SPECIAL Asia SECTION: Pacific A s i a P acific Distinguishing gas sand from shale/brine sand using elastic impedance data and the determination of the lateral extent of channel reservoirs using amplitude data for a channelized deepwater gas field in Indonesia PAUL THOMPSON, JIM JASON HARTMAN, and MUHAMMAD ANUN ANUNG ANANDITO, Chevron Indonesia DHANANJAY KUMAR and JIM MAGILL, Chevron Energy Technology Company KIYOSHI NOGUCHI, INPEX Corporation BRAHMANTYO KRINAHADI GUNAWAN, BPMIGAS Sadewa Field is in Kutei Basin in the Makassar Strait between the Indonesian islands of Kalimantan and Sulawesi about 5 km from the shelf edge in water depths of 1500 2500 ft (Figure 1). The discovery well was drilled in 2002, and a total of nine wellbores have been drilled. Two basic development scenarios have been assessed for the field: (1) shelf edge platform with extended reach drilling (ERD), and (2) subsea development. Both are very expensive options. The challenge is to make reliable probabilistic reserve estimates for the economic evaluation. Critical to the reserve estimates is quantitative geophysical reservoir characterization, or what is known in Chevron as reservoir properties from seismic. For Sadewa, elastic impedance inversion data can be used to distinguish gas sand from shale/brine sand and acoustic impedance inversion data can be used to derive porosity data but only for reservoirs close to or above tuning thickness. For reservoirs much less than the tuning thickness, for instance near the edges of the Sadewa reservoir bodies, seismic amplitude data are more reliable than inverted data for geophysical reservoir characterization. Reserves sensitiv- Figure 1. Sadewa location, bathymetry, and seismic data. (a) Shaded relief map. (b) Bathymetry map with curvature overlay with well locations and seismic shooting direction annotated. (c) Arbitrary seismic line showing shelf edge, Pleistocene channeling, Pliocene sequence, and Upper Miocene reservoir sequence. 312 The Leading Edge March 2009
Figure 2. Sadewa geology. (a) Schematic cross section showing stratigraphy of Kutei Basin with Paleocene to Eocene extension (rifting), Oligocene subsidence (sag), and Miocene Holocene deltaic progradation (after Saller et al.). (b) Generalized deepwater geological model showing Sadewa upper slope channels (modified from Saller, personal communicaton). (c) Type log with gamma ray and resistivity; gas sands are red and oil sands are green. ity analysis has shown that reservoir body area is the single most important parameter, and, thus, a special focus has been in the determination of the lateral extent of reservoirs using seismic amplitude data. The Sadewa reservoirs are Upper Miocene age and interpreted to have been deposited in a deepwater upper slope environment (Figure 2a). The geological model (Figure 2b) is very analogous to the present day bathymetry (Figure 1b), suggesting that the shelf edge has remained in roughly the same position for at least the last 7 8 million years. A type log for Sadewa with gamma-ray and resistivity logs (Figure 2c) reveals that many sands show some fining upwards, typical of deepwater deposition, and are gas-filled. A 3D seismic reflection data set was shot across the area in 2004 in an essentially bathymetric strike direction (Figure 1b). Cable length was 6000 m with bin size of 6.25 12.5 m and 60 fold. Processing paid particular attention to waterbottom multiple suppression (a severe problem in the Kutei deepwater) while maintaining amplitude integrity, and used a modified prestack time migration (PSTM). Reservoir properties from seismic The success of any reservoir properties from a seismic (RPFS) Figure 3. Angle stack maximum peak+trough amplitude maps from amplitude conditioned, compensated, and AVO-calibrated, zero-phase amplitude data set, for example, Sadewa reservoir. The gas reservoirs are shown with colors other than gray. project is dependent on high-quality data, favorable rock properties (that can be studied from wireline logs), and reliable inversion (also dependent on good wireline logs and data). The type of inversion required depends on the rock properties relationships. To achieve the necessary high-quality data for the Sadewa March 2009 The Leading Edge 313
