Gao_edit.qxp 24/10/08 11:07 Page 83
Geosciences
Adaptive Seismic Texture Model Regression for Subsurface Characterisation
a report by
Dengliang Gao
Marathon Oil Corporation (Currently at Chevron Energy Technology Company)
Concept and Theory here the algorithm designs a simple 1D seismic texture model by
Texture is a scale-dependent property that has different definitions and using a full wavelength of trigonometric sine function defined by 15
implications in different areas of science. In exploration seismology, a amplitude samples.
seismic response is a result of constructive and/or destructive interference
(wavelet convolution) among all of the wavelets scattered from Next, the algorithm sets a pointer to the first – or the next – sample
microreflectors within a small zone centred about a location in 3D space. location, and retrieves data surrounding each sample location to
In a seismic volume, the magnitude and variability of neighbouring form a texture element with the same dimensions and size as the
amplitudes (seismic texture) at a sample location are physically related to model, which in this case is a 1D wavelet consisting of 15 samples
the distribution of scatterers (geological texture) within the small zone that are equivalent to 30 milliseconds at a two-millisecond
at the corresponding location. In seismic stratigraphy, texture is a sampling rate.
composite function of the rogusity, lithology, thickness and configuration
of beds and thin beds, all of which directly affect fluid flow direction and The algorithm then performs linear least-squares regression between
efficiency. These relationships suggest that seismic texture analysis has the amplitude samples in the data texture element and those in the
important implications for interpreting depositional facies and model, and calculates the regression gradient, g:
characterising reservoir properties from reflection seismic data.
n
(M
i
- M)(D
i
- D)
Spatial variability in seismic texture requires that it be evaluated at
g=
i=1
n
each sample location based on a small analysis window, which is
(M
i
- M)
2
i=1
usually called a texture element (see Figure 1). Generally, a seismic
texture element consists of N
x
by N
y
by N
z
neighbouring amplitude where D and M denote the mean values of the data texture element
samples in the in-line, cross-line and vertical directions, respectively. D
i
and the model M
i
, respectively.
It can be a 3D mini-cube, a 2D mini-window or a 1D wavelet (see
Figure 1). The optimal size of the element depends on the dominant To minimise the impact of phase of seismic traces, the algorithm
frequency of the trace data of interest and should typically cover designs a dynamic model with a flexible phase (see Figure 2).
approximately one average wavelength of the traces. For the data Computationally, this is achieved by constantly changing the phase
set presented here, the average wavelength is approximately 30 of the model until a best correlation is realised between the model
milliseconds, which is equivalent to 15 samples at two-millisecond and the data, at which point the corresponding regression gradient
sample increments. A seismic texture element that approximates the g (see Figure 3) is written as an output at the sample location.
average wavelength in its vertical dimension is recommended for
statistically robust texture characterisation. The adaptive model regression process proceeds from sample to
sample and from trace to trace throughout the input seismic
Methodology volume. As a result, the regular amplitude volume is transformed
This study implemented an adaptive texture model regression into a regression gradient volume that represents textural similarity
method to characterise seismic texture and seismic facies in the relative to the model and is deemed to be indicative of seismic facies
deepwater offshore Angola (West Africa). In contrast to the 3D grey- in 3D space.
level co-occurrence matrix (GLCM) texture attribute extraction,
1
the
adaptive texture model regression algorithm uses an interpreter-
Dengliang Gao works at the Chevron Energy Technology
defined texture model as a reference to evaluate seismic textural
Company. He previously worked as an Adjunct Professor
similarity relative to the model using a liner least-squares regression
in the Department of Geosciences at the University of
Houston, teaching a graduate-level 3D seismic
analysis.
2
In general, the workflow consists of four steps:
interpretation course. He has also worked at Marathon Oil
Corporation, Exxon Production Research Company and
• construction of a model M
Tongji University (China). Dr Gao has been developing 3D
i
(i=1…n);
seismic texture theories and methodologies as applied to
• retrieval of data texture element D
i
(x,y,z) (i=1…n);
seismic structure, facie and reservoir characterisation. He
• linear least-squares regression between model M
i
and data
has been the recipient of several patents, the Grover E Murray Best Published Paper award
and the Science and Technological Advancement award (China). He has been recognised by
texture element D
i
(x,y,z);
the Society of Exploration Geophysics (SEG) for his contributions as a peer reviewer and
• output of the regression gradient g (x,y,z). Associate Editor. He is an SEG Geophysics Guest Editor and Associate Editor, and is also on
the American Association of Petroleum Geologists (AAPG) Publications Committee. In 1997,
Dr Gao received a PhD in geology and geophysics from Duke University.
The first critical step of the process is to build a texture element as a
calibration model. Since seismic texture is a function of waveform,
© TOUCH BRIEFINGS 2008
83
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