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Directional guided seismic attributes and their use in assisting structural, stratigraphic and

lithological interpretation Ahmed A. Aqrawi1*, Wolfgang Weinzierl 1 , Ralph Daber 1 , Trond H. Boe2

1

SIS Norway Technology Center,2

Schlumberger Stavanger Research

Summary Seismic attributes are one quantitative measure inherent inthe phase, frequency and amplitude content of reflectionand refraction data. Detection of structural and stratigraphic

information based on seismic attributes is one of thefundamental workflows when it comes to interpretingsubsurface features and seeking reservoir engineeringinformation from seismic data. Not only do we need to

identify the correct location of structures and edges likefaults, it is of critical importance to understand throw

profile behavior along faults and the true location of faulttips for volumetric and spill point analysis. Proper attributeanalysis gives an interpreter a tool to help characterize the

static and dynamic subsurface characteristics such aslithology terminations, possible juxtapositioning oflithologies and cross fault sealing capabilities to fluid flow.

As we move into more complex environments, attributes based on structurally oriented filtering have been used toimprove S/N ratios while preserving the discontinuities

found in the data thereby enhancing stratigraphic boundaries and lithology terminations. We present anEuler-based method for directional filtering and apply thismethod to different structurally complex environments. We

obtain improved S/N ratios and a more consistent andcontinuous edge mapping in comparison to conventionalisotropic methods. Results for delineating salt in the Gulf ofMexico (GOM) and detecting fault extents offshore

Norway indicate that methods based on structurallyoriented filtering can provide increased clarity in theidentification and evaluation of subsurface features.

Introduction

In their review of the developments in the field of seismicattributes Chopra and Marfurt (2005) outlined advances instructurally oriented filtering and indicated the improved

behavior of edge enhancement algorithms tracking on these

edge-preserving attribute volumes. Although the generationof directionally guided attributes has only become

justifiable with the parallel deployment of even more

powerful workstations the use of attributes having a

directional component based on the Euler method was first

shown by Rijks and Jauffred (1991). Introducingdip/azimuth maps and extracting amplitudes theyestablished what is still perceived a best practice to date. In

the past few years the value of directional attributes forreservoir analysis has been demonstrated by Nissen et al.(2009), whereas the most recent development in the field is

the directional lineament analysis using color maps byWallet et al. (2011).

Method

In this work, we will be focusing on two different structuralattributes and show the effects that directional muting hason the output generated by them. To perform structurallyoriented filtering using the Euler method the attribute is

calculated independently in the in- and cross-line directionand then subsequently summed using the Euler equation (1)

(Rijks and Jauffred 1991).

(1)

The attributes used in this study have a directional

parameter which can be tuned to highlight features in a

particular azimuth while muting responses perpendicular toit. The dip illumination attribute (Aqrawi and Boe, 2012),which builds upon the dip scan method from Marfurt and

Chopra (2007), demonstrates how directional muting can be used to reduce acquisition noise. The amplitude contrastattribute (Aqrawi and Boe, 2011) shows how directionalguiding can improve edge detection vertically as well as

laterally and thereby help in the extent identification of

discontinuities and their terminations.

Noise reduction and muting

Noise removal using structurally oriented filtering creates avolume which can be interpreted more consistently. Thedip illumination attribute from Aqrawi and Boe (2012),which is a dip exploit attribute, calculates a dip scan

estimation using a 5x5 neighborhood of traces, and isextended with an Euler directional component to filteracquisition noise embedded in the seismic response. Thisattribute consists of either calculating the dip magnitude

(Figure 1b) or the directional dip (Figure 1c)

Figure 1a displays a depth slice of an original seismicvolume acquired in the GOM. Although the structural

features, i.e. salt intrusions, are being mimicked properly by the isotropic operator used in Figure 1b, identification ofthe imaged salt body boundaries is difficult due to the small

scale high frequency responses. Contrarily the directionalfiltered dip illumination results (Figure 1c) highlight saltedges from the west together with N-S trending faultsthereby clearly showing how this muting de-masks the

blurring effect often introduced in seismic attributegeneration.

