应用叠前反演弹性参数进行储层预测_英文_

应用叠前反演弹性参数进行储层预测_英文_ Reservoir prediction using pre-stack inverted elastic parameters* 1,21,234Chen Shuangquan, Wang Shangxu, Zhang Yonggang, and Ji Min Abstract: This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre- stack data and inversion results are increasingly used for production objectives. The pre- stack seismic property studies include not only amplitude verse offset (AVO) but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/µ, and K/µ properties, which indicate the gas characteristics in the study reservoir. Keywords: elastic parameters, pre-stack inversion, reservoir prediction, AVO analysis, angle gather (AVO) characteristics in the pre-stack seismic data. The Introduction information correlated with AVO characteristics in pre- stack seismic data can be used to derive fluid indicators (Smith and Gidlow, 1987). Especially, elastic property Presently, hydrocarbon exploration is facing more parameters are useful to identity hydrocarbons in the complex surface and subsurface geological conditions reservoir (Fatti et al., 1994; Gray et al., 1999; Goodway throughout the world. Seismic data processing methods et al., 1997). Pre-stack inversion determines subsurface mainly focus on pre-stack seismic data. At first, we formation property contrasts across interfaces from the naturally consider studying amplitude verses offset Manuscript received by the Editor April 13, 2009; revised manuscript received September 27, 2009. *This research is supported by the National Basic Priorities Program “973” Project (Grant No. 2007CB209600) and China Postdoctoral Science Foundation Funded Project. 1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249 China. 2. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum, Beijing 102249 China. 3. Science and Technology Institute, China Petroleum & Chemical Corporation, Beijing 100086 China. 4. Overseas Research Center, Petroleum Research Institute of Exploration & Development, SINOPEC, Beijing 100086 China. angle dependence of seismic amplitudes. The objective with density could be useful tools in AVO analysis. Gray of pre-stack seismic inversion is a set of relative et al. (1999) showed how to estimate the parameters µρ and λρ more directly by a new parameterization of the property contrasts expressed by Δx/ , where x represents linearized AVO equation, as did Chen (1999). Russell et a formation property parameter, such as impedance, 2 2 density, velocity, or Poisson's ratio, Δx is the difference al. (2003) introduced the attribute I–cIwith c being a P S 2of the property across the interface, and is the average function of local (α/β), where α and β are the dry rock P- and S-wave velocities. Qiao and An (2007) analyze the value of the property above and below the interface. sensitivity of rock physics parameters using the well log These contrasts can also be expressed as reflectivity R. x Dewar et al. (2002) used well logs and laboratory data, and show that the fluid and lithology discrimination data to extend reservoir property rock physics to seismic can be greatly improved in the combined P- and S-wave attributes. The purpose was to understand the elastic type parameters. Thus, we can analyze the characteristic moduli in relation to three constituents: lithology, of these contrasts using the laboratory measurement core porosity, and fluid. For the fluid component, it was data to find some relationship between elastic property concluded that lambda decreases in the presence of parameters and hydrocarbon. Young and Tatham (2007) hydrocarbons and that the effect is larger with increasing claimed in their recent study that EI was one of three porosity, while µ is relatively unaffected by fluid content. successful AVO analysis techniques that were applied to In a comparison study, Feng et al. (2007) examined discriminate between true and false bright spots. These some hydrocarbon indicators to find which one can be provide a good empirical guide for studying the rock the most sensitive to pore-fluid content. It was concluded properties from seismic data. In this paper, we computed that there are various combinations of rock properties elastic parameter changes using the class