what is the best seismic attribute for quantitative seismic reservoir characterization

7/27/2019 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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What is the Best Seismic Attribute for Reservoir Characterization?

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.

what is the best seismic attribute for quantitative seismic reservoir characterization

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