Characterization and Identification of Gas Hydrate Bearing Sediments of Oman Sea Using Seismic Methods

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1 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) 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. Maryam_sarmadi@ut.ac.ir 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 metaattributes 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 19 th century by Davy [2]. In 1930, formation of gas hydrate in gas pipelines and their obstruction effect attract more attention to them in oil

2 Title of paper (6 words) (Times New Roman 10.5 pt. Italic Bold) (Note that all the even pages should be like this) 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 m 3 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 lowtemperature 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).

3 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) Fig.1. Seismic section with obvious BSR 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 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 sin 2 ( ) (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.

4 Title of paper (6 words) (Times New Roman 10.5 pt. Italic Bold) (Note that all the even pages should be like this) 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. 2.a 2.b Fig.2. a. Part of Intercept section. b. Part of gradient section 3.a 3.b Fig.2. a. Poissons ratio reflectivity. b. Fluid factor section 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.

5 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) Fig.3. Amplitude versus offset curve for CDP 7031 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 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

6 Title of paper (6 words) (Times New Roman 10.5 pt. Italic Bold) (Note that all the even pages should be like this) 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). Fig.5. Training data set with 45 points above and 45 ones below the BSR 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.6. Test data set including a square of data which containing the BSR

7 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) 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, and 28.11%. As can be seen, the error of LFDA attributes pattern, less than the.

8 Title of paper (6 words) (Times New Roman 10.5 pt. Italic Bold) (Note that all the even pages should be like this) Table 1. Initial attributes with their characters. Symbol Initial attributes with their characters 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 Mexican Hat 40 : SpecDecomp transformtype=cwt cwtwavelet="mexican Hat" spectral decomposition Mexican Hat 50 : SpecDecomp transformtype=cwt cwtwavelet="mexican Hat" spectral decomposition Mexican Hat 60 : SpecDecomp transformtype=cwt cwtwavelet="mexican Hat" spectral decomposition Gaussian 30 : SpecDecomp transformtype=cwt cwtwavelet=gaussian spectral decomposition Gaussian 40 : SpecDecomp transformtype=cwt cwtwavelet=gaussian Variance : VolumeStatistics stepout=0,2 shape=ellipse gate=[-20,20] nrtrcs=1 spectral decomposition Gaussian 50 : SpecDecomp transformtype=cwt cwtwavelet=gaussian spectral decomposition Gaussian 60 : SpecDecomp transformtype=cwt 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

9 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) spectral decomposition Morlet 30 : SpecDecomp transformtype=cwt cwtwavelet=morlet spectral decomposition Morlet 40 : SpecDecomp transformtype=cwt cwtwavelet=morlet spectral decomposition Morlet 50 : SpecDecomp transformtype=cwt cwtwavelet=morlet spectral decomposition Morlet 60 : SpecDecomp transformtype=cwt cwtwavelet=morlet Norm Variance : VolumeStatistics stepout=0,2 shape=ellipse gate=[-20,20] nrtrcs=1 Seismic 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 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 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.

10 Title of paper (6 words) (Times New Roman 10.5 pt. Italic Bold) (Note that all the even pages should be like this) 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. 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. 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

11 2nd National Iranian Conference on Gas Hydrate (NICGH) Semnan University (Note that all the odd pages should be like this) 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. 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. References [1] Kvenvolden, K.A., "A primer on gas hydrate; The future of energy gases", U. S. Geol, Survey Professional Paper, Vol. 1570, [2] Davy, H., "On a combination of oxymuriatic gas and oxygen", Philosophical Transaction of the Royal Society, Vol. 101, [3] Hammerschmidt, E.G., "Formation of gas hydrates in natural gas transmission lines", Industrial and Engineering Chemistry, Vol. 26, [4] Shipley, T.H., and Didyk, B.M., "Occurrence of methane hydrates offshore southern Mexico", Initial Reports of the Deep Sea Drilling Project, Vol. 66, [5] Kvenvolden, K.A., "Methane hydrate- a major reservoir of carbon in the shallow geosphere", Chemical Geology, Vol. 71, [6] Thakur, N.K., and Rajput, S., Exploration of Gas Hydrates, Geophysical Techniques, Springer, 2011.

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