Application of Spectral Decomposition Technique in Carbonatite Reservoir Prediction YANG Yingjun

Physical and Numerical Simulation of Geotechnical Engineering
5th ISSUE, Dec. 2011
Application of Spectral Decomposition Technique in
Carbonatite Reservoir Prediction
YANG Yingjun1, LV Youliang1，WANG Chengyi 2
1. China University of Geosciences, Beijing, P.R.China, 100083
2. Landmark Graphics International Inc. Beijing office, P.R.China,100020
[email protected]
ABSTRACT: Spectral Decomposition Technique is a processing and interpreting method in
frequency domain. Its main theory is based on thin-layer tuning theory. It obtains great success in
the study of thin layer in sedimentary rock. In the basis of the analysis for the basic principles of
the technology, in connection with the special natureof the carbonate reservoir, this paper expands
the analysis and application of spectral decomposition technology, introduces the prediction
researches in carbonate reservoir with detailed examples, and demonstrates the notable results of
the spectral decomposition technology in the prediction of void and cavernous reservoirs.
KEYWORDS: Spectral decomposition technique, Notches-in-thin-bed, Carbonatite, Dual
porosity, Reservoir
INTRODUCTION
Spectral decomposition technology is an interpreting
method that transfers time domain seismic data to
frequency domain processing. This method is proposed by
Lopez et al in 1997, which mainly studies thin-layer
changes and the continuity of geological bodies in the short
time-window, for 3D seismic interpretation and reservoir
prediction. Once the method was introduced, it has been
rapidly promoted to use. At present, the technology has
made significant effect in the fine depiction of a complex
fault system in sand and shale formations (especially small
fault identification of development block), the phase
boundary delineation / differentiation of sedimentary
environment (such as rivers, delta boundaries, etc.) and
predicting reservoir thickness.
In view of the outstanding performance of spectral
decomposition in sand shale formation, the technology is
gradually introduced into the carbonate reservoir research.
Carbonate reservoir has a typical structure of dual porosity,
pore developed. The shape, size and style of vertical
composition of the pores vary greatly, so these factors must
show their different response characteristics in the seismic
data. It can be processed using spectral decomposition
techniques, highlighting the characteristics of fractured
reservoir, for reservoir prediction.
1 PRINCIPLE METHODS AND VALIDATION
ANALYSIS
1.1 Fundamental principles
Spectral decomposition technology is mainly based on
the tuning principle of layer reflection. That is when the
thickness of thin layer increases to a quarter of wavelength
to the tuning thickness, the seismic reflection amplitude
reaches maximum. Thin layer reflection can be
© ST. PLUM-BLOSSOM PRESS PTY LTD
characterized as changes in stratigraphic thickness in the
frequency domain. Spectral decomposition technology is
thickness prediction with the use of the phenomenon of thin
layer frequency depression in early stage. In the amplitude
spectrum, the amplitude gradually increases with increasing
frequency, then the amplitude decreases with increasing
frequency after reaching the tuning frequency. When the
difference between top and bottom comes up to one-half
wavelength, the top and bottom reflection offsets,
amplitude achieves minimum, which is the notch
response[2]. Ideally, the reciprocal of the difference between
two adjacent frequency depressions is the thickness of time
of the thin layers. This is the theoretical basis of the thin
layer frequency depression phenomenon predicting the
thickness of the thin layer.
Traditional spectrum analysis method and spectrum
decomposition techniques differ mainly in the length of
time window of data analysis. Long window and short
window amplitude spectrum generates very different
frequency responses. Because traditional method requires
the Fourier transform signal in (- ∞ ~ + ∞) , as to spectrum
analysis for long-time window data (generally greater than
100ms), seismic reflection coefficient consistent with noise
spectrum shape, which is a constant, while the form of
spectrum is decided by the sub-wave morphology, which is
a trapezoid (Partyka G, 1999) (Fig. 1, left), the thin layer
reflection information for the spectral decomposition of
long window can not be achieved; when using the
short-time window (less than 60ms) for data analysis, for
the time window is short, and it includes small amount of
the thin layer, thus the reflection coefficient is no longer in
a random sequence, and its spectrum does not have the
characteristics of white noise, so the frequency depression
phenomenon appears in the thin layer reflection coefficient
amplitude spectrum, which is conducive to perform
Application of Spectral Decomposition Technique in Carbonatite Reservoir Prediction
DOI: 10.5503/J.PNSGE.2011.05.002
characteristics of thin layer (Fig. 1, right).
