characterization and identification of gas hydrate...

2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University

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Characterization and Identification of Gas Hydrate Bearing

Sediments of Oman Sea Using Seismic Methods

M.Sarmadi Doost

1*, F.Erfani

2, H.Hashemi

3, M.Sokooti

4, M.Sadiq-Arabani

5

1,2,3 Institute of Geophysics, University of Tehran

4, 5 Exploration Directorate, National Iranian Oil Company, Tehran, Iran.

[email protected]

Abstract

Gas hydrates attracted worldwide attention due to their potential as huge energy resource in the

recent decades. Therefore identifying and prospecting them is essential for strategic hydrocarbon

reservoir management. Seismic methods are known as a powerful gas hydrate exploration

methodology. In this research pre-stack seismic attributes have been used to identify elastic

properties and qualitative hydrate saturation of sediments. Using AVO analysis on pre-stack

seismic data, occurrence of gas hydrate has been confirmed in the Oman Sea. Also post-stack

seismic meta-attributes (applying pattern recognition and classification methods on several

attribute planes) have been successfully used to make separation between hydrate and non-hydrate

sediments. Joint use of pre- and post-stack seismic attributes will be a good evaluation techniques

for confirmation of this study.

Keywords: Gas hydrate, Oman Sea, AVO, LFDA, Seismic Attributes, multi attribute analysis.

Research Highlights Using a pattern recognition system that has been designed for building seismic meta-

attributes in identification of gas hydrate sediments.

Innovative application of Local Fisher Discriminant Analysis (LFDA) as strong feature

extraction techniques on seismic data. This is not used on any seismic data before.

Pre-stack attributes can be used successfully for identification of gas hydrated sediments.

1. Introduction

Gas Hydrates are a kind of unconventional gas resources which are found below the surface

of the earth, in polar zones, deep seas and oceans. They are naturally occurring crystalline

material compose of water and light hydrocarbons (mainly methane), which gas molecules are

entrapped in cage consist of water molecules [1].

For the first time, gas hydrates were found in 19th

century by Davy [2]. In 1930, formation of

gas hydrate in gas pipelines and their obstruction effect attract more attention to them in oil

Title of paper (6 words) ……(Times New Roman 10.5 pt. Italic Bold)

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exploration industry [3]. In 1980s, they were exploited in marine areas of Mexico [4];

thereafter their presence was reported in Pacific and Athlantic oceans [1].

Abundant presence of methane which is considered as a clean fossil fuel is seen in gas hydrate

sediments. The amount of stored methane in 1 m3 of methane hydrate is equal to 164 m

3 of

methane gas in standard pressure and temperature [5]. Gas hydrates contain more than half of

the organic carbon of the earth [1]. Therefore, they become an enormous resource of natural

gas and are considered as a main energy resource for future. But their importance has other

aspects. The occurrence of gas hydrate makes the sediments vulnerable to physical and

chemical changes in the surface of the earth. The changes in the environmental conditions

may lead to destabilize hydrates and it may cause release of large amount of gas in the

atmosphere. It also can cause slope instability of the seabed [6].

Gas hydrates change the seismic properties of their host sediments. Therefore, seismic

methods are useful to identify them. Seismic attributes are one of the appropriate tools in

seismic investigations that can be used in characterizations of gas hydrates. Seismic attributes

decompose all the complex information which is recorded inside seismic traces to more

comprehensive ones.

In this research, two types of attributes have been used. These attributes relate to pre-stack

and post-stack seismic data. Pre-stack seismic data obtained from one or more common depth

points (CDP) and they are dependent to azimuth and offset. In stacking process a kind of

averaging is applied and azimuth and offset effect are omitted, but the time relations are

preserved.

In pre-stack study, AVO (amplitude variation with offset) attributes have been used to extract

of seismic properties of the gas hydrate sediments. Post-stack attributes were used to separate

gas hydrate sediments from non-hydrate ones. For this purpose, some attributes were

combined by LFDA (local Fisher discriminant analysis) method and new attributes were

produced which can follow this goal.

2. Pre-stack study

Presence of gas hydrates changes the elastic properties of sediment and makes them

detectable on seismic sections. The most important seismic marker of gas hydrates is bottom

simulating reflector (BSR) that is caused by the hydrated sediments underlain by either brine

or free-gas saturate sediments. BSR marks the base of the high-pressure and relatively low-

temperature zone in which hydrates are stable [7].

