Hybrid Seismic Attribute for identifying geological Features

7/24/2019 Hybrid Seismic Attribute for identifying geological Features

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Hybrid Seismic Attribute for identifying geological Features

Mohamed I. Shihataa, IPS

Abstract

Seismic attributes used to identify and isolate important geological features

from seismic data, while no unique attribute is expected to perfectly identify the

targeted object, various attributes contributing to the same purpose should be utilized

simultaneously when performing detection. In this work we present new hybrid

attributes generated by combining various seismic attributes to enhance identifying of

interested geological features from seismic data, by combining different spectral

bands frequencies to increase signal-to-noise ratios, one of new hypride attributes

average SD(spectral decompositions ) attributes, this attributes generated by

combination divergent types of seismic attributes to eliminate noises effect and reduce

effect of un wanted geological feature, average SD attribute used to generatesimilarity attribute to improve shallow channel detection and guidance to determine

gas migration pass, it is important to combine faults attributes with amplitude

attributes to identify faults trends, To validate the proposed method we use the

volume of the Netherlands offshore F3 block downloaded from the Open Seismic

Repository, average SD deliver promising results for both shallow and deep thin

geological features interpretation because it combine different bands frequencies in

one volume. Furthermore, the results show that average SD attributes can use for

predict gas migration pass and faults attributes help for identify shallow minor faults.

Introduction

Seismic attributes are defined as any measure of seismic data that helps to

visually enhance or quantify features of interest. A good seismic attribute is either

directly sensitive to the desired geologic feature or reservoir property or allows us to

define the structural or depositional environment and thereby enables us to infer some

features or properties of interest (Chopra and Marfurt, 2007). In the last decades

numerous published works have documented the successful use of seismic attributes

to explore for hydrocarbon-bearing sediments and to extract key information about

their lithology and their different saturating fluids (Hardage et al., 1996a; Chopra and

Marfurt, 2007; Chen et al., 2008).

Spectral-domain seismic data attributes have been useful for some applications

in hydrocarbon-reservoir characterizations. For example, Dilay and Eastwood (1995)

analyze seismic data in the spectral domain for monitoring bitumen production by

cyclic steam stimulation (steam injection) at Cold Lake,Alberta, Canada. Partyka et

al. (1999) discuss spectral-decomposition analysis and interpretation of 3D seismic

data. Extracting the spectral components at different dominant frequencies may

provide more precise perspectives of given geologic structures. For example, the

thickness of a channel and its spectral amplitude are strongly correlated (Laughlin et

al., 2002). spectral decomposition could be used to image hydrocarbon sands at


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certain frequency bands (Burnett etmal., 2003; Sinha et al., 2003). The seismic

response of a given geologic feature is expressed differently at different spectral

bands. Often, a particular frequency component carries the information regarding

structure and stratigraphy. Spectral decomposition methods map 1D signal into the 2D

time and frequency plane, generating amplitude and phase spectral components

(Castagna et al., 2003). Sun et al. (2010) use discrete frequency coherence cubes infracture detection and find that high-frequency components can provide greater detail

Combination spectral decomposition. Farfour and Youn (farfour and youn, 2012) used

frequency decomposition for delineating stratigraphic traps and identifying subtle

frequency variations caused by hydrocarbons. The application of complex spectral

coherence shows that it is useful for detecting different-scale structural and

stratigraphic discontinuity features (Li and Lu, 2014).

In this work, we used different hybrid attributes to identify important

geological features that hard to determine by unique attribute, average SD attributes

has been developed based on seismic spectral decomposition analysis, this method

was started by removing high and low frequencies noises depend on our targetsfrequencies band and used mean smooth filter to reduce effect of foot print noises, our

first target to generate new hybrid attribute (average SD) to identify thin shallow

channels trend, first step depend on determine channel dominant frequency using

tuning thickness analyses for extracted wavelet. Then generate spectral band

frequencies around dominant frequency .Finally, average SD attribute was generated

to enhance thin channel interpretation. Calculation similarity attribute by average SD

shows that it is useful for enhancing thin geological features interpretation and obtains

promise results for shallow and deep geological features interpretation. In order to

evaluate the proposed method, we use the volume of the Netherlands offshore F3

block downloaded from the Opendtect website and compare the obtained results with

normal amplitude and spectral decomposition attributes, we conclude that this new

simple average attributes help to identify thin channels with different frequencies

bands.