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lithological interpretation

Figure 1: Depth slice of original seismic (a), dip illumination

attribute (b) and directional guided dip illumination attribute (c).

The muting of events in the center is clearly visible and allows for

a more concise delineation of structurally oriented features.(Datacourtesy of WesternGeco)

Comparing the directional results with the results retrieved by an isotropic operator, it is clear that the apparent noise

for the undirected attribute is removed or muted. Thisshows how directional filtering is capable of isolating thestructural information. The attribute results are verydependent on the choice of direction and small changes in

orientation can result in the enhancement of othercomplementary features while suppressing others. Aninterpreters understanding of the structures and stratigraphyis therefore irreplaceable when it comes to defining the

parameters used in each environment to obtain the bestresults. In this particular example, we were primarily

interested in salt delineation. Nevertheless we are able togain important understanding of tops and flanks.

Although structurally oriented filtering can be a very

powerful tool for an interpreter the tradeoff betweendirectional filtering and the perpendicular muting directionis something an interpreter has to be aware of when

analyzing the result. An example of this can be clearly seenin Figure 2, where the highlighted fault structures around asalt diapir are detected in the isotropic case, and muted inthe directional filtered approach. Removals of structural

features seem to decrease the information we are able toretrieve. Diametrically opposing to this assumption it can

be advantageous when using an orientation perpendicularto the faulting direction, thereby enhancing the

discontinuities over possible background features or noise.Furthermore we do, by combining several directionalresults, enhance faulting events which can then bevisualized and interpreted together aiding in the

understanding of fault-fault relationships, tectonicdevelopment and fault properties.

Figure 2: Depth slice of original seismic (a), dip iluminationattribute (b) and directional guided dip illumination attribute (c).Highlighted are the effects of muting features using directional

attributes. The flank of the salt diapir can be immaged with much

higher accuracy using the directional result.

Fault detection and enhancement

As described in the previous section, directional filtering based on the Euler method is capable of removing noise bydirectional muting. Not only does this method allow for an

estimation of noise from the operator output due to stepedges, but it can be used for directional enhancement ofthese edges themselves. Edge-detection based ondirectional algorithms allows for a direct illumination of

faulting events and seismically detectable fracture corridors

and is capable of significantly enhancing the output ofunbiased operators. While in some scenarios an interpreteris primarily interested in the evaluation of structures along

a preferred direction, in others it could be of particularinterest to evaluate directional structural changes toeliminate the effects of noise resulting from a globalisotropic operator.

a) b) c)

a)

b)

c)

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Figure 3: Depth slice of the original seismic (a), an edge

enhancement volume using an isotropic operator (b) and

directional filtering (c) edge volume. The area displayed in Figure

4 is highlighted. Not only is the response of the edge enhancement

algorithm much higher but small edge terminations and/or

crossings are being imaged with much higher resolution.

To be able to compare the effects of structurally orienting

an operator used for the attribute generation we use ahighly fractured dataset offshore Norway. Figure 3adisplays a depth slice of an original seismic volume

acquired 100km west of Ormen Lange. Using theamplitude contrast attribute from Aqrawi and Boe (2011)we generate attribute volumes for both isotropic anddirectional filtering. In the case of directional filtering we

have isolated the directions of 30, 60, 90, 120 degrees fromthe inline direction and merged those results together. Wethen run an ant seeding edge enhancement methodintroduced by (Pedersen et. al. 2002) to enhance our edgevolumes. The enhanced isotropic results are shown in

Figure 3b, and the enhanced structurally oriented results arein Figure 3c. Comparing the results of directional edgedetection and enhancement workflows with conventional

operators reveal the effectiveness of the directional methodin obtaining more consistent results with a highercoherency ultimately enhancing edges while preserving the

response amplitude in each direction (see both Figures 3and 4).