BSR shows specific characteristics that make it detectable on seismic sections. BSR is

approximately parallel to seabed reflector topography. It has reversed polarity compared to

the seabed reflection. BSR cut across the dipping strata which are not parallel with water

bottom. Because of high acoustic impedance difference between the hydrated sediment above

and free gas saturated sediment bellow the gas hydrate stability zone BSR is a strong reflector

with high amplitude [8]. There are more attributes related to gas hydrate such as flat spot and

bright spot.

Deep parts of Oman Sea have been confirmed as favorable region for presence and stability of

gas hydrate by geochemists and geologists. In geophysical view BSR and other gas-hydrate

related attributes are clearly observed on the seismic sections of Oman [9]. Here the study

area is Oman Sea, BSR is chosen as a gas hydrate indicator and seismic sections with strong

and obvious BSR are selected for various analysis( figure 1).



AVO is a useful technique to detect if there is free-gas bellow the BSR or not. Various AVO

attributes such as intercepts (A) and gradients (B) and their various combinations were

computed for BSR and free gas bellow the BSR on seismic sections of Oman Sea. Seismic

attributes section derived from AVO analyses indicate that both gas-hydrated and free gas

saturated sediment can be exist in places where BSR is observed. Type IV of AVO anomaly

has been determined for BSR which has been studied in this research.

2.1. Theory

The variation of the P-wave amplitude with offset can be used as a direct hydrocarbon

indicator [11]. The basis of AVO analysis is described by Zoeppritz equations. They express

the reflection amplitude changes with the incident angle (offset). There are many

approximations for the Zoeppritz equations.

Aki-Richards approximation 12] linearizes Zeoppritz AVO Equation as follows:

R (θ) =A + B sin2 () (1)

Where R (θ) is the reflection coefficient, θ is the incident angle, A is the P-wave reflection

coefficient at normal incidence, and B is the gradient. AVO inversion has done based on

equation (1).

The input data were NMO-corrected CDP gathers and seismic velocity model data. The

processing sequences are as follows: geometry assignment, true amplitude recovery, statics

correction, linear noise attenuation, predictive deconvolution, first step velocity analysis,

residual static correction, second pass velocity analysis, radon demultiple attenuation final

velocity analysis, NMO, stacking and Pre-stack migration.

Pre-stack analysis was carried out by the Hampsson-Russel software and the A, B, A+B, A*B

and fluid factor attribute sections are obtained.

In Figure 2, attribute section of “A” is shown. Here “A” implies P-wave zero-offset reflection

coefficient, which indicates the variation of P-wave impedance. The BSR shows negative

values in the “A” profile (Figure, 2.a), which reveals that the impedance is smaller below the

BSR than above the BSR. Acoustic impedance increases in hydrated sediment due to their

compaction and decrease in gaseous zone due to their loosens.

Fig.1. Seismic section with obvious BSR



The attribute “B” reveals P-wave zero-offset reflection coefficient variations with incident

angles. In Figure 2.b, “B” section shows positive values along BSR. It indicates that absolute

value of the BSR amplitude decreases with incident angles.

The A+B section (Figure 3.a) which is identifier of Poisson’s ratio reflectivity, shows

negative values for BSR implying that the values of the Poisson’s ratio below the BSR is far

smaller than that above the BSR. This coincides with our expectations about nature of BSR

and suggests the existence of hydrated sediment above and free-gas bellow. It is because of

that hydrate may increase the Poisson’s ratio by cementing the sediments.

Fig.2. a. Part of Intercept section. b. Part of gradient section

Fig.2. a. Poissons ratio reflectivity. b. Fluid factor section

2.a 2.b

3.b 3.a



On the other hand free gas decreases the Poisson’s ratio as the result of decreasing the P-wave

velocity. As we can see in figure (3.b), the fluid factor also shows negatives values for BSR.

According to Fatti [13], we would expect nonzero values of F at the top and base of

gaseous zone.

As we can see in figure 4, absolute value of amplitude decreases with offset for a selected

CDP gather as an example. From cross plot of intercept versus gradient and amplitude versus

offset curves for different CDP gather type IV of Williams's classification is identified for this

BSR. For hydrate saturation above 50%, A is large negative and B is positive, which can be

classified as an indicator of type IV gas sands [14].

Other attributes sections such as P-wave impedance reflectivity, S-wave impedance

reflectivity, Pseudo poisson reflectivity, P-wave and S-wave velocity and Lame’s constant

were obtained as well. Based on these various attribute sections and angle gathers derived

from AVO analysis, using the typical methods of seismic data processing and the

identification experience of AVO attribute sections, we have identified occurrence of gas

hydrated and free-gas sediment in the area.

3. Post-stack study When there is a BSR in an area with favorable conditions for the formation and stability of

gas hydrates, it can be sure of gas hydrate existance in that region. But in some cases such as

Gulf of Mexico, gas hydrate were found in areas without BSR [15]. In these cases, it is

necessary to investigate another tools to identify gas hydrates.