I, II, III, and IV that have been proposed as hydrocarbon indicators and sand models given by Castagna et al. (1998), in order to 2 analyze the sensitivity of each hydrocarbon indicator and there is a great deal of equivalence in fluid indicators I P2 examine the performance of these indicators. –cI, K–µ, λρ, and λ/µ. The best indicator needs to be S Based on the previous research results, we set up a calibrated and tested for the local situation. Thus, we processing flow for detecting gas in the study reservoir, can analyze the characteristics of these contrasts using i.e., a method to invert the elastic parameters using pre- laboratory measurement core data to find relationships stack seismic and well log data. First, we analyze the between elastic property parameters and hydrocarbons. property changes between the elastic parameters using The starting point of pre-stack seismic inversion is core data under rock physics theory. The second step is usually the Zoeppritz equations (Aki and Richard, 1980) which relates the reflected and transmitted wave properties choosing the best hydrocarbon indicators. Finally, we get the inverted elastic parameter sections, which contain to the incident wave. It is very difficult to directly analyze the amplitude relationships using the Zoeppritz matrix some indications of reservoir and gas characteristics, allowing us to discriminate the anomalies caused by oil which doesn't have interrelated explicit expressions with and gas reservoirs from the surrounding rock. Therefore, rock physics properties. We must simplify the equations for analyzing AVO characteristics with different rock finding the suitable parameters, estimating the best seismic wavelet, and applying the proper experience physics properties, especially for detecting hydrocarbon from pre-stack seismic data. There are many ways of are the key points for reservoir prediction. Based on the pre-stack elastic property parameter inversion, we use simplifying Zoeppritz P-P reflection coefficient equation a field data example to examine which elastic property (Aki and Richards, 1980; Shuey, 1985; Smith and Gidlow, 1987; Verm and Hilterman, 1994; Wang, 1999). All of parameters or combination of parameters can most easily discriminate a gas sand from the background geology these simplified equations provide good AVO modeling results for different reservoir models. and which property is most sensitive to pore-fluid content. Comparing the inversion results to the well data, The challenge is how to perform pre-stack properties we found that it is useful to predict the gas reservoir inversion to directly detect the hydrocarbon information. using the λ, λρ, λ/µ, and K/µ property parameters which For pre-stack AVO analysis, Smith and Gidlow (1987) indicate the gas characteristics in the study reservoir. proposed the fluid factor and Castagna et al. (1985) combined the linearized AVO equation with the mudrock line. Gardner et al. (1974) also combined density and Elastic parameter response analysis P-wave velocity changes using the Gardner’s equation. A fluid factor which utilized density was introduced by Fatti et al. (1994). Goodway et al. (1997) suggested that AVO characteristics of pre-stack seismic gather the Lamé elastic parameters λ and µ and their products Chen et al. data is the response of the reflection, refraction, and above and below the interface. Subscript 1 is the layer transmission from subsurface lithologic interfaces. The above the interface and subscript 2 refers to the layer Zoeppritz equations correctly describe the amplitude is the reflectivity or change ratio below the interface. Rx a n d p h a s e c h a n g e s o f t h e p r e - s t a c k s e i s m i c d a t a of the property parameter x. For the rock physics property and AVO response with different incident angles when the rock physics parameters change across the interfaces. Thus, the basic characteristics analysis of gas sand reservoirs, Castagna et al. (1998) classified the reservoir into four types rock physics method of pre-stack seismic data analysis is the characteristics analysis of the parameter changes with according to the AVO curve changes. We analyzed the the interface. For gas-bearing