Figure 1 Comparison of long time window and short time window spectral decomposition
and convolution model (Partyka, 1999)
Spectral decomposition forms 2 categories of data: the
tuning body and the discrete frequency body.
1) The tuning body
The tuning body is the amplitude data that changes
continuously in the vertical effective band, generated
through short time-window calculation along the purpose
Figure 2 Tuning body process
2) The discrete frequency body
The discrete frequency body refers to the tuning
amplitude data of a series of discrete frequency generated
along short sliding window. The difference between the
discrete frequency body and the tuning body are that the
data volume of the discrete frequency body in the vertical is
the time, while each resulting data body contains only a
level or between two levels (Fig. 2). In the vertical
direction, the data body is the frequency that continuously
changes; in the plane, each of these frequencies
corresponds to normalized tuning amplitude. It could be
understood as data sets combined of several different
frequency slices in an effective earthquake frequency band.
Figure 3 Discrete frequency energy processes
single frequency component (Figure 3.) This frequency
analysis method can avoid the impact of layer using sliding
time window analysis method, and also it can eliminate
impact of interpretation brought by the structural shape
using sliding along the layer method to calculate the time
window.
9
Physical and Numerical Simulation of Geotechnical Engineering
5th ISSUE, Dec. 2011
1.2 Calculation method
Amplitude energy body of different frequency bands can be
obtained using discrete frequency characteristics of thin
layer tuning body, to analyze change in amplitude and
phase frequency characteristics of different frequency
bands. Generally, the best time window election is in the
40ms or less. The algorithm is applied for the target body
whose target is far less than the tuning thickness.
At present, the main spectral decomposition algorithm
are Fourier transform, short time Fourier transform,
maximum entropy, wavelet transform and the S transform.
LandMark software mainly provides two kinds of
algorithms: short-time Fourier algorithm and maximum
entropy algorithm.
1.2.2 Maximum entropy algorithm
1.2.1 Short time Fourier algorithm
The
Use Fourier transform to calculate the amplitude of each
frequency from the beginning to the end frequency, using
the following formula,
M
N
G ( k . f )    g ( m , n ) e
i
2
mk
M
e
method
algorithm:
is
Z e
2ift
based
on
the
Z
transform
, t represents sampling rate of
time domain.
The most obvious feature of this method is the
outstanding performance of small characteristics.
Drawback is that we can only recognize the single in partial
area, which is regional less effective, and needs
comparative analysis with the results of Fourier transform.
2
i
nf
N
n 0 n 0
Add a window function, which slides in the timeline,
outside of the data function, and we obtain the
transformation frequency tuning data of different times.
1.3 Verifying analysis
Figure 4 Curve 1 shows the amplitude of the curve;
Curve 2 is the tuning amplitude curve after frequency processing
Compare amplitude changes in the model channel with
frequency processing tuning amplitude to verify and
analyze. The analysis is carried out by forward modeling.
First, design a layered model, assuming that the amplitude
of top model reference channel is 100, with the horizontal
amplitude decreasing from 10 to 50 (Table 1), relative
reference channel variation rate from -10% to -50%. Use
the Ricker wavelet with frequency of 30Hz for convolution
to form seismic on the theoretical model, and then undergo
frequency processing on the theoretical model and the
sub-30Hz seismic respectively. The 10% amplitude
difference between the different channels of the theoretical
model is enlarged about 5 times magnification after
frequency treatment (Table 1, Figure 4). This shows the
tiny changes of the amplitude of lateral reservoir can
amplify by the frequency processing, highlighting the
exception.
Table 1 Data analysis of theoretical model
Amplitude
0～200
Model Road
Reference
Reflection amplitude
Theoretical
change rate (relative
Model
reference channel)
Frequency tuning
tuning amplitude
201～ 251～ 301～
351～
401～
250
300
350
450
500
100
90
80
60
50
-40%
-50%
0
-10%
-20%
33
16.2
0
50.1%
0
400
70
-30%
451～
100
0
-15.8
-32.6
-47.8
33
148%
199%
245%
0
amplitude(frequency change rate (relative
30hz)
reference channel)
10
100%
Application of Spectral Decomposition Technique in Carbonatite Reservoir Prediction
DOI: 10.5503/J.PNSGE.2011.05.002
2 FORWARD ANALYSIS OF CARBONATE
RESERVOIRS
Carbonate Formation reservoir types include four
categories, namely fracture, fractured porous, hole-and
cave reservoir. According to a western mining drilling
statistics, the dense layers speed is 6134 m/s, fractured
layer velocity is 5988 m/s, pore and fracture pore layer
velocity is 5813 m/s, and the cave layer velocity is 4232m/s.