In this study, seismic attributes have been selected as separation tools to segregate hydrate

sediments from nonhydrate ones. For this purpose, multi attribute analysis method has been

utilized and some pattern recognition algorithms – PCA and LFDA - have been used as a way

to combine initial attributes and produce some new attributes with more efficiency.

3.1. Methodology

In multi attribute analysis, two data sets are needed, test data and training data. Training data

is necessary to find appropritiate patterns for classification. Test data is used to investigate the

performance of the obtained pattern. To provide these two sets, the class of each object must

Fig.3. Amplitude versus offset curve for CDP 7031



be clear. For this purpose, a seismic section with obvious BSR has been used. BSR shows the

boundary of the gas hydrate and nonhydrate sediments (figure 1).

The training data must have the label. So 45 points from above the BSR and 45 ones from

below the BSR have been selected (figure 5). The selection of these points has been randomly

and there was no pattern to follow to choose them. Next, a square of 11 traces and 12 ms has

been considered around each point. These squares have formed the training data.

The lable of test data is not considered in calculations, but each point class must be clear to

show the accuracy of the classification. To this end, a square of data which containss BSR,

has been considered. This squre has been intended as the test data (figure 6).

To study the gas hydrates of Oman Sea, 27 different seismic attributes relating to basic

seismic characters – like frequancy, phase, amplitude and attenuation – have been selected

(table 1). All of these attributes have been made with OpendTect/dGB software.

Fig.5. Training data set with 45 points above and 45 ones below the BSR

Fig.6. Test data set including a square of data which containing the BSR



The amount of each initial attributes in each point of training and test data, has been extracted.

These valuse formed a data with 27 dimensions. Maybe it seems good. Because the more

attributes, make bigger amount of information and finally more accurate results. But it should

be noted that the bigger amount of data, causes the more complex situation in calculations,

while much of them are unhelpful. Because in each classification, there are some properties

that contain more important data and it makes them more useful in comparison with the rest.

Then, we need some algorithms to identify these significant properties – in this case initial

attributes – and reduce the dimension of data [16]. In this research, Principal Component

Analysis (PCA) [17] and Local Fisher Discriminant Analysis (LFDA) [18] algorithms have

been used to achieve this goal. These algorithms – according to the training data – noticing to

the importance of each attribute, in classification pattern, give a coefficient to each of them.

The greater coefficient, means the more impotance roll. With this method, some linear

combination are made from the initial attributes called new attributes which are more

efficience for classification.

After making new attributes, a neural network has been applied to classify the data.

3.2 The Roll of Dimensional Reduction Method in classification

There are many factors that effect on the quality of gas hydrates classification. One of them is

the dimentional reduction method. A proper method can produce some appropriate attributes

and they can result a reasonable pattern that could identify gas hydrate sediments among the

others.

In this research, two methods have been applied. The first is PCA which is an unsupervised

method. So, its training data does not have the lable. In PCA the number of new attributes is

the same of initial ones. But, the new attributes are sorted according to the scattering of the

data. This means that the first attribute is in the direction of the greater scattering of the data

and the second attribute is in the direction of the second greater scattering of the data and so

on. So, we can choose the first few attributes while we have the most of the required

information.

The other method is the LFDA. LFDA is a supervised method and its training data has the

lable. In this method the user can choose the number of new attributes. LFDA is the

combination of FDA [19] and LPP [20] methods. It is a proper way to assortment of a

multimodal data – that has some clusters – [18].

To compare these methods, training data has been extracted with OpendTect/dGB software

and transferred to MATLAB. Next, 3 points from each squares of data have been selected

randomly. Then the all selected data has been normalized and two dimensional reduction

algorithms have been applied on it.

The plots of the second new attribute in terms of the first new attribute of each method, has

been represented in figure 7. As can be seen, the LFDA attributes can separate the gas hydrate

points from nonhydrate ones, better than the other method.

For more exact investigation, the first five attributes of PCA, LFDA and the all of the 27

initial attributes have been entered to a neural network separately. The neural network had 5

neurons and has been repeated 15 times to achieve more exact results (figure 8).

The mean network error for the produced pattern of LFDA, PCA and initial attributes are

19.73, 27.93 and 28.11% . As can be seen, the error of LFDA attributes pattern, less than the

.



Table 1. Initial attributes with their characters.

