AVO Carbonate

AVO for Fluid & Fracture Analysis In Carbonate Reservoirs

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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1. AVO for Fluid Analysis in Carbonate Reservoirs Until recently, seismic analysis of data from carbonate reservoirs relied mainly on interpreting zero-offset (stacked) volumes. Common knowledge within the world of AVO suggests that zerooffset information is often insufficient to differentiate shale from carbonate porosity, or to discriminate gas-saturated from brinesaturated reservoirs. However, in the last a few years, great efforts have been made to apply AVO analysis to carbonate reservoir characterization but several issues must be addressed in investigating the feasibility, potential, and sensitivity of the response of carbonate rock properties to porosity and fluid. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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First, a lack of carbonate rock property information is considered an obstacle in applying AVO to carbonate reservoir characterization. Second, the differences between clastic AVO and carbonate AVO need to be clarified. Third, procedures and calibration in seismic data processing and interpretation need to be developed. The situation has been greatly improved due to recent significant acquisition of dipole sonic logs. Below is the illustration given by Li et al (2003) on the application of AVO for carbonate reservoir in the Western Canadian Sedimentary Basin (WCSB). Some issues such as physical relationships between rock properties, fluid sensitivity of the carbonate rock property, calibration and interpretation are reviewed and discussed. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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1.1. Carbonate rock properties. Figure 1 shows a set of dipole well logs from the Foothills of the WCSB in which the mudrock line for clastics is Vs = 0.862 Vp 1172.4. A line with the relationship of Vs = 0.4878 Vp + 230.0 is fitted to the carbonate lithology cluster. Similar to Castagna's definition, we call this linear relationship a carbonate line. It can be seen that the carbonate line deviates from the clastic mudrock line with a slope significantly less than that of clastic rocks. In Figure 1, as is always observed, the data points of the gas sand in these two wells shift away from the clastic rock cluster and have a low Vp and a low Vp/Vs ratio in comparison with watersaturated sandstone. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Fluid effects in carbonates, especially gas effects, are contentious but of great interest. The common wisdom is that fluids have little or no effect on carbonate rock properties because carbonate rocks have very high moduli. In other words, the high velocity of the carbonate rock matrix causes seismic waves to travel primarily trough the matrix where they are little influenced by pore fluids. However, an analysis of the dolomite data from the Williston Basin by Rafavich (1984) indicates that gas does influence carbonate rock properties and its effect is significant (Figure 2).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 1. Velocities and Vp/Vs ratio of dipole well logs from Foothills, the WCSB (Li et al, 2003).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 2. Gas effect of dolomite rock properties for the data set from Williston Basin (Li et al, 2003).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Further evidence of this can be seen in an analysis of a large data set of lab measurements on carbonate rocks from the WCSB. This data set includes lime-stones and dolomites. It represents a wide range of carbonate reservoirs and nonreservoirs. An analysis of this data set indicates that the result is consistent with the data set of the Williston Basin (Figure 3). Notice that the behavior of dolomite rocks due to gas saturation is similar to that of sandstones. Namely, P-wave velocity and Vp /Vs ratio decrease, and S-wave velocity increases slightly due to decreasing density. In addition, the rocks are more sensitive to fluid with increasing porosity. The results of limestone are not shown. In general they are similar to dolomites except less sensitive to fluid. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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The influence of fluid on carbonate rock properties described above implies that AVO response to gas and brine saturated rocks should be different. Figure 4 shows theoretical calculations to examine these for the most often encountered reservoir types (porous limestone and porous dolomite encased by tight limestone). First, for limestone reservoirs encased by tight limestone, AVO gradient responses are similar for both gas and brine cases. Consequently, zero-offset amplitude becomes the attribute differentiating gas from brine. But, as porosity itself could produce the same response as fluid, zero-offset amplitude is ambiguous in determining fluid effect in a reservoir