Without any preconditioning of the data, i.e. frequencyfiltering or Gaussian based smoothing; we perform bothisotropic and directional filtering and consecutively feed

the result into the edge enhancement algorithm. The choiceto perform directional filtering in multiple directions thatrun perpendicular to structural trends is giving the finalresult obtained in Figures 3c and 4c. The coherency of the

edge enhancement algorithm is dependent on the signalamplitude obtained by the edge detection method, and it isevident that preconditioning the operator (i.e. limiting thedirection to a confined trend) will remove artifacts

introduced by considering every trace surrounding a nodeof interest (see Figures 4a and 4b).

Figure 4: Zoom indicated in Figure 3of the original seismic cube

(a), the edge enhancement by an isotropic operator (b) anddirectional filtered volume (c). The crossing of the discontinuities

mapped is imaged in much higher detail making a differentiation

of events possible.

Since a superposition of directional edge volumes allows aninterpreter to view a crossing of intersections from differentangles, he is capable of distinguishing these events based

on higher consistency of the amplitude response alongseveral directions, as can be seen in Figures 4b) and 4c).

a) c) b)

a)

b)

c)



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The crossing of the two faults highlighted was enhancedsolely by limiting the operator to a particular direction.Using an isotropic operator as in Figure 4b does not allow

distinguishing the events extracted for the discontinuities. It

is very difficult to differentiate the terminations foundacross several faults since the operator is smoothing theeffects which would be found along a predefined direction.

Conclusions

Comparing the results obtained for salt structures in theGOM and a highly faulted environment as found offshore

Norway we conclude that using conventional isotropic incombination with unconventional directional operators it is

possible to map and extract structural heterogeneities in amore detailed and coherent manner. Dependant on the

information to be obtained or filtered the structurallyoriented attributes are capable of clearly enhancing orsuppressing information extracted from the seismicsubsurface response. The use of directional filtering in

seismic attributes shows a distinct advantage over isotropicoperators to focus on specific features of interest, whetherthose be of structural, stratigraphic or lithological nature.Directional attributes based on the Euler method can be

used to filter unwanted noise and enhance the signalresponse for detecting edges along a preferred directionfrom the seismic amplitude, frequency, and phase content.

Acknowledgements

We would like to thank Schlumberger Information

Solutions for the use of the Petrel software andWesternGeco for the permission and use of their data in

this work.



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http://dx.doi.org/10.1190/segam2012-0674.1

EDITED REFERENCES

Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2012

SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for

each paper will achieve a high degree of linking to cited sources that appear on the Web.

REFERENCES

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Aqrawi, A. A., and T. H. Boe, 2011, Detecting salt domes using a dip guided 3D Sobel seismic attribute:81st Annual International Meeting, SEG, Expanded Abstracts, 1014–1018.

Aqrawi, A. A., and T. H. Boe, 2012, Detecting structural geology using dip and directional dip:Submitted to SEG.

Chopra, S., and K. J. Marfurt, 2005, Seismic attributes — A historical perspective: Geophysics, 70, no. 5,3SO–28SO.

Chopra, S., and K. J. Ma rfurt, 2007, Seismic attributes for prospect identification and reservoir

characterization: SEG.

Daber, R. E., and T. H. Boe, 2010, Using azimuthal curvature as a method for reducing noise in poststackcurvature volumes: 72nd Conference and Exhibition, EAGE, Extended Abstracts, D024.

Luo, Y., W. G. Higgs, and W. S. Kowalik, 1996, Edge detection and stratigraphic analysis using 3Dseismic data: 66th Annual International Meeting, SEG, Expanded Abstracts, 324–327.

Nissen, S. E., T. R. Carr, K. J. Marfurt, and E. C. Sullivan, 2009, Using 3-D seismic volumetric curvatureattributes to identify fracture trends in a depleted Mississippian carbonate reservoir: Implications forassessing candidates for CO2 sequestration: AAPG Studies in Geology 59, 297–319.

Pedersen, S. I., T. Randen, L. Sonneland, and O. Steen, 2002, Automatic fault extraction using artificialants: 72nd Annual International Meeting, SEG, Expanded Abstracts, 512–515.

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Seg 2012674