Application of Spectral Decomposition Technique in Carbonatite Reservoir Prediction

Application of Spectral Decomposition Technique in Carbonatite
Reservoir Prediction
YANG Yingjun1, ZHENG Junmao1, LV Youliang1, 2
1. China University of Geosciences, Beijing, China, 100083
2. Landmark Graphics International Inc. Beijing office, 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 obtained 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 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 frequency depression phenomenon (Greg et al, 1999). 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
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the thickness of the thin layer.
Traditional spectrum analysis method and spectrum decomposition techniques differ mainly in the 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 characteristics of thin layer (Fig. 1, right).
Figure 1 Comparison of long time window and short time window spectral decomposition and convolution
model
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 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 2 Tuning body process
Figure 3 Discrete frequency energy processes
2. The discrete frequency body
The discrete frequency body refers to the tuning amplitude data of a series of discrete frequency
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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 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.
1.2 Calculation method
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.
1.2.1 Short time Fourier algorithm
Use Fourier transform to calculate the amplitude of each frequency from the beginning to the end
frequency, using the following formula
M
G (k . f ) =
−i
N
∑ ∑ g∑
n=0 n=0
e
(m ,n )
2π
mk
M
e
−i
2π
nf
N
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. 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.
1.2.2 Maximum entropy algorithm
2πif∆t
The method is based on the Z transform algorithm: Z ≡ e
, ∆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.
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
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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
0 200
201
251
301
351
401
451
Model Road
Reference 250
300
350
400
450
500
Amplitude
Reflection amplitude
100
90
80
70
60
50
100
Theoretical change rate (relative
Model
reference channel)
0
-10%
-20% -30%
-40%
-50%
0
～
Frequency tuning tuning amplitude
amplitude(frequency change rate (relative
30hz)
reference channel)
～
33
16.2
0
50.1%
～
0
100%
～
～
～
～
-15.8
-32.6
-47.8
33
148%
199%
245%
0
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
6134m / s, fractured layer velocity is 5988m / s, pore and fracture pore layer velocity is 5813m / s, and
the cave layer velocity is 4232m / s. Analyze the above speed data, caves and rock layer velocity
difference 1902m / s, with the relative speed difference 30%; holes, fractured porous layer and rock
layer velocity difference 321m / 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
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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
~ 130ms 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.
3 Spectral Decomposition Technology
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 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 a
characteristics of a sub-zone in the longitudinal, 0-20ms reservoir develops, and increased with depth,
the reservoir has the trend of becoming better, which may correspond to epikarst; 20-30ms reservoir
becomes worse, which might correspond to upper part of vertical seepage; 30-40ms reservoir becomes
better, which may correspond to the lower part of vertical vadose zone; 40ms the following reservoir is
better and may correspond to the level of subsurface band (underground river).
3.1 Reservoir prediction of corrosion holes
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 coincide with drilling venting or mud losses well section.
Analysis of frequency data shows 0-30ms from the top surface of the hill in vertical , the amplitude
spectral features is not obvious ,30-130ms or so beaded amplitude bright is well developed (Fig. 7). As
mentioned earlier drilling has confirmed that 30-130ms range is focused developmental section of
beaded characteristics in cave reservoir, 0-30ms 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
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carving frequency highlights in 30-130ms 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.
Figure 7 Development of case plans of cave reservoirs in the buried hill
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
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.
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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.
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