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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In contrast, the fluid effect on offset amplitude or "gradient“ in a dolomite reservoir encased by tight lime-stone is significant. With increasing porosity from 0 to 20%, all AVO classes (I-IV) are present. More specifically, a class III AVO mainly corresponds to porosity of 6-14%, and class III-IV to 14-20% (Figure 4d). In the WCSB, most carbonate reservoirs are in these porosity ranges. In addition, these AVO responses are accompanied by weak-to-strong zero-offset reflectivity. As a shale/limestone interface could produce a class II or III AVO response, care must be taken in standard AVO analysis. In Figure 4, Shuey's two-term AVO and three-term AVO calculations are shown as blue and red lines, respectively. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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1.2. Analysis. Figure 5 shows selected dipole well logs that represent gassaturated dolomite reservoir at about 3700 m and a brinesaturated dolomite reservoir at 3000 m. The gas saturated reservoir has a thickness of 30 m, an average P-wave velocity of 5400 m/s, density of 2.5-2.6 g/ cc, and porosity of 8-16%. In Figure 5, the gas and wet dolomites are red and green squares, respectively tight limestone data points are black squares and small blue dots represent entire well logs. Empirical relationships for sand, shale, and carbonates are overlain to establish a background where major lithologies are located. Such plots facilitate understanding relationships among different lithologies and fluid effect. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 3. Gas effect of dolomite rock properties for a data set from the WCSB (Li et al, 2003).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 4. Theoretically calculated AVO responses for carbonate reservoirs (Li et al, 2003)..

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 5. Gas-and brine-saturated dolomite reservoirs in velocity and modulus domains (Li et al, 2003).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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The empirical relationships of carbonates were developed from lab measurements and it can be seen that the log data agree with them. There is no gas sand in these wells (refer to Figure 1). The observations that can be made from Figure 5 are: (a) the gas effect is apparent in the Vp/Vs ratio,λ /µ ratio, and λρ domain (λ is Lame's constant, µ is shear modulus, and ρ is,density); (b) wet dolomite or wet limestone can be used as the background reference in order to quantitatively determine the degree of the gas effect; (c) the shear modulus of carbonates is higher than that of shale; and (d) shale and porous carbonate can be distinguished as they occupy different spaces in cross-plotting domains. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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To determine a quantitative assessment of the influence of fluid on carbonate reservoir rocks, brine substitution (using the BiotGassmann equation and calibrated by the empirical relationship in Figure 2) was performed for the gas-charged dolomite in Figure 5. Figure 6 illustrates the sensitivity of rock properties in various domains. Figure 6 shows, in moving from the gas case to the brine-substituted case, that the density, velocities, Vp/Vs ratio and impedances change less than 10% in magnitude. The change in λρ, however, can be as great as 66%. The λρ contrast (relative variation) between encasing limestone and the gas-saturated dolomite is also significantly enhanced. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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The high sensitivity of the reservoir rock and the enhancement of contrast between the encasing limestone and the gas-saturated reservoir in λρ domain are mainly contributed from the decreasing of Vp/Vs ratio (γ) of the reservoir rock. They are governed by the relation ∆λρ / λρ = (( 2γ 2 - 4γ ) / ( B (γ 2 - 2)) R p

where Rp is P-wave reflectivity and B is the slope of the carbonate line, Based on this relation, a small decrease in Vp/Vs ratio will result in a large increase in ∆λρ /λρ The above observations are consistent with observations in clastic reservoirs. Consequently, for carbonate reservoir characterization, λρ and λµ ratio may be used as fluid indicators. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Synthetic gathers for the gas-charged, reservoir and the brinesubstituted case were then generated ( Figure 7). A class III AVO at the base of the gas-charged reservoir changes to weak class II AVO after brine substitution. This is consistent with the theoretically calculated AVO response in Figure 4.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 6. Sensitivity of rock properties in responding to fluid (Li et al, 2003)..