Analyze the above speed data, caves and rock layer
velocity difference 1902 m/s, with the relative speed
difference 30%; holes, fractured porous layer and rock
layer velocity difference 321 m/s, with the relative speed
difference of 5%; and fracture and rock layer rate
difference 146 m/s, with the relative speed difference of
2.3%. Relatively speaking, the cave reservoir will form a
strong reflection amplitude, which is the most easy to
predict; porous, fractured porous second easy; fracture
hardest to predict.
The thin layer is defined as a stratum with the thickness
of less than a quarter of the wavelength in the geophysics.
Assume that the earthquake frequency is 30Hz, according
to H = (V * t) / 2 = V / (4.6f *), f* for the earthquake
frequency, calculated identifiable stratigraphic thickness of
about 44m. The majority of holes (burrows) reservoirs are
less than this thickness, the reservoir appears essentially
thin or thin layer (beaded), and so there will be tuning
effect.
Figure 5 Formation model, forward seismic modeling and frequency processing results of top of limestone
According to the rate statistical data, combined with the
mainland mine layer structure, design formation model,
combined with the designed fracture in corresponding
drilling sites, fractured porous and porous reservoir model,
using ray tracing method to form artificial seismic trace,
and do frequency processing 0 ~ 130 m/s range on the top
surface of hill (Figure 5).
It can be seen from forwarding the model:
1. There appears beaded reflection characteristics in
seismic of cavern reservoirs, after frequency processing the
characteristics is amplified, showing beaded highlights,
features clear, easy to predict;
2. Fractured porous reservoir forms relatively weak
amplitude characteristics, easily predictable;
3. Fractured reservoir’s reflection characteristics not
obvious, it is difficult to predict.
cave. Reservoir has a good corresponding relationship with
the ancient landscape in the plane. The location and type of
reservoir development, is related to ancient karst and the
positions of the major faults. The continuity of the reservoir
in steep highlands in the karst area is poor, which is point,
and agglomerate; karst develops in the ramp area of karst
highland, where reservoir continuity is better, dendritic.
Reservoirs have characteristics of a sub-zone in the
longitudinal, 0-20 m/s reservoir develops, and increased
with depth, the reservoir has the trend of becoming better,
which may correspond to epikarst; 20-30 m/s reservoir
becomes worse, which might correspond to upper part of
vertical seepage; 30-40 m/s reservoir becomes better, which
may correspond to the lower part of vertical vadose zone;
40 m/s the following reservoir is better and may correspond
to the level of subsurface band (underground river).
3.1 Reservoir prediction of corrosion holes
3
SPECTRAL
TECHNOLOGY
DECOMPOSITION
Through forward analysis, we consider that the spectral
decomposition technology has a good ability to identify in
vuggy reservoir prediction, so the technology is transferred
into the carbonate reservoir prediction. The drilling
statistics show that the main type of reservoir is cavern and
Dissolution is divided into the cavern and cave reservoir
according to the size of holes, which is an important area of
oil and gas reservoir space. Unfilled or half-filled cave is a
symbol of high-yield wells. Cavern or cave reservoir
performs short axis, high amplitude, bead-like features in
the seismic profile (Figure 6, left), and shows vertical
continuous strong bright combination in the frequency
profile (Fig. 6, right). These reservoirs are calibrated to
11
Physical and Numerical Simulation of Geotechnical Engineering
5th ISSUE, Dec. 2011
coincide with drilling venting or mud losses well section.
Analysis of frequency data shows 0-30 m/s from the top
surface of the hill in vertical, the amplitude spectral
features is not obvious, 30-130 m/s or so beaded amplitude
bright is well developed (Fig. 7). As mentioned earlier,
drilling has confirmed that 30-130 m/s range is focused
developmental section of beaded characteristics in cave
reservoir, 0-30 m/s is mainly fractured reservoir. Reservoir
development reflection is mainly controlled by meteoric
water filtration, dissolution and transformation of the fault.
Figure 6 Seismic profiles feature and frequency profile feature of high-yield wells in the porous reservoir
Analyze frequency response characteristics from the
current high-yield wells; all the high-yield wells are located
near the point or mass-like features of deep fault.
According to this feature, first do space carving frequency
highlights in 30-130 m/s time window. The results showed
that there develops a large number of point or dough strong
amplitude characteristics representing cave reservoir
development in the time window, which indicated that the
reservoir has experienced strong early dissolution, with the
formation of a wide range of cave reservoir development
(Figure 8). Lower parts of the cave reservoir is easily to be
filled later by post-transformation, so comprehensive
analysis in conjunction with Palaeogeography depositional
environment and development situation of the faults is
needed, and drilling wells shall be selected in a high steep
hill in the ancient site near the fracture reservoir. It is
proved to obtain a high drilling success rate.
