In OpendTect

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In OpendTect, seismic attributes are calculated using user-defined parameters. The

parameters selected are based on factors such as the quality of the seismic data, the nature

of faulting in the area, size of the dataset and the availability of high end computing

facilities for the attribute calculation. Once the desired parameters are selected, the

seismic attributes are calculated on-the-fly or stored as attribute volumes.

Table 3.2 is a summary of the parameters used for the calculation of the seismic attributes

presented in this chapter. The time gate is a measure of wavelength of structures mapped

in the seismic attribute. A smaller time gate will image short-wavelength structures in the

seismic volume and a larger time gate will image broader structures. The step-out defines

the radius of investigation, while the full steering modes ensure that the attribute is

calculated from one trace to another. The concept of steering the calculation of seismic

attributes presented in this thesis is described and illustrated in section 3.2.2.2 of this

chapter.

Attribute Time gate (ms) Lateral position Other settings

R aw steering - Step-out (3,3,3) -

Detailed steering - Step-out (0,0,5) -

Background

steering

- Step-out (0,5,0) -

Similarity (-24, 24) - No steering

Dip-steeredsimilarity

(-24, 24) Full steering Full steering

Maximumcurvature

- Step-out (3) Full steering

Table 3.2: List of parameters used in the calculation of seismic attribute volumes. The time gate is the time

window sample, the step out is the radius of investigation in inlines and crosslines. The steering data is the

dip trends of the seismic volume used to steer the calculation of similarity and curvature attributes. These

parameters can be selected to suit the quality of the input data and the desired end results.



In addition to enhancing the resolution of discontinuities in the seismic volume, the dip-

steered similarity attribute has also enhanced the resolution of the stratigraphic pattern of

the seismic reflectors. Figure 3.21 is a cross sectional demonstration of how dip-steering

can guide the interpretation of stratigraphy from the similarity pattern of the seismic

reflectors.

The seismic reflection pattern in the top half of the cross section is predominantly strong.

A prominent reflection marks a change from strong to weak/transparent reflections. Two

strong reflections occur where the pattern of seismic reflection changes. In similarity

cross sections the predominantly strong reflections in the top half of the seismic cross

section is a zone of high similarity and alternations of high and low similarity is probably

due to changes in acoustic impedance caused by alternating lithology. The predominantly

weaker and chaotic reflections below the upper zone of high similarity appear as a zone

of poorly defined chaotic similarity and are perhaps a reflection of a different lithology.

The high similarity reflection (block red arrows) is probably one of several detachment

levels typical of gravity-driven thin-skinned deformation in the Niger Delta. The top of

basement is presumably the strong high similarity reflection below the presumed

detachment level (block black arrow).

Previous interpretation of stratigraphy using seismic amplitude cross section has suffered

from the poor resolution of the sedimentary sequences in seismic cross sections.

Therefore these attributes not only aid in interpreting structures but also permits the

identification of lithologic types based on the attribute response of the seismic reflectors.



A demonstration of how dip-steered similarity attributes have been used to guide the

interpretation of the stratigraphic setting of the seismic dataset is presented in chapter

four of this thesis.



WSW ENE

WSW ENE

1.0 stwt t

2500 m

a

b

Amplitude

+ve -ve

Similarity

0.0 1.0

Stong reflection

Stong reflection

High similarity

High similarity

Weak reflection

Weak reflection

Low similarity

Low similarity

1.0 stwtt

2500 m



Figure 3.21: Seismic amplitude cross section (a), and dip-steered similarity cross section (b).The dip-

steered similarity attribute can guide the interpretation of stratigraphy from the similarity pattern of theseismic reflections. The block red arrows show how the imaging of prominent stratigraphic features in the

seismic data is enhanced by the similarity attribute. Similarity values close to zero are interpreted as low

similarities and values close to one are interpreted as nearly similar trace segments. Vertical scale is seconds

(two-way-travel time) and horizontal scale is in meters.

Figure 4.6: Colour scale used to present the curvature maps of thrust faults mapped in JDZ seismic volume.

The Gaussian curvature colour bar (left) is selected to show anticlastic (negative), planar and synclastic(positive) Gaussian curvature of the faults at a particular range of scale of observation, while the maximum

colour bar is selected to show positive (convex), planar and negative (concave) maximum curvature.