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Figure 7. AVO responses of gas-charged and brine-saturated dolomite reservoir (Li et al, 2003). AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 7 shows a real data example of a CDP gather at a dolomite gas well. The reservoir, at about 3000 m, has a thickness of 20 m and porosity of 12-14%. Figure 8a is the Ostrander gather and Figure 8b is the constructed gather using P-and S reflectivities (Rp and Rs) extracted using Fatti’s AVO equation: R pp (θ ) = R p (1 + tan 2 θ ) − 8 (V s / V p ) 2 R s (sin 2 θ ). As with the synthetic gather for the gas case in Figure 7, a class III AVO is at the base of this reservoir. This again confirms that a gas-charged dolomite reservoir does produce an AVO anomaly: the amplitude brightens at far offsets. At this specific well location, another class III AVO appears underneath the reservoir and suggests a new potential reservoir. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 9 shows a 2D stack section with three CDP gathers from two tight wells and one gas well. The gas dolomite discovery well produces 13 million cubic feet per day. The reservoir manifests as the highlighted bright spots on the stack. Without examining prestack gathers, the bright spots may be interpreted either as gas porosity, shale-filled channel, or gas charge reservoir. The CDP gather at the gas well shows class III AVO anomaly. In contrast, the seismic responses on the CDP gathers at the two tight wells are quiet. This example demonstrates that far offsets can contribute significantly to the amplitude anomaly of bright spots on a stacked section. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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In an attempt to better define a carbonate reservoir, we analyzed the elastic property inversion method developed by Goodway et al (1994).The procedure is to first extract P- and S-reflectivities (Rp and Rs) from CDP gathers by using Fatti' AVO equation, invert these reflectivities into P- and S-impedances by introducing low-frequency background of P- and S-impedance, and finally calculate the modulus attributes, λρ,µρ, and λµ ratio using λρ= Ip2-21s2 and µρ = Is2. This technique has been widely used in the WCSB clastic reservoirs. Its effectiveness is based on the fact that λρ and λ/µ ratio are sensitive to fluid as shown in Figure 5.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 10 shows λρ and µρ sections for a carbonate gas play. There are three wells: tight, good gas, and marginal gas. Direct observation indicate that the good gas well corresponds to the low λρ anomaly, and the tight and the marginal gas wells correspond with higher λρ values. However, µρ varies little within the reservoir zone. Since the shale formation may manifest itself as an amplitude anomaly on a stack section and low impedance in P-impedance section, the introduction of shear-wave information via AVO would help differentiate shale from carbonate. In Figure 10, there is low λρ and µρ shale zone under the reservoir zone. It can be seen that ambiguity between shale and reservoir prevents defining such a zone as a reservoir. However, crossplotting can solve this problem because the shear modulus of reservoir carbonate is higher than that of shale. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 8. CDP gathers: (a) Ostrander gather and (b) the reconstructed gather using P and S reflectivities (Li et al, 2003).

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 9. Stacked section and CDP gathers for a dolomite reservoir (Li et al, 2003).