Geologic Background and Seismic Data

F3 is a block in the Dutch sector of the North Sea (Figure 1). The block is

covered by 3D seismic that was acquired to explore for oil and gas in the Upper-

Jurassic Lower Cretaceous strata, which are found below the interval selected for

this demo set (Figure 2) . The upper 1200ms of the demo set consists of reflectors

belonging to the Miocene, Pliocene, and Pleistocene. The large-scale sigmoidal

bedding is readily apparent, and consists of the deposits of a large fluviodeltaic

system that drained large parts of the Baltic Sea region (Sorensen, 1997; Overeemetal,

2001).

The structural and depositional development of the southern North Sea basin

has been well documented. At the large scale the Southern North Sea sedimentary

basin can be seen as a basin dominated by rifting during most of the Mesozoic with a

Cenozoic post rift sag phase. Rifting already started in the Triassic, and culminated in

the Jurassic and Early Cretaceous with the various Kimmerian extensional tectonic


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phases related to the opening of the Atlantic Ocean. Active rifting was followed by a

post-rift sag phase from Late Cretaceous to Present, which was mostly characterized

by tectonic quiescence and subsidence of the basin, with the exception of a few

compressional tectonic pulses during the Late Cretaceous and Tertiary. During most

of the post-rift phase the basin accumulated thick sedimentary mega-sequence (

Schroot, B.M., 2002(

Figure.1 Satellites map of F3 a block in the Dutch sector of the North Sea.

Figure 2. Netherlands offshore sector. Showing license blocks. Locations of 2D and

3D Survey.

Only in the very south the Pliocene-Pleistocene is overlying much older

Tertiary deposits. In the same area crag-like deposits were very locally deposited in


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Pliocene-Pleistocene times, similar to those presently outcropping in East Anglia

(Cameron et al, 1989a). Coastlines shifted back and forth over the Netherlands North

Sea and surrounding areas from the end of the Pliocene onwards (Sha, 1991) leading

to a variety of sedimentary environments and grain sizes.

Average SD (spectral decomposition) Attributes Workflow

Spectral decomposition was expected to reveal stratigraphic features of the

channel that could not be seen in seismic images. To accomplish this, different

frequencies were calculated for a single time slice at this interval (Farfour and Youn ,

2012). Over the last decades, several studies have demonstrated that spectral

decomposition can provide more interpretable results if it is integrated with edge

attributes. To handle this problem, it is prefer to divide seismic data to several spectral

bandwidth and average the best three frequencies bands to generate new hybrid

average SD attribute (Figure 3).

Similarity is an ideal attribute in mapping lateral variation in waveform withindefined time window; but it is relatively insensitive to amplitude change. In a very

thin bed reservoir, the below tuning implies that the waveform stabilizes and only

seismic amplitude changes; thus, similarity is not the appropriate attribute. On the

other hand, spectral decomposition is known to be a good indicator of amplitude

change

Figure 3 An example broadband trace (left), its spectrogram (middle) with the

limiting frequencies indicated in white and the band-limited reconstructions (right) for

the three frequency bands.( Lowell, J., Eckersley, A., Kristensen, T., Szafian,

P.,2014)

Average SD attributes depend on detecting dominant frequency for interested

geological features time window, first similarity attribute was used to identify shallow

channels time window from 0.8 s to 1.04s, then Dominant frequency found by tuning


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thickness analysis using extracted wavelet surrounded interested channel interval.

Finally, average three frequencies combined around dominant frequency to generate

Average SD attributes that reduce effect conflict of other uninterested spectral band

and eliminated noises effect of other bands.

Figure 4(a) shows Survey spectrum at survey time interval where there aredifferent band widths interfere with interested channel band width frequencies. Figure

3(b) presents Survey spectrum at channel interval shows dominant frequency around

60 Hz. A noticeable decrease from 90 to 60Hz is associated to high Frequency

attenuation and absorption while traveling to deeper formations

Figure 4. a)Survey spectrum at survey time interval, b) Survey spectrum at channel

interval shows dominant frequency around 60 Hz.

