seismic data over known oil and/or gas reservoirs for a
basin. This allows one to build a database that can be
sorted by fluid type or reservoir or reservoir thickness.This database tool can be very useful for quantifying
exploration risk.
Conclusion
A quantitative seismic reservoir analysis needs to be done
using a seismic dataset whose format allows easy
comparison between well data and different seismic
datasets. This can be done with absolute impedance data,
scaled impedance data or scaled zero phase data. Theabsolute impedance data is theoretically the best option, but
it has drawbacks related to its low frequency content. If thelow frequencies are removed, the result is a relative
impedance dataset, which is in practice the best seismic
attribute.
Acknowledgements
The authors would like to thank ARCO Alaska, ARCO
Exploration Technology and Operations, and ARCO
Indonesia for permission to publish this work. The
interpretations and conclusions discussed in this paper are
those of the authors and do not necessarily represent those
of the Prudhoe Bay Unit Working Interest Owners.
References
Cooke, D.A. and Schneider W.A., Generalized LinearInversion of Reflection Seismic Data, Geophysics, Vol. 48,
No. 6 (June 1983) P. 665-676
Cooke D.A. and Muryanto, T., Reservoir Quantification of
B Field, Java Sea via Statistical and Theoretical Methods,Submitted for presentation at the 1999 SEG International
Exposition and Meeting, Houston, TX USA
Lindseth, R. , 1979 Synthetic Sonic Logs - A process for
Straigraphic Interpretation: Geophysics, 44, 3-26.
Figure 4. Tuning curves made from synthetic relative impedance
data scaled to match amplitudes with 3D survey.
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