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Figure 10. (a) λρ section and (b) µρ section with tight, good gas and marginal gas wells. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 11 shows the crossplots of The λρ and µρ sections in Figure 10. There is good separation between shale and carbonates. For reservoir and nonreservoir carbonate rocks, it can be seen that a space filled with data points from the gas well (lower half of Figure 11) has almost no points from tight and marginal gas wells (upper half of Figure 11). Furthermore, Figure 11 demonstrates that, in crossplot space, data points from the gas-charged dolomite reservoir are distinct from data from the tight limestone and marginal gas well. One can thus isolate the reservoir from non- reservoir rocks by projecting a polygon in the crossplot domain back into the 2D section or 3D volume. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 12a has a polygon for the reservoir rocks indicated in Figure 11 and Figure 12b shows the projected results in a λρ section. Up to this stage the reservoir has been successfully isolated. It can be seen that a good gas well is located at the center of the most continuous low λρ zone; the marginal well is near a small gas zone but misses the target; and the location corresponding to the tight well has no λρ anomaly. Figure 12b further suggests that potential drilling locations may exist at CDPs 500 and 810.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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1.3. Calibrations. Calibration is a cross-examination between petrophysical rock properties, seismic rock properties, seismic, and inverted seismic rock properties. Figure 13, a flow chart for the calibration and interpretation in carbonate reservoirs using AVO, has two main branches: one for rock physics analysis and AVO modeling and one for seismic processing. Seismic interpretation should start from a stacked seismic in which a seismic amplitude anomaly and/or phase anomaly may already be seen. An AVO anomaly often can be determined through analyzing Ostrander or super gathers.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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AVO modeling can be conducted at this stage to assist determining whether an AVO anomaly corresponds to a reservoir, The elastic rock property inversion provides P-and Simpedance, λρ, µρ and λ/ µ ratio. As P-impedance cannot solve the ambiguity between shale and carbonate porosity, shear-wave information becomes crucial in discriminating reservoir from nonreservoir. During the elastic rock property inversion, the relationship of P - and S-reflectivity trend may be used to check if offsetdependent amplitudes have been processed properly. The relationship between P-and S-impedance may be used to check if the inversion was performed with the correct low-frequency background. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 11. λρ and µρ crossplots at tight, marginal gas and good gas well locations (Li et al, 2003).

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Figure 12. Projection of gas zone on λρ section

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Figure 13. Flowchart of AVO processing and interpretation for carbonate reservoirs (Li et al, 2003). AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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2. AVO for Fracture Characterization 2.1. Introduction Fracture characterization is an important part of reservoir development, in particular for carbonate reservoirs. Different techniques have been used to estimate fracture orientation and density. Traditionally, S-waves generated at the surface and recorded by three component geophones (either with vertical seismic profiling or surface geometries) have been used for this purpose (Alford, 1986). However, since acquisition and processing of S-waves is costly and the availability of shear waves sources is limited, different alternatives have been considered. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Recently, P-S converted waves have become more popular because they are expected to contain the same information as S-S waves but can be generated with compressional sources, which makes the acquisition not only inexpensive but also less labor intensive than S-S waves recording. Garotta and Granger (1988) and Ata and Michelena (1995) showed examples of the use of PS converted waves to estimate fracture orientation. P-S waves, however, are more cumbersome to use than nonconverted waves because of the asymmetry of the ray path, and they are more expensive to record and process than conventional P-P waves.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Since many areas in the world are already covered by 3-D P-wave data, various authors have focused their attention on the use of this existing information to estimate fracture properties. A modeling exercise presented by Mallick and Frazer (1991) shows that the amplitude-variation-with-offset (AVO) response of Pwaves can be affected by the presence of fractures depending on the relative orientation between fractures and the recording line. Lefeuvre (1994), Lynn et al. (1995), perez and Gibson (1996), and perez et al. (1999) presented examples that confirmed Mallick and Frazer's (1991) predictions. Rüger (1996) developed the theory behind these observations and showed how to estimate other fracture properties besides orientation.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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A more recent line of research initiated by Grechka and Tsvankin (1996) is devoted to the estimation of fracture properties from the normal-moveout (NMO) analysis of multiazimuth P-wave data. Corrigan et al. (1996) successfully applied these ideas to field data. All the previously mentioned methods have their own limitations and do not necessarily yield the same results when applied in the same area. They are influenced by different sources of noise that need to be properly considered and reduced to obtain accurate results.

