in opendtect
TRANSCRIPT
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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.
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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.
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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.
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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
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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
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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
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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.
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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 )
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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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