reservoirs, the pore fluid elastic property parameter changes using the four gas sand reservoir models of Castagna et al. (1998) and is gas which differs significantly from the rock matrix in the reservoir. So, the elastic parameters affecting the found the effective hydrocarbon indicators (Figure 1). Table 1 shows the rock physics parameters for the analysis and finding the hydrocarbon indicators are the basic research to detecting gas in the reservoir. four gas sand reservoirs. Figure 1 is the computed The AVO characteristics of the pre-stack seismic results for the four gas sand reservoirs listed in Table 1 using equation (1). In the figure, the different colors data property parameters change above and below the interface. We can analyze the different elastic property indicate the different property parameter change values in the reservoir, with red indicating the largest, cyan parameter changes using the following equation indicating intermediate changes, and blue indicates ( x x) 2 1 , (1) the small changes. Table 2 lists the different elastic R x ( x+ x) / 2 2 1 property parameters and their combinations studied in this paper. where xand xare defined as the property parameters 1 2 Table 1 Parameters of the four gas-sand models Class P-wave velocity (m/s) S-wave velocity (m/s) Density (g/cc) Shale 2380 1100 2.30 I Gas sand 2840 1860 2.09 Shale 2500 1110 2.35 II Gas sand 2880 1810 1.99 Shale 2250 770 2.10 III Gas sand 1529 689 1.79 Shale 3050 1630 2.45 IV Gas sand 2660 1700 1.95 Shale-Gas Sand Elastic Properties Changes (Class I) Shale-Gas Sand Elastic Properties Changes (Class II) μ μρ μ E μρ 100 E100 β Is β λρ+2μρ β/α β/α Is λ+2μ Ipα α λ+2μ λρ+2μρρ Ip 0 0 KρKρ E/μ E/μ -100 -100 K λ σ Kρ λρ K/μ λ/μK/μ -200 -200 λ/μ λρ λ-300 -300 σ (a) (b) Shale-Gas Sand Elastic Properties Changes (Class III) Shale-Gas Sand Elastic Properties Changes (Class IV) 100 100 β/α μ β μρ β/α Is E λρ+2μρ λρ+2μρ 0 0 β ρ ρ E/μα α E/μIs Ipμ Ip λ+2μ E μρ σ K/μλ+2μ σ -100 K K -100 Kρ Kρλ/μ K/μλ λρ λ λρ λ/μ -200 -200 Avg. Change (%) Avg. Change (%) -300 -300 (c) (d) Fig.1 Gas sand elastic property parameter changes. (a) Class I gas sand. (b) Class II gas sand. (c) Class III gas sand. (d) Class IV gas sand. In the panels, the red, cyan, and blue colors indicate large, intermediate, and small change values. The plotted parameters are: α, β, ρ, β/α, I, I, λ, µ, K, E, σ, λρ, µρ, E/µ, λ/µ and K/µ. PS Avg. Change (%) Avg. Change (%) Table 2 The de,nitions of the elastic property parameters and their combinations Symbol Meaning Symbol Meaning α velocity P-wave β S-wave velocity λ Lamé coefficient modulus Shear μ ρ Density P-wave impedance IP K Bulk modulus Young’s modulus E λ+2μ, ρ+2μρ, Kρ, λρ, Elastic parameter combinations. μρ, E/μ, λ/μ, and K/μ Overlying layer Subscript 2 Underlying layer Subscript 1 The seismic reflection signal is mainly affected by the reflection coefficient equation (Aki and Richard, 1980). impedance difference above and below the interface. p Aa A 1 A1 2 2 2 2 2 However, the impedance change may not be the largest R (0 ) (1 4y sin 0 ) + sec0 4y sin 0 , PP 2 p 2 a parameter change. Analyzing Figure 1, we can see that the parameter changes vary. Of all rock physics (2) properties, density change is the smallest in classes I to where III, with S-wave velocity being the smallest in class IV O = (i+ i) / 2 1 2 (Figure 1d). It also means that for gas sand reservoirs, is the average angle of incident and transmitted P-wave the density inversion will be very unstable, so it can’ and t be regarded as an effective hydrocarbon indicator. y = 1/ a+ 1/ a/ 2 ) Moreover, P-wave velocity, S-wave velocity, P-wave 1 1 2 2 impedance, and S-wave impedance changes also aren’ is the average of the S- and P-wave velocity ratio above t the largest for the four classes of gas sand reservoirs. and below the interface. The other parameters are From Figure 1 we also see that for class I gas sand reservoirs (Figure 1a), the λ/µ change is the largest, Va = (a a), V1 = (1 1), Vp = (p p) ,2 1 2 1 2 1 followed by K/µ, µ, σ and µρ. For class II gas sand reservoirs, the largest change is σ, followed by λ, λρ, λ/µ, a = (a+ a) / 2, 3 = (3+ 3) / 2,2 1 2 1 and K/µ (Figure 1b). For class III reservoirs, the largest and changes are shown by λ, λρ, λ/µ, K/µ, and the class p = (p+ p) / 2. 