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In the following sections, the results of Perez et al (1999) study are discussed. The study presents different seismic methods used to estimate fracture orientation when applied to different data sets recorded in the same field. The methods used are the rotational analysis of converted waves, azimuthal AVO analysis, and NMO ellipticity. The results obtained from different methods generally agree (except that for 3-D NMO ellipticity), follow one of the fracture sets detected with Formation microScanners (FMS) logs, and coincide with the trend of the maximum horizontal stress in the area.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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2.2. The Study Area Maporal field is located in the north-central part of the BarinasApure Basin, Venezuela. Structurally, Maporal field is a dome slightly extended toward the northeast. Geologically, the sediments are nearly flat -lying, dipping toward the northeast at approximately 4o. The target zone is the “O” member of the Escandalosa Formation. This member is a 25-m-thick fractured limestone located at a depth of approximately 3000 m (2.32 s). Since fractures seem to control production from the Escandalosa Formation, reservoir engineers decided to continue the exploitation of the field using horizontal wells oriented perpendicularly to the densest fracture systems. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Existing well log information in the field provides good background information about the reservoir and its fracture properties, and can be used to calibrate the results obtained from the seismic data. FMS logs were used to estimate fracture orientation, fracture density, and orientation of maximum horizontal stress at four different wells. The rose diagrams in Figure 14 shows the presence of different fracture systems in the area. The orientation of the maximum horizontal stress measured is constant across the field. The open fracture system tends to be parallel or quasi parallel to the orientation of maximum horizontal stress.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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2.3. Available Data The data consisted of a 3-D P-wave survey recorded over an area (640 km2) much larger than the area of interest to help in the characterization of other adjacent reservoirs, and three l0-km 2-D 3-C lines centered in the area of interest (Figure 15). The multicomponent acquisition was performed right after the 3D acquisition finished and, therefore, the results of the analysis of one data set were not used to help the design of the other. The three 2-D 3-C multicomponent lines were centered over the area of interest with an intersection point coinciding with a well location. For calibration purposes, each line intersected, or was close to, at least one additional well with log information. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Previous P-wave 2-D seismic data were used to identify two nearly orthogonal faults systems that cross the field (Figure 14). The azimuths of two the multicomponent lines were almost parallel to these systems, whereas the other line bisected them, forming an angle of approximately 45° with each. The 3-D seismic data were collected using a swath geometry with a shot line perpendicular to eight receivers lines. A bin spacing of 80 m, a fold of approximately 40 traces, and a maximum offset of 3626 m were acquired. A subset of 25 km2 from the original 3-D data set centered at the intersection point of the 2-D multicomponent lines was used for this study.

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Superbins of 240 x 240 m2 were formed to insure adequate coverage in offset and azimuth for AVO and NMO analysis. Figure 16 shows atypical CDP supergather from the 3-D data. Only f-k filtering in the shot domain has been applied to these data to eliminate surface waves. Notice how the presence of static noise, or possible azimuthal anisotropy. Reflections from the top of the target (Escandalosa Formation) are located at 2.32 s. Figure 17 shows a 2-D 3C, raw commonshot gather recorded over Line 2. Notice the presence of energy in both horizontal components, which is an indication of azimuthal anisotropy. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Michelena et al. (1994) performed modeling to demonstrate that azimuthal anisotropy, not heterogeneity, is responsible for the energy observed in the transverse component in this area. As shown by Ata and Michelena (1995), fracture orientation changes across the field, which explains why not all 3-C records show energy in the transverse component. Converted waves from the top of Escandalosa Formation are indicated by the arrow at 3.8 s. Figure 18 shows the structural map of the top of the Escandalosa Formation interpreted from the 3-D P-wave seismic data. This map confirms the gentle nature of the structural variations in the field already known from previous 2-D P-wave data. As we mentioned before, this structure dips 4° toward the northeast. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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2.4. Data Analysis Fracture orientation was estimated by applying different methods to the different data sets available, started by analyzing the P-S converted waves in the multicomponent data. Then, we analyze the AVO and NMO responses of Pwaves recorded in the vertical component of the 2-D 3-C data around the intersection point of the three lines. Finally, the azimuthal variations of AVO and NMO responses of the P-waves for each bin in the 3-D data was investigated. The following sections describes the obtained results.

