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Adaptive Seismic Texture Model Regression for Subsurface Characterisation
Figure 1: Seismic Texture Element Examples
method helps to define detailed facies variations along and across
channels in deepwater (>2,000m) offshore Angola (West Africa).
AB C
Systematic distribution patterns and variations are interpreted as
possibly related to different facies elements ranging from channel
fill, lobes, levee and overbank to marine shale in a typical turbidite
system.
3,4
In comparison, the conventional seismic attribute methods
(see Figures 4b, 4c and 4d) fail to define facies variations in as much
detail as the texture model regression method (see Figure 4a).
Although the coherence attribute map (see Figure 4b) effectively
delineates outlines of channels (container), it fails to map facies
A: 1D (linear) texture element consisting of 15 amplitude samples; B: 2D (planar) texture element consisting
variations either within or outside the channels because the
of 9 by 15 amplitude samples; C: 3D (cubic) texture element consisting of 9 by 15 by 5 amplitude samples.
coherence algorithm calculates lateral correlation between
Figure 2: Schematic Representation of the Texture (1D) Model
neighbouring traces that is not sensitive to textural (waveform)
Regression Process Using a Trigonometric Cosine Waveform as the
characters without model calibration.
Model with Constant Amplitude and Frequency but Adaptive Phase
In addition to mapping seismic facies variability at a specific
Dynamic
1D model geological time using a single stratigraphic surface, the evolution of
facies can be effectively unravelled by sequentially slicing through
the texture facies volume using a series of stratigraphic surfaces.
1
Figure 5 shows a series of facies maps made at discrete stratigraphic
2
levels in the Miocene and Pliocene stratigraphic intervals of the
3
turbidite system in the survey area. Several major facies elements are
evident and easily interpretable, including channel fill,
levee/overbank, lobes and marine shale based on the spatial
relationship and amplitude characters of different facies elements,
along with regional geological information and exploration well tests
Data trace
in the nearby survey areas (Milliken 2005, personal communication).
Linear least-squares regression is performed repeatedly at each sample location as the model (right) moves These maps demonstrate both spatial variation and temporal
along the data trace (left). To minimise the impact of phase of the trace on facies visualisation, the algorithm
keeps updating the phase of the model while moving along the data trace on a sample-by-sample basis. On
evolution in geometry, trend, morphology, width, areal extent and
the right are the snapshots at three sample locations (labelled 1, 2 and 3) with three different phases
lithology of turbiditic channels and associate facies. Investigating this
(indicated by green, yellow and red, respectively).
spatial and temporal variability is instrumental in evaluating the
Figure 3: Schematic Graphical Representation of the Linear
bathymetric gradient, play fairway and hydrocarbon potential in
Least-squares Regression Analysis and the Regression Gradient g the deepwater offshore Angola (West Africa) and other deep-marine
(the Slope of the Regression Line)
deposition settings.
D
i Discussion
255
In the exploration phase of frontier sedimentary basins such as in the
D=gM+b
deepwater region offshore Angola (West Africa), texture model regression
is helpful in evaluating hydrocarbon potential, identifying features of
interest such as channel sand and delineating play fairway. The new
algorithm achieves the objective for seismic facies discrimination by using
a texture model as a soft calibration filter. Not only is the new method
127 straightforward and computationally efficient, it also streamlines the
seismic facies interpretation workflow. In the frontier sedimentary basin,
the algorithm deployed one of the simplest seismic texture models, but
created a superior result that was geologically meaningful and consistent
with regional geology and outcrop analogues of deep-marine
depositional facies. Furthermore, the texture model regression method
(0)
M
i
127
255
can facilitate calibrate seismic texture with geology by interactively and
iteratively updating the dynamic model until the best match is realised
Results and Geological Implications between the finalised result with geological concepts and outcrop
3D seismic facies volume visualisation is typically performed by analogue of similar depositional systems.
slicing the facies volume using a stratigraphic surface in a similar
fashion to amplitude horizon slicing through regular amplitude In the exploitation phase of mature sedimentary basins, texture model
volume. Comparative analysis (see Figure 4) indicates that the results regression can be applied to model and predict reservoir properties based
created using the texture model regression method are superior to on extensive well bore information whenever and wherever available. In
those created using amplitude extraction and other routine attribute this application, the algorithm can implement a fully dynamic texture
extraction algorithms. Figure 4a demonstrates that the current model defined by variable amplitude, frequency and phase in the process
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EXPLORATION & PRODUCTION – OIL & GAS REVIEW 2008 – VOLUME 6 ISSUE II
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