Buried hill surface
Figure 7 Development of case plans of cave reservoirs in the buried hill
Karst high-steep hill
Karst low-steep hill
Karst mesa
Figure 8 Development plan of cave reservoir in buried hill (buried under the surface of 30-130ms)
Left: the background of the ancient landscape; Right: background hill side
12
Application of Spectral Decomposition Technique in Carbonatite Reservoir Prediction
DOI: 10.5503/J.PNSGE.2011.05.002
3.2 Characteristics of underground river reservoir
Such reservoirs are generally developed in karst gentle
slope, connected with each other along the slopes, which is
generally evaluated as Class 2 reservoir because it is in the
lower part of the ancient landscape, and is often filled with
mud later. It is manifested in the frequency plan for the
connected, dendritic morphology, in the section on the
performance of long axis, vertical 2-3 combination of high
amplitude bright features (Fig. 9). Underground River is a
good oil and gas rich region in the structure of the higher
parts of the reservoir.
Figure 9 The development of underground river in the situation of hill reservoirs
4 CONCLUSIONS AND RECOMMENDATIONS
Through this experimental analysis, we consider that
spectral decomposition technique also applies to carbonate
reservoir prediction, and prediction of fractured reservoir
has achieved great success. Made the following
conclusions:
1) Spectral decomposition originates in the clastic thin
projections, the analysis and practice show that the
technique also applies to prediction of carbonate fissure
cave reservoir;
2) The actual cave carbonate reservoir was stratified in
the vertical distribution. To achieve the tuning effect,
window selection is critical. Here, we use 30Hz, step 30ms,
and the maximum entropy method predict the best cavern
reservoirs;
3) Carbonate buried hill in the area has experienced a
number of tectonic subsidence, there are multiple layered
fractured reservoir development buried under the surface.
We go through fine calibration, encryption layer
interpretation in the “looking for buried hill in hill.”
Through analysis of frequency plane properties predicted,
underground rivers and reservoirs are developed, there
comes the need for detailed study of the reservoir. The
current use of the technology is still only qualitative
analysis of the spatial development conditions of cave
reservoir, and it is difficult to quantify.
[1].
[2].
[3].
[4].
[5].
[6].
[7].
[8].
[9].
REFERENCES
13
Lopea A et al. Identification of deltaic facies with 3D
seismic coherency and spectral decomposition cube.
Abstract of Istanbul’ 97 International Geophysical
Conference and Exposition, 1997, 7-10
Greg Partyka, James Gridley and John Lopez. Interpretation
applications of spectral decomposition in reservoir
characterization. The Leading edge, 1999 (3): 353~358
HUANG Xude. Discussion on notches-in-thin-bed [J].
Progress in Exploration Geophysics, 2002, 25 (5): 1-5 (in
Chinese)
JIANG Xianyi, LIU Xiangong, SONG Kui. Forward
simulation of complex structure model [M]. Beijing:
Petroleum Industry Press, 2004
YANG Lin. Discussion on the problems in using the
technology of seismic spectral decomposition [J].
Geophysical Prospecting for Petroleum, 2008, 47 (4):
405-409 (in Chinese)
ZHANG Yazhong, ZHAO Yuhui, LU Xinbian, LIU
Zhesheng, YE Jianwei and SONG Bohu. Application of
spectral factorization technique to predict carbonate
fracture-vuggy reservoir in TH area of northern Tarim basin
[J]. Oil Geophysical Prospecting, 2006, 41 (supplement):
16-20 (in Chinese)
WANG Peng, ZHONG Jianhua, ZHANG Hongwei, YANG
Shuhua and AN Peng. Application of Spectral
Decomposition Technique in Complex Fault Block Area [J].
Journal of Oil and Gas Technology, 2010, 32 (3): 248-251
(in Chinese)
YUAN Zhiyun, KONG Linghong and WANG Chenglin.
Application of spectrum decomposition in reservoir
prediction [J]. Oil Geophysical Prospecting, 2006, 41: 12-15
(in Chinese)
CAI Gang, LV Ximin, SU Mingjun, GONG Honglin and
YAO Qingzhou. Application of frequency spectrum
decomposition technique in exploration in the JUNGGAR
basin [J]. Natural Gas Industry, 2008, 26 (4): 35-38 (in
Chinese)