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2.4.1. 2-D data Rotation analysis of P-S converted waves.—Figure 19 shows portions of migrated horizontal components around three different points in the field. Notice that points located on lines 1 and 3 clearly show that the horizontal component parallel to line 3 arrives earlier than the other component, which is what we expected from the direction of the maximum horizontal stress in the field. Remember that the orientation of the faster shear arrival generally coincides with the direction of maximum horizontal stress.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 14. Maximum horizontal stress (inward facing arrows) from break-out orientation logs at wells, 16, 17, 20, and 23. The rose diagrams indicate fracture orientation and density from FMS logs in the same wells (Perez et al, 1999) .

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Figure 15. Study area over 2-D and 3-D surveys (Perez et al, 1999)

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Figure 16. Typical supergather from 3-D data. The bin size is 240 x 240 m2 (Perez et al, 1999)

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Figure 17. 3-C raw common-shot gather recorded over line 2 of the 2-D multicomponent lines. Converted waves from the top of Escandalosa Formation are indicated by the arrow at 3.8 s in the horizontal components (Perez et al, 1999).

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Figure 18. Structural map of the top of Escandalosa Formation interpreted from the 3-D seismic data. Colors indicate two-way traveltime in second (Perez et al, 1999)

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Figure 19. Horizontal components from three different locations in the field (Perez et al, 1999)

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Figure 20. Fracture orientation from rotational analysis of converted waves (Perez et al, 1999).

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After interpreting the migrated sections of radial and transverse components, rms amplitudes in a window around the target was compute. Then, rotational analysis based on the amplitude ratio between the two horizontal components (Ata and Michelena, 1995) to estimate fracture orientation for each common conversion point (CCP) of the three lines was performed. From the angles estimated at each CCP, we obtain new angles for points outside the multicomponent lines using 2-D spline interpolation. The orientation of the fastest shear arrival happens to be approximately constant for all depths across the field. Figure 20 shows a smoothed map with the results of the rotational analysis plus interpolation.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Each arrow indicates the local fracture orientation (fracture strike) estimated from converted waves. The colors show the same structural map presented in Figure 18. Since the azimuths between the lines have been interpolated, orientations presented in this map are more reliable along and in the neighborhood of the three lines. As we can see, fracture orientation follows the trend of the maximum horizontal stress in the area. Notice that the estimated fracture orientation follows the direction of one of the fracture systems present in the area. Azimuthal AVO from 2-D data.—For the 2-D P-wave data, we perform conventional AVO analysis over CDP gathers located along each line close to the intersection point. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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We obtain the sections of AVO gradient and AVO intercept for each line. Figure 21 shows the AVO gradient sections around the intersection point along almost perpendicular lines 1 and 3. Notice the different AVO gradients for the reflection from the bottom of the reservoir along these two lines. No significant differences are observed in AVO response from the top of the reservoir. Figure 22 is a graph of AVO gradient versus AVO intercept. Gradients for lines 1 and 2 (nearly perpendicular to fracture orientation) are positive and higher than gradients along line 3 (which is nearly parallel to the fracture orientation estimated from converted waves). As expected, the AVO intercept is almost the same for all lines.

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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The exact direction of the maximum AVO gradient was estimated from the three CDP gathers located at the intersection point of the multicomponent lines. We use a formula derived by Rüger (1996) for the reflection coefficients in transversely isotropic media with a horizontal symmetry axis. The estimated azimuth of the maximum AVO gradient is 56°. Since it is expected to be perpendicular to fracture orientation, we obtain the fracture azimuth is 146° (Figure 23) .

AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Fracture orientation estimated using this technique also follows the regional maximum horizontal stress and is close to the orientation obtained from the analysis of the converted waves (Figure 20). NMO ellipticity from 2-D data.—From the same three gathers used to obtain the orientation of the maximum AVO gradient, we estimate the parameters that describe the best fitting horizontal ellipse of the NMO velocities for all azimuths (Grechka and Tsvankin, 1996). The NMO ellipse obtained from this analysis is shown in Figure 21. The azimuth of the major axis is 125°. The major axis corresponds to the maximum NMO velocity that is expected to coincide with fracture orientation. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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The differences between maximum and minimum velocities are around 5%, but the axes of the ellipse have been exaggerated to give a better idea of its orientation. In this case, the orientation of the major axis of the NMO ellipse also follows the regional trend of the maximum horizontal stress in the area. 2.4.2. 3-D Seismic Data Azimuthal AVO from 3-D data.—3-D P-wave data was gathered using a bin size of 240 x 240 m2 to achieve the coverage needed in both offset and azimuth to perform azimuthal AVO analysis. The orientation of the maximum AVO gradient was estimated for each superbin based on the amplitudes located within a time window that follows the top of Escandalosa. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 25 shows smoothed results of the azimuthal AVO analysis for each superbin. The arrows in Figure 25 are oriented according to these new, smoothed angles. As we can see, the estimated orientations follow closely the local structural changes, but the general trend is still close to the regional maximum horizontal stress. Areas with abrupt changes in structure seem to have a more erratic AVO response when compared to other areas in the field. Notice the similarity between the results obtained with converted waves (Figure 20) and 3-D azimuthal AVO analysis (Figure 23). There are differences, however, between the two results, especially in the northwest part of the area. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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No interpolation was used to generate Figure 12 because AVO analysis was performed at equally spaced grid points. On the contrary, to generate Figure 7, we interpolated the azimuths measured along the lines for all grid points where no information was available. In principle, this can create unrealistic orientations in areas surrounded by angles that represent the same direction but different orientations (0º and 180º, for instance ), which is the case in the northwest part of the area. No attempt was made to change these angles in the data before interpolation.

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Figure 21. AVO gradient sections around the interception point for lines 1 and 3. Notice how the AVO responses changes around the bottom of the reservoir located at 2.370 s (Perez et al, 1999). AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Figure 22. AVO intercept versus AVO gradient for lines 1,2 and 3 at the intersection point of the 2-D multicomponent lines (Perez et al, 1999)

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Figure 23. Orientation perpendicular to the maximum AVO gradient at the intersection point of the 2-D multicomponent lines. This result is compared to the orientation of line 3, which is nearly parallel to the orientation of maximum horizontal stress in the area (Perez et al, 1999)

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Figure 24. Orientation of the NMO ellipse from 2-D P-wave data at the intersection point of the 2D multicomponent lines (Perez et al, 1999).

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Figure 25. Fracture orientation from 3-D azimuthal AVO analysis. The arrows indicate the local orientation of the perpendicular to the maximum AVO gradient (Perez et al, 1999).

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Figure 26. Fracture orientation from 3-D NMO ellipticity. The arrows indicate the local orientation of the maximum axis for the NMO ellipse (Perez et al, 1999).

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Figure 27. Lateral velocity variations of the isotropic NMO velocity at the bottom of the reservoir (Perez et al, 1999)

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NMO ellipticity from 3-D data.—3-D NMO analysis was performed for the same superbins used in the 3-D azimuthal AVO analysis. Figure 26 shows the result of this process: the orientation of the semimajor axes of NMO ellipses at the target for each CDP. The general trend in the orientation of these axes is not as expected from all our previous results, which followed the orientation of the maximum horizontal stress more closely. In Figure 26, the differences between the semimajor and semiminor axes of the NMO ellipse are of the order of 3%. We can speculate about various reasons to explain the orientation of NMO ellipses for each CDP. First, the NMO ellipses include a cumulative influence if the overburden from the surface to the target. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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We did not do layer stripping to obtain interval velocities because the target is too thin (25 m) compared to its depth and, therefore, the error amplification due to stripping is expected to be severe. Second, since the ellipticities (i.e., the elongations of NMO ellipses) are small (about 3%). The ellipse orientation becomes a poorly determined quantity, so that the estimated ellipse azimuths may be inaccurate. The third issue is that we believe that the proper way to remove the effects of near-surface azimuthal anisotrophy is by doing independent static corrections for each azimuth, which we did only for the 2-D data. The effect of static variations, nearsurface anisotropy, AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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and azimuthal anisotropy in the subsurface that affect the NMO velocities cannot be separated whit static correction methods that analyze simultaneously all azimuths in a superbin. We found that after applying such corrections for any depth, lateral coherency of events was improved considerably, but differences between major and minor axes of NMO ellipses were reduced to less than 0.01 %. For this reason, we did not remove static corrections in the data used to generate Figure 27. Finally, the influence of lateral heterogeneity may make azimuthal variation of the NMO velocity elliptical even in the absence of anisotropy.