Initial attributes with their characters Symbol

lowpass-20 : FreqFilter type=LowPass maxfreq=20 isfftfilter=yes

window=Hamming fwindow=CosTaper

energy-48 : Energy gate=[-48,48]

energy-28 : Energy gate=[-28,28]

Event : Event issingleevent=yes tonext=yes output=2`6'

average frq : Frequency gate=[-20,20] normalize=yes window=Hamming

Q : Frequency gate=[-10,10] normalize=yes window=Hamming

Max spectral amplitude : Frequency gate=[-20,20] normalize=yes

window=Hamming

Average frequency squared : Frequency gate=[-20,20] normalize=yes

window=Hamming

spectral decomposition Mexican Hat 30 : SpecDecomp transformtype=CWT

cwtwavelet="Mexican Hat"







spectral decomposition Gaussian 30 : SpecDecomp transformtype=CWT

cwtwavelet=Gaussian


cwtwavelet=Gaussian

Variance : VolumeStatistics stepout=0,2 shape=Ellipse gate=[-20,20] nrtrcs=1


cwtwavelet=Gaussian


cwtwavelet=Gaussian

lowpass-30 : FreqFilter type=LowPass maxfreq=30 isfftfilter=yes

window=Hamming fwindow=CosTaper

instantaneous amplitude : Instantaneous

instantaneous frequency : Instantaneous

instantaneous cos phase : Instantaneous



others. While the error of the PCA and initial attributes pattern are close.

Then, the value of each new attributes have been calculated for the each point of the test data

and the patterns which calculated with neural network, have been applied on that. The results

can be seen in figure 9. In this picture, it is clear that the LFDA attributes pattern can separate

gas hydrate zone from nonhydrate one and show this method is proper to identification of gas

hydrates of Oman Sea.

3.3. Discussion

The situation of a geoligical zone, is different in each part of the area. It happens because of

the lithological and geological changes. So, a geological data is not homogenous and has

many clusters. It means this data is multimodal.

For assortment of multimodal data, it is important to have a method that can recognize the

desired property among the different parts of a data, and can choose that parts without

noticing the other properties of them.

When we use the initial attributes in classification, all the attributes are considerd as the same

and they have the same coefficient in calculations, although they do not have the same roll in

spectral decomposition Morlet 30 : SpecDecomp transformtype=CWT

cwtwavelet=Morlet


cwtwavelet=Morlet


cwtwavelet=Morlet


cwtwavelet=Morlet

Norm Variance : VolumeStatistics stepout=0,2 shape=Ellipse gate=[-20,20]

nrtrcs=1

Seismic

Fig.7. The second new attributes in terms of the first attribute of Left: LFDA, Right: PCA. L1 and L2 represent

the first and the second LFDA attributes and N1 and N2 represent the first and second PCA attributes. The

blue points show the hydrates and the green.



classification. So, we do not have a good result from this attributes.

The PCA attributes are made based on the amount of the scattering of the data in each

direction. This means the more important PCA attribute is made in the direction of the greater

scattering of the data, the second important PCA attribute is made in the direction of the

second greater scattering of the data and so on. But this is not a appropriate way to recognize

the gas hydrate sediments. Because as we said before, a geological data is infulenced by many

events and the presence of gas hydrates is only one of them. So, many attributes maybe have

the same value, in gas hydrate and nonhydrate sediments (actually they do not affected from

the gas hydrate presence and the other natural phenomenas control them). So, the scattering of

the data is not a good criterion to identify the gas hydrates.

In LFDA method, a training data with lable, is entered in calculation. The algorithm gets the

characters of desired classification from this entrance data and can find the main attributes

that are effective in this classification. Further more, LFDA is a specific method for the

assortment of multimodal data. Then it is proper to identify the gas hydrate sediments.

4. Conclusions AVO analysis can be helpful to idenifing whether is hydrate and free gas above and bellowthe

BSR or not. Using this method we can determine the saturation of hydrate above the gas

hydrate satability zone.

Post-stack attributes are proper tools to identify gas hydrates. But the linear combinations of

them are more useful for this purpose. To make these combinations, we can use dimensional

reduction methods. According to this study, because of the multimodal characteristic of the

Fig.8. The used neural network algorithm. The inputs are attributes of each methods and the outputs are

the patterns that can classify the hydrate and nonhydrate points. This neural network has 5 neurons and

repeated 15 times to get more exact results.



geological data, unsupervised methods like PCA are not appropriate for the separation gas

hydrate sediments. But a supervised method, like LFDA is proper for this classification.

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Fig.9. The results of classification with different methods attributes on the test data. Above – Right:

seismic section of test data, Above – Left: LFDA result, Below – Right: PCA result and Below – Left:

initial attributes result. As can be seen, the LFDA pattern can classify the gas hydrates and nonhydrates

better than the other patterns.



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