A layer is called a thin layer when 1 < /d 4, and an ultra-thin layer when,

/d > 4, where is the dominant wavelength within the layer and d is the layer

thickness (Liu and Smith 2003). Tuning Analysis allows analyzing tuning thickness

from frequencies content of the wavelet. Geologic layers did not identified at one

frequency/wavenumber or in a broadband display may be prominent at the specifictuning frequency that relates to the actual layer thickness. It is important to understand

that spectral decomposition can reveal the acoustic response related to certain

thicknesses. The interpreter must determine whether this spectral decomposition

acoustic response relates to actual bed thickness.Tuning and survey spectrum analysis

was run at this channel interval inferred that the dominant frequency was around

60Hz (Figure 5).

Where tuning thickness = 1/4*

Actual time thickness need for tuning thickness = 1/4* P

So p = 4*(actual time/2) =4*.008/2= 0.16 s (1)

b)a)


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FD = 1/p = 1/.0 16= 62.5 Hz (2)

Where = wave length, p = periodic time, FD= dominant frequency.

Spectral decomposition calculated for different bands width frequencies

around dominant frequency, tuning curve analysis used wavelet extracted around time

window of shallow channel from 0.8 to 1.04 s, Tuning and survey spectrum analysiswas run at this channel interval inferred that the dominant frequency was around

60Hz from equation (1) and (2) where P is periodic time and FDrepresent dominate

frequency.

Figure 5.Tuning analysis for extraction wavelet.

I3D (Illuminator-3D) attributes application

A variety of different seismic attributes, such as Symmetry and Similarity for

example, can reveal and display fault patterns in a formation. However, actual fault

patterns in a formation may not be continuous, and a single fault may appear as a

combination of seemingly isolated parts. In addition, horizontal footprints may coexist

in the fault attributes in great numbers further obscuring the faults. Fault analysis can

be done more easily if isolated parts of a single fault can be connected together into asingle piece, while footprints of low dips can be removed. The I3D algorithm (patent

pending) performs these operations which enhance the fault image in all spatial

directions. I3D Energy, Dip, and Azimuth are generated to represent the fault

distribution patterns in the fault attribute volume. Enhancing the fault attributes

improves automatic and manual fault extraction workflows, regardless of the fault

attributes that are being enhanced. Figures 6 shows the resulting attributes present

smoother and cleaner curve lines or plane patterns of sharper contrast with additional

dip and azimuth information.


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One unique feature of this attributes is that it does not require a spatial context

window. It is inspired by the neuronal mechanisms of the primary visual cortex for

orientationperception (Yingwei Yu, Cliff Kelley, and Irina Mardanova,2013)

The orientation energy E reflects the strength of orientation features. The low

values of orientation energy mean that there are fewer oriented patterns in the

neighborhood, while the stronger ones mean the orientation feature is more salient in

the context. Figure 7 shows an example of the orientation vector field (OVF)

Figure 6: Rotational Symmetry in a 3D Seismic Volume

Figure7: Orientation Vector Field near a Salt Dome. The orientation vectors (red) are

plotted on top of the seismic image in a region near the salt dome. The magnitudes of

the vectors are normalized (modified after Yu, Kelley and Mardanova2013)


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Results

Compared results of spectral decomposition frequencies confirm our proposal

dominant frequency where edge of channel have been enhanced after extracted SD

frequency 62 Hz, Figure 5 compares between normal amplitude and amplitude for SD

frequency 62 Hz at same time slice there are improve in edge of channel and increase

resolution of reflectors (Figure 8).

Figure 8.Normal amplitude slice at 1.036 s (left), amplitude for SD frequency 62 Hz

(right).

Average SD attributes calculated by combining best three frequencies around

thin channel dominant frequency to enhance channels edge and depend on determine

dominant frequencies by tuning analysis (Figure 9), this attribute combine different

band frequencies to enhance thin channels, Figure 4 shows average SD merge the best

three frequencies around dominant frequency (55-64-70 Hz) at 1.036 s to enhancechannel edge compare with normal amplitude at same time slice, this attribute add

valuable geological information

Similarity is an ideal attribute in mapping lateral variation in waveform within

defined time window; but it is relatively insensitive to amplitude change. In a very

thin bed reservoir, the below tuning implies that the waveform stabilizes and only

seismic amplitude changes; thus, similarity calculated by normal amplitude is not the

appropriate attribute. On the other hand, spectral decomposition is known to be a

good indicator of amplitude change,to handle this problem, average SD attributes

used to calculate similarity attributes to enhance subtle channel detection better than

using normal amplitude, combination best detected three frequencies bands thatreduce random noises and maximize amplitude for interested stratigraphic target and