2 1 IV gas-bearing sand reservoir is almost same as class III except that Kρ is also large (Figure 1d). Therefore, From equation (2), we can derive (Fatti, 1994) in order to effectively carry out gas-bearing reservoir R(9 ) = R(a ) + R(1 ) + R(p ) = cR+ cR+ cR, (3) detection, it is necessary to identify the parameters PP P P S S p p effectively indicating that gas is contained within the 2222 where c= secθ, c= -8γsinθ, γ = , and c= 4γsin θ– P S ρ reservoir, that is, the strongest parameter changes. It 2tanθ are P-wave velocity, S-wave velocity and density appears that the most effective elastic parameters are λ, reflection constants, respectively. λρ, λ/µ, K/µ, in class II and III gas sand reservoirs. In this The input seismic data for pre-stack inversion are study, the reservoirs are similar to class III gas reservoirs common-reflection-point (CRP) gathers which should so we use the elastic property parameters λ, λρ, λ/µ, K/µ, be the output of pre-stack time or depth migration. From as hydrocarbon indicators. equation (3), we see that the pre-stack seismic gathers should be in the angle domain. We can use a velocity model which can be made from well log data or seismic Pre-stack seismic inversion processing velocities to transform the CRP gathers to common angle gathers. After that, we consider that there are M seismic angle gathers and we can write out the The principle of pre-stack seismic inversion is simple. following equation using equation (3). We start with the Aki-Richard simplified PP-wave Chen et al. where W (θ) is seismic wavelet and D is a constant. R(O) r iri(1)c(1) c(1) Rcr i 1 P S p P As described above, we have modeled the behavior R. (4) S of a seismic trace T, at an angle θ as a function of the R(O)c( M ) c( M ) c( M )R M P S p p ] ] ]impedances and density in equation (6). Equation (4) is a typical geophysical inversion If we rewrite the reflectivity as the natural logarithm p r o b l e m : b a s e d o n o b s e r v e d d a t a d , e s t i m a t e t h e form of the impedance: model m, and the physical relationship can be written as d = Gm. From equation (6), we can establish a 1 1 ln I(i + 1) — ln I (i) R (i) A ln I(i)(5) 1 , P P P P basic iterative pre-stack inversion algorithm for the 2 2 problem. Thus, for processing, the input is the pre- where L= ln (I) is the natural logarithm of the acoustic P Pstack angle gather data, seismic wavelet, and the impedance, i is the index of the subsurface layer, and i n i t i a l v a l u e s f o r P - w a v e a n d S - w a v e i m p e d a n c e th L(i) is the P-wave impedance of the ilayer. Similarly, P and density. Finally, we can do some optimization L= ln (I) and L= ln (ρ). The pre-stack seismic data S Sρ to calibrate the inversion results using a computed equation can be written operator from well data. 1 1 1 T (0 ) = c W (0 )DL + c W (0 )DL + c W (0 )DL , (6)P P S S p p 2 2 2 2800 3200 4500 3200 3400 4520 3600 T1 4540 3600 4000 4560 3800 4400 T2 3900 4580 4800 2000 4000 6000 2000 4000 6000 2000 4000 6000 Velocity (m/s) Velocity (m/s) Velocity (m/s) (b) (a) (c) 3 11000 2.5 9000 2 7000 Vp/Vs ratio1.5 5000 Depth (m) 1 3000 3000 3500 4000 4500 0.8 1 1.2 1.4 1.6 (g*m/s*cc)×104 P-wave impedance Depth (m) (d) (e) Fig. 2 The well log data analysis results. (a) P- and S-wave velocity curves, (b) Enlargement near T1 in (a), (c) Enlargement near T2 in (a), (d) P- to S-wave velocity ratio,(e) P- and S-impedance crossplot for (b). Depth (m) s-wave impedance (g*m/s*cc) Depth (m) Wavelet Seismic Impedance Reflectivity Seismic Seismic Application of the inverted 1 2 1 2 3 1335 1340 3 1325 1330 1.0 elastic parameters The target reservoir is a set of tight gas- 1.2 bearing sands at depths between 3000 and 4600 m. The main gas reservoirs are sandwiched within tight hydrocarbon sands. The target 1.4 r e s e r v o i r s e x h i b i t s m a l l p o r o s i t y b u t a r e fractured. The gas-sand thickness varies from 5 to 40 m. Figure 2 shows the well log data analysis 1.6 r e s u l t s f o r t h e s t u d y a r e a . F i g u r e 2 a i s a comparison of the P-wave (black line) and S-wave (grey line) velocities. Figures 2b