4.2.5 Faul t curvat ur e fr equency plots

Frequency plots of fault surface curvature show the graphical distribution of curvatures in

the time axis of the faults (Fault transport direction). The default view is ³colour-

mapped´ in which the data points are ³binned´ by the number of divisions on the plot and

colour coded. The plot viewer samples the surface for a modelled surface attribute along

the horizontal and picks up different attribute values and plots the values along that time

in the cross plot. Furthermore, a frequency colour map ranging from zero to hundred

percentage is used to point to indicate the frequency of occurrence of the attribute. High

frequency colour bin is an indication of the large concentration of data points in the plot,

Synclastic

Planar

Anticlastic -Concave

Planar

Convex

a bkGauss kmax



and a low frequency is an indication of few data points. Zero frequency is an indication of

the lack of modelled data on the surface or non existance of data. The colour-binned plot

view therefore provides a three-dimensional plot of the surface attributes. The three axes

include the x and y representing the fault attribute and the horizontal time axis, while the

frequency colour bin constitutes the third axis. In this thesis, a spectrum colour bar is

used to show the frequency of curvature attributes on the fault surface in the time axis

(Figure 4.7). The top and bottom of the colour bar represents the minimum and maximum

fault surface attribute frequencies respectively.

Figure 4.7: Colour scale used to present the frequency plots of fault surface curvature. The red shadesrepresents low frequency of surface attribute and purple shades represents high frequency of curvature.

Figures 4.8 is an example of how the plot viewer is used to present and interpret graphical

plots of an attribute modelled on a fault surface. Figure 4.8a is the raw plot of a modelled

fault surface attribute. The x and y axis represents the fault surface attribute and time

direction respectively. The bold black ellipse in the raw data plot indicates regions of

Frequency (%)

0

100



high raw data points. The dashed ellipse represents regions of low raw data points. In

figure 4.8b, the raw fault surface attribute data have been colour-binned to show a colour-

coded representation of the frequency of the attribute on the fault surface. The black bold

ellipse in figure 4.8b indicates regions of high frequency of fault surface attribute (purple)

based on the high density of raw data points in figure 4.8a. In the same manner, the

dashed ellipse in figure 4.8 represents low frequency of fault surface attribute (red) based

on the low density of raw data in figure 4.8a. Figure 4.9 is a normalized frequency

histogram of the modelled fault surface attribute. In figure 4.8b, the majority of the high

frequency data plots where the fault surface attribute is high. This is confirmed by the

high frequency of high fault surface attribute.



Figure 6.26: Similarity fault slices parallel to fault 1 at 100 m in the hanging wall and footwall. Note the localized region of low

similarity indicated by the block arrows in the hanging wall and footwall. Vertical scale is in milliseconds two-way-travel time andhorizontal scale is in meters. Vertical exaggeration is approximately 1.6.

1 km

500 ms

Strike (degrees)

0.00

360

1000 m

500 msN

N

1000 m

500 msN

Similarity

0.00

1.00

Similarity

0.00

1.00

Hanging wall similarity slice 100 mparallel to fault

Region of low similarity inhanging wall slice

Region of low similarity infootwall sliceFootwall similarity slice 100 m

parallel to fault

Trace length (m)

T WT ( m s )

Trace length (m)

T WT ( m s )



Figure 27: Fault surface model (a), strike model of the fault surface (b), hanging wall similarity slice 100 m parallel to the fault superimposed on the f100 m parallel to the fault superimposed on the fault strike model (d). Note the close match between the zone of pronounce curvature of fault, zone of pr

zones of low similarity in the hanging wall and footwall at 100 m in the wall rocks. Vertical scale is in milliseconds two-way-travel time and horizapproximately 1.6.

1000 m

500 ms

Strike (degrees)

0.00

360

1000 m

500 msN

N

Region of prono uncedcurvature on fault plane

Region of pronounced changein fault strike on fault plane

Regfaullow

Hanging wall similarity slice 100 mparallel to fault superimposed faulton strike model

RestrFootwall similarity slice 100 m paral-

lel to fault superimposed on fault-strike model

1000 m

500 ms

1000 m

500 ms

Trace length (m)

T WT ( m s )

Trace length (m)

T WT ( m s )

Trace length (m)

T WT ( m s )

Trace length (m)

T WT ( m s )



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