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reduce effect of conflict of unwanted signal. Figure 9 compares between similarity

generated by normal amplitude and average SD attribute, subtle thin channels system

easily identified in right image especially in middle and in east part. In other side,

noises and unwanted bands signals reduce channels system in left image.

reduce effect of conflict of unwanted signal. Figure 9 compares betweensimilarity generated by normal amplitude and average SD attribute, subtle thin

channels system easily identified in right image especially in middle and in east part.

In other side, noises and unwanted bands signals reduce channels system in left

image.

Figure 9.Amplitude slice at 1.036 s for SD frequency 62 Hz (left), Average attributes

time slice at 1.036 enhance channel image (right).

Figure 9.Comparing between similarity attribute calculated along normal amplitude

(left) and similarity attributes calculated along average SD (55-64-70 Hz) attributes

(right) with white black color.


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There are another important advantage for average SD attributes it can use for

DHI and reduce noise for similarity attributes results, figure 10 compare between

amplitude and average SD attributes for inline 690, left map present amplitude

attributes where it is hard to identify shallow gas indication, right line represent

average SD attributes average four bandwidth frequencies (8, 25, 40, 60 Hz) that

determined from spectral analysis for seismic cube as Figure 3.

Figure10. Normal amplitude attributes for inline 690 (left), average SD attributes for

inline 690 (right).

Figure 11 show usage of average SD attributes to identify shallow bright spot that

hard to detect by normal amplitude, right map represent amplitude time slice at 0.624,

left map show average SD attributes at 0.624 where two black circler isolate two

important bright spots that hard to detect in right map.

Figure11: Normal amplitude time slice at 0.624 (left), average SD attributes timeslice at 0.624 (right).


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it is important to eliminate effect of noise in the similarity attributes results to enhance

geological features detection, calculated similarity attributes using average SD give

good result for fault detection and reduce effect of noise , figure 12 show comparison

between similarity attributes calculation

Figure12. Similarity attribute calculated using normal amplitude(left), similarity

attribute using average SD (right).

Figure 13 represent comparison between symmetry attributes and new fault

attributes, right figure represents symmetry attributes time slice at 0.624 where it is

hard to identify faults because noises effect on results, left figures represent new faultI3D illuminators energy attributes where it enhance fault image and reduce effect of

noise because it depend onorientation pattern is analyzed in frequency domain, and

inspired by the neuronal circuits in the biological brain.

Figure 31: Symmetry attribute at time slice 0.640 (left), I3D energy attribute

calculated from symmetry attribute (right).


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Blend fault attributes I3D energy with edge attributes enhance fault image, multi-

attributes help to identify faults trends and reduce risk of seismic interpretation

(Figures 41).

Figure 3: Blend fault attributes I3D energy with amplitude attributes to enhance

faults interpretation and aid to identify edge of gas chimney and reduce risk.

Dip maximam similarity isvery important to identify geobodies with highly

dip and high contrast between surrounding lithology, there are a lot of geobodies

effected by gas migration from gas chimney unfortunately geometric attributes alone

hard to identify lithology change or predict gas accumulation but can identify edge of

geobodies and edge of gas accumulated, it is important to combine physical with

geometric attributes for identify geobodies and lithology change (Figures 15,Figure

16).


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Figure 15: Shallow geological features may be indicate for gas migrated and

accumulated.

Figure 16: Blending average energy with similarity attributes indicate shallow gas

accumulated.


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Conclusion

average SD attribute used to enhance similarity attributes results and improve

seismic interpretations for shallow, it is important to merge different bands

frequencies cubes in one volume, to handle this problem, average SD attribute was

created to sum absolute values for three bands frequencies and generate one volume

for important bands frequencies, this new hybrid attribute eliminated foot noises

effect and reduce effect of un wanted geological feature, average SD attribute used to

generate similarity attribute to improve shallow channel detection and guidance to

determine boundary of deep reservoir, average SD deliver promising results for both

shallow and deep geological interpretation because it combine different bands

frequencies in one volume.

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Hybrid Seismic Attribute for identifying geological Features