and Traveltime (s) 2c show the enlargments corresponding to 1.8 the boxes near T1 and T2 in Figure 2a. The heterogeneous and thin tight-sands constitute the reservoir. Figure 2d shows the P- to S-wave 2.0 velocity ratio with ratio values between 1.5 and 2.0 highlighted by the box. Figure 2e is the P- and S-impedance crossplot for the data in Figure 2b. We fit the crossplot data using linear 2.2 regression to get Is = 0.43977 Ip + 1345.5, The P- and S-impedances are consistent over the reservoir depth range in the study area so it is Fig.4 Well to seismic tie result. difficult to predict the reservoir using only P- The ,rst curve is impedance (left) and the second is computed re,ectivity series. The and S-wave information. third curve is the extracted seismic wavelet from seismic and well data with the wavelet zero-time set to 1.2 s. The fourth curves are from the seismic section near the well location, and the ,fth curves are the synthetic seismic traces. Well-logging data We set up a pre-stack seismic m a t c h p r o c e s s i n g i n c l u d i n g Lithology interpretation inversion workflow for detecting well curve modification and the gas reservoirs (Figure 3). s e i s m i c w a v e l e t e x t r a c t i o n . Four classes gas sand reservoirs It includes the main steps for The correlation is high in the inversion, conversion of pre- o b j e c t i v e r e s e r v o i r i n t e r v a l Reservoir type s t a c k s e i s m i c g a t h e r s , a n d denoted by the box in Figure seismic wavelet estimation. 4 . T h i s r e l a t i o n s h i p c a n b e Selective preference W e a p p l i e d t h e p r e - s t a c k established using the velocity elastic parameters i n v e r s i o n m e t h o d t o t h i s model if there is well log data in the study area. In this case, study area. First, we get the Interpretation Inversion initial models c o m m o n - r e f l e c t i o n - p o i n t w e c o m p a r e d t h e g e o l o g i c seismic horisons g a t h e r s f r o m p r e - s t a c k t i m e and seismic characteristics of or depth migration. The time- our objective reservoir to an Pre-stack elastic Pre-stack seismic depth relationship between the adjacent area. We could also get parameters inversion data common-reflection-point gathers the time-depth relationship by and common-angle gathers can simply modifying the time-depth Reservoir prediction be determined from well log r e l a t i o n s h i p o f t h e a d j a c e n t Hydrocarbon detecting data analysis. Figure 4 shows the area. This step is very important well to seismic match after the for the subsequent inversion Fig.3 The pre-stack elastic parameter inversion detailed time-depth relationship process. Because only by using work,ow. Chen et al. an accurate time-depth relationship can the common- figure. From this figure, we have not only accomplished reflection-point gathers be converted to common-angle the conversion but also checked the correctness of gathers and calibrate the horizons. the velocity model computed from the well log data Figure 5 shows a pre-stack common-reflection- based on the gather characteristics before and after the point gather and the corresponding common-angles conversion. In the common-angle gathers, we can see gather using the velocity model from the log curves of the consistency of the horizon imaging with common- a gas well. The target reservoir formations are located r e f l e c t i o n p o i n t g a t h e r s d a t a a n d s o m e m o d i f i e d between 2.2 and 2.6 s as indicated by the rectangle in the amplitudes on the gathers. Offset (m) Angle (degree) 0 6 12 18 24 30 36 42 75 325 575 825 1075 1325 1575 1825 1.0 1.0 1.2 1.4 1.5 1.6 1.8 2.0 2.0 Traveltime (s) 2.2 2.4 2.5 2.6 2.8 3.0 3.0 Fig. 5 A common image point gather in the offset domain (left) and computed gather in the angle domain (right). Figure 6 shows the stacked section and three inversion density results for the four types of gas sand reservoirs. results: P-wave impedance, S-wave impedance, and density. Based on the rock physics parameter relationships, they From the sections we can see that the inverted density can be used as hydrocarbon indicators. In these sections, changes are much smaller than impedance