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HGSLuncheon Meeting

Rob Simm, Rock Physics AssociatesLtd, UK; Michel Kemper, IkonScience Ltd UK; and ]agat Deo,Seismic Image ProcessingLtd UKTalk to bepresented by MartynMillwood Hargrave

AVO Impedance: A New Attribute for Lithologyand Fluid Discrimination

(Ed. note: This article was presented at Petex 2002 by the firstauthor in December 2002.)

Introduction

Porosity and fluid discrimination from seismic using elastic inver-

sion techniques is currently an area of considerable interest for oil

and gas exploration. Since the formulation of elastic impedance

(Connolly 1999) many workers have been evaluating the possibil-

ities of combining the benefits of inversion (i.e., calibration) with

the exploitation of AVO phenomena that potentially provideenhanced discrimination of fluid and lithology.

The work of Goodway et al. (1997) highlighted the benefits of rock

property related attributes (A and 11)in terms of both physical

interpretation and discriminating power. Recently, Whitcombe

and Fletcher (2001) formulated the attribute GI (effectivelya pro-jection of elastic impedance), and showed that projections on AlGI

crossplots can be used to differentiate fluid or lithology. This paper

presents another elastic attribute, termed AVOImpedance, that canbe considered alongside attributes such as LMR and AlGI.

AVO Impedance is a data-adaptive attribute based on a weighted

combination of acoustic impedance (AI) and elastic impedance(EI). Using a well dataset from the Central North Sea, the AVO

Impedance attribute is shown to be similar (in terms of its pre-

dicting power) to other attributes based on projections of AI and

GI and MR (IlP) and 11M (1../11).The simplicity of the AVOI

attribute and the direct relevance to the outputs of near- and far-

stack inversions are compelling.

DatasetA dataset from a well in the Central North Sea is used as a means

of comparing the different attributes. A 300-m section of the well

is used with variable thickness brine-bearing sands of Jurassic age.

Rock physics analysis and fluid substitution to oil bearing was

undertaken as well as depth to time conversion (2-ms sampling).

Various facies were then described from the time- sampled data.

AVO Impedance Construction

If acoustic impedance is crossplotted against elastic impedance (at

an angle of 25°) (Figure 1), it is evident that the trends of the various

facies are similar (Le.,diagonally from bottom left to top right) but

that there is an arrangement of shales at the top, then brine sands,

and finally oil sands progressing down the plot). AVOImpedance

effectivelynormalizes the data to a trend on this plot (usually the

brine sand trend is chosen as the normalizing trend).

The equation that performs this normalization is quite simply(AI*a+b) - EI where a is the slope of the trend and b is the

regression intercept. Essentially this operation is a projection of

the data points along,a trend, a technique borrowed from weight-ed stacking of AVO attributes.

Figure 1. AI vs EI Crossplot

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Figure 2. AVOImpedance interpretation template

Houston Geological Society Bulletin April 2003

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AVO Impedance Interpretation Template

It has been found that a crossplot of AVO Impedance vs AI pro-

vides a very useful template for the interpretation of porosityand fluid fill. A porosity and fluid fill framework is established

using rock physics analysis of well data.

In the example shown in Figure 2 the AVOImpedance attribute isnormalized to the brine sand data of the rock model. On the

crossplot. the shales (denoted by Kimm Clay and Ettrick shalespoints) plot to the left, with the brine sands in the center of the

plot and oil sands to the right. It illustrates that of the two attrib-

utes plotted (AI and AVO!) AVO Impedance is the principal

discriminator of fluid. Porosities plot successivelydown the cross-

plot showing the dependency of acoustic impedance on porosity.The plot also highlights the difficulties in differentiating fluid andlithology at low porosities «15%) using elastic attributes.

Of course, if there is variation in the fundamental nature of the

rock fabric of the sands then the framework becomes more com-

plex. However, in many cases the sands in a localized area can be

defined adequately by a simple porosity-dependent rock physicsmodel (e.g., Dvorkin et al. 2002).

Comparison with LMR and AlGI

AlGI and LMR crossplots for the same data are shown in Figures

3 and 4, respectively. The framework trends for fluid and porosi-

ty are also shown as is the projection trend that gives theoptimum discrimination of fluid (arrow with dashed line).

The AlGI and MR vs LlM crossplots show that individually theattributes have limited discriminating power. However, as

Whitcombe and Fletcher (2001) note, the discriminating power

is in the projection of the data. Projections designed to discrimi-

nate fluid have been applied to both crossplots and projection

attributes generated. To compare the attributes, histograms werecreated to which Gaussian fits were made for each of the facies.

The results (shown in Figure 5) show that AVOImpedance, the

"'ortho- fluid'" projection of AI and GI, and the projection of MR

and LlM have comparable similar discriminating power. In fact,

fuzzy logic cross-validation techniques (i.e., facies prediction on

the learning dataset) yields a 90% hit rate in the prediction offacies using each of the attributes.

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Discussion

The AVO Impedance attribute as described here would appears

to be as good a discriminator as any other projection of elastic

attributes. In a sense this is not surprising as they are all deriva-

tives of the same elastic parameters. However, AVO Impedance iselegant in its simplicity of construction and has a direct rele-

vance to the outputs of near- and far- angle stack inversions.

When crossplotted against acoustic impedance and linked to a

rock model framework, it provides a useful template for theinterpretation of elastic inversions.

The limits of practical application of the elastic attributes dis-

cussed here are an area yet to be fully explored (and documented

in the literature). Clearly,projections of AVO-related attributes are

sensitive to noise, and much depends on the pre-stack data quality

as to the success of application. It follows that noise-free compar-isons of different attributes may be misleading. White (2000)

illustrated that although there may be a theoretical justification of

the LMR attributes in comparison to the AVOintercept and gradi-

ent, the LMR attributes are more sensitive to noise. In the presenceof noise LMR-related attributes gave the same misclassification

rates as the AVO attributes. Limiting the adverse effects of noise in

inversions is one of the challenges of applying this technology.

An evaluation of AVOI-,AIGI- and LMR- related attributes gen-

erated from real seismic data is currently being undertaken.

References

Connolly, P., 1999. Elastic impedance. The Leading Edge,April, 438--452.

Dvorkin, J., Gutierrez, M.A and Nur, A., 2002. On the universality of dia-

genetic trends. The Leading Edge, January, 40--43.

Goodway, W., Chen, T. and Downton, J., 1997. Improved AVO fluid

detection and lithology discrimination using Lame petrophysical

parameters; Ir, mr and 11m fluid stacks from P and S inversions.

Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.

Whitcombe, D.N and Fletcher, J.G, 2001. The AlGI crossplot as an aid to

AVO analysis and calibration. Internat. Mtg., Soc. Expl. Geophys.,

Expanded Abstracts.

White, R.E., 2000. Fluid detection from AVO inversion: the effects of noise

and choice of parameters. 62nd EAGE meeting Glasgow, 1 June 2000.

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