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Figure 27 shows lateral variations of the isotropic NMO velocity ( e.g., a circular approximation of NMO ellipse) estimated from conventional velocity analysis around the target. Even though the changes in these velocities are not that large, they may distort our inferences about P-wave azimuthal anisotropy which are made under the assumption that the medium is laterally homogeneous.

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BIBLIOGRAPHY Alford, R. M., 1986, Shear data in the presence of azimuthal anisotropy: 56th Ann. Internat. Mtg., Sac. Expl. Geophys., Expanded Abstract, 476-479. Ata, E., and Michelena, R. J., 1995, Mapping distribution of fractures in a reservoir with P-S converted waves: The Leading Edge, 12,664-676. Corrigan, D., Withers, R., Damall, J., and Skopinski, T., 1996, Analysis of amplitude versus offset to detect gas-oil contacts in the Arabian Gulf: 66th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstract, 1834-1837. Garotta, R, and Granger, P. Y., 1988, Acquisition and processing of 3C x 3-D data using converted waves: 58th Ann. internat. Mtg., Soc. Expi. Geophys., Expanded Abstract, 657-658. Grechka, V., and Tsvankin, J., 1996, 3D description of normal moveout in anisotropic media: 66th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1487-1490. Johnson, W. E., 1995, Direct detection of gas in pre-Tertiary sediments?: The Leading Edge, 14, 119-122. Lefeuvre, F., 1994, Fracture related anisotropy detection and analysis: and if the P-waves were enough?": 64th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 942-944. Li, Y. et al, 2003, Recent application of AVO to carbonate reservoirs in the Western Canadian Sedimentary Basin, The Leading Edge, July 2003, vol.22, no.7, SEG. AVO for Fluid & Fracture Analysis in Carbonate Reservoirs By : Sigit Sukmono

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Lynn, H., Simon, K. M., Layman, M., Schneider, R, Bates, C. R., and Jones, M., 1995, Use of anisotropy in Pwave and S-wave data for fracture characterization in a naturally fractured gas reservoir: The wading Edge, 14, 887-893. Mallick, S., and Frazer, L N., 1991, Reflection/transmission coefficients and azimuthal anisotropy in marine seismic studies: Geophys. J. Internat., 105,241-252. Maria A. Pérez*, Vladmir Grechka‡, and Reinaldo J. Michelena*, 1999, Fracture detection in a carbonate reservoir using a variety of seismic methods, Geophysics, v.64, no.4, 1266-1276 Michelena, R. J., Ata, E., and Sierra, J., 1994, Exploiting P-S converted waves: Part I, Modeling the effects of anisotropy and heterogeneities: 64 Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstract, 236--239. Perez, M., and Gibson, R., 1996, Detection of fracture orientationing azimuthal variation of P-wave AVO responses: Barinas Field (Venezuela): 66th Ann. Internat Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1353-1356. Perez, M., Gibson, R., and Toksoz, N., 1999, Detection of Fracture orientation from azimuthal variation of Pwave AVO responses: Geophysics, 64, 1253-1267, this issue. Ruger, A., 1996, Reflection coefficients and azimuthal AVO analysis in anisotropic media: Ph.D. thesis, Colorado School of Mines.

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