especially in the we denoted the gas well location by red circles, the target Traveltime (s) vicinity of the gas-bearing sandstone reservoir, but in the formation by red short curves, and the two black lines are inverted P- and S-wave impedance sections we see that the the top and bottom of the gas reservoir from the geologic change is very obvious. So, we can use the inverted results interpretation. We see that the obvious lateral lithology to effectively identify hydrocarbons contained in the tight changes occur at the reservoir location. Comparing these sand reservoir formation. sections to Figure 6, we see that the resolution is much Figure 7 shows elastic property parameter sections, higher than in the impedance inversion sections (Figures which are calculated using the inverted impedance and 4b and 4c). Distance (km) Distance (km) (m/s)*(g/cc) 12 0 2 4 6 8 10 12 0 2 4 6 8 10 1.4 1.4 1.60 1.50 1.40 1.8 1.8 1.30 2.2 2.2 1.20 1.10 Traveltime (s) Traveltime (s) Traveltime (s) Traveltime (s) 1.00 2.6 2.6 4×10 (a) (b) Distance (km) Distance (km) (m/s)*(g/cc) 12 0 2 4 6 8 10 12 g/cc 0 2 4 6 8 10 1.4 1.4 10.5 3.0 0.95 2.8 1.8 1.8 0.85 2.6 2.2 2.2 0.75 2.4 0.65 2.2 2.6 2.6 0.55 2.0 4×10 (c) (d) Fig. 6 (a) Stack section; (b) P-impedance inversion section; (c) S-impedance inversion section; and (d) Density inversion section. (b) - (d) are the inversion results from prestack common angle gathers. Distance (km) Distance (km) Traveltime (s) Traveltime (s) Traveltime (s) Traveltime (s) 12 Gpa 0 2 6 8 10 12 0 2 6 8 10 4 4 Gpa*(g/cc) W-1 W-1 1.4 1.4 20 50 16 40 1.8 1.8 12 30 2.2 2.2 8 20 4 10 2.6 2.6 0 0 (a) (b) Distance (km) Distance (km) (Nondimensional) 12 0 2 6 8 10 12 (Nondimensional) 0 2 6 8 10 4 4 W-1 W-1 1.4 1.4 1.9 1.2 1.7 1.0 1.8 1.8 1.5 0.8 0.3 0.6 2.2 2.2 0.1 0.4 0.9 0.2 2.6 2.6 0.7 0 (c) (d) Fig. 7 Elastic coef,cients calculated from the inversion results in Figures 4b - 4d. (a) Lame’s coef,cient λ. (b) λρ section. (c) λ/µ. (d) Κ/µ. Figure 8 shows low and high frequency sections from prediction, the time-frequency analysis results show that a time-frequency analysis for the reservoir, the left is a there is the phenomenon of “shadow in low frequency, 15 Hz frequency component section and the right is a absorbing in high frequency”, which is caused by the 35 Hz frequency component section. The red line in the gas bearing reservoir that we predicted. Under the two panels indicates the gas well location. For reservoir reservoir the area with the same characteristics also will Chen et al. be verified by drilling. Comparing the time-frequency data, we find that it can effectively predict the gas results with the inverted elastic property parameter reservoir using the λ, λρ, λ/µ, and K/µ properties and sections as hydrocarbon indicators and with the well their combinations in this study area. Distance (km) Distance (km) 4 4 0 2 6 8 10 12 0 2 6 8 10 12 W-1 W-1 2.2 2.2 2.3 2.3 2.4 2.4 Traveltime (s) 2.5 2.5 2.6 2.6 (a) (b) Fig. 8 Time-frequency sections for the gas-sand reservoir formation (Top and bottom horizons of the reservoir are drawn in the panels). Left is a 15 Hz frequency section across a gas well (indicated by the red line) and right is a 35 Hz frequency section. suggestions allowed us to improve the quality of the Conclusions final paper. We have built an elastic property parameter inversion References processing workflow based on rock physics parameters that improves the ability to predict gas sand reservoirs and detect hydrocarbons. We performed pre-stack Traveltime (s) Aki, K., and Richards, P. G., 1980, Quantitative seismology: seismic inversion for gas sand reservoirs using the Theory and methods: W. H. Freeman and Co. largest reflectivity parameters which, not only improve Castagna, J. P., Batzle, M. R., and Eastwood, R. L., 1985, the stability of the pre-stack seismic inversion, but also Relationship between compressional-wave and shear- enhance the ability of gas detection using the inversion wave velocities in clastic silicate rocks: Geophysics, 50, results. Application to real data shows the effectiveness 571 – 581. of this method. Castagna, J. P., Swanz, H. W., and Foster, D. 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