Figure 4. Rock property analysis. (a) Density and P-velocity histograms and probability density functions for brine-filled and gas-filled sands compared to shale (shale is represented by green). (b) Acoustic impedance (gas sand and brine sand) and elastic impedance (gas sand) histograms and probability density functions for high-porosity, medium-porosity, and low-porosity sands, shale, and nonreservoir sand. (c and d) Monte Carlo seismic amplitude modeling. (c) Amplitude versus gradient P10-P90 limits for gas sand, oil sand, brine sand (all over shale with sand thickness of 100 ft) and shale/shale for near-, mid-, far-, and very far-angle stacks. (d) Amplitude versus gradient P10 P90 limits for gas sand of low, medium, and high porosity (sand thickness is 100 ft) and for average porosity sand with variable sand thickness (0 220 ft). On (d) the P20 shale over shale far-angle stack amplitude cutoff is annotated in white. Figure 5. Rock physics relationships for gas sand and shale. (a) P-impedance versus S-impedance and (b) P-impedance versus far-angle elastic impedance. Both shear and elastic impedances along with P-impedance are able to distinguish gas sand from shale, especially for high-porosity sands. 314 The Leading Edge March 2009
Figure 6. Rock physics analysis for lithology estimation and porosity estimation of sand. (a) The far-angle elastic impedance at 38 0 (EI38) can be used to differentiate gas sand from shale/brine sand. (b) A range of EI38 cutoffs can be used to estimate the probability of finding gas sand. (c) Once gas sand is estimated, a porosity volume can be derived from acoustic impedance using a regression relationship. Note the regression relationships for porosity estimation of gas sand and brine sand from acoustic impedance are different. RPFS study, amplitude conditioning (beyond what is done in basic processing), compensation, and AVO calibration are required. The conditioning consists of a number of processing steps including the application of a wavelet transform filter, f-k dip filtering, differential moveout, destretch, dynamic structural correction, and residual amplitude correction. The amplitude compensation is required because of shallow transmission effects, and a spatial amplitude correction is applied based on amplitude normalization of the geologically spatially uniform Pliocene sequence (Figure 1c). AVO calibration is achieved by applying well data average rms amplitude calibration multipliers from synthetics for each angle range near, mid, far, and very far. An example set of angle stack reservoir amplitude maps extracted from the seismic data after conditioning, compensation, and AVO calibration ( calibrated data set) is shown in Figure 3. The far-angle stack (effective angle of 38 0 ) is considered the optimum angle stack from the point of view of overall signal-to-noise and reservoir amplitude/coherency. Amplitude increasing with angle with relatively low amplitude on the near-angle stack indicates AVO Class 3. Rock-property analysis of all available well data shows that gas sand can confidently be distinguished from shale, largely as a result of density contrast. However, brine sand and shale are largely indistinguishable (Figure 4). The gas sands in Figure 4b were subdivided into porosity bands: high (>19%), medium (15 19%), low (10 15%), and nonreservoir (<10%). All penetrated Sadewa reservoir sands fall in the high-medium porosity bands, with the majority in the former. The Monte Carlo seismic amplitude modeling indicates an AVO Class 3 response, and that far angles give the best discrimination of gas sand. Additionally, Monte Carlo mod- March 2009 The Leading Edge 315
Figure 7. P10 and P20 shale over shale models (Shale:Shale) with a far-angle stack amplitude cutoff. (a) Far-angle stack amplitude tuning plot for gas, oil, and brine sands (high-porosity sand; sand-over-shale model) with P0 P50 shale over shale amplitudes annotated. (b) Geological model of upper slope channel and corresponding amplitude profile with amplitude/thickness cutoffs annotated. (c) Actual calibrated far-angle seismic amplitudes as penetrated by the Sadewa wells with amplitude associated with hydrocarbons plotted as green diamonds and amplitude not associated with hydrocarbons plotted as magenta squares and shale over shale P0 P50 amplitudes annotated. eling shows that high-porosity gas sands with a thickness near the tuning thickness also lead to better discrimination of gas sand from shale (Figures 4c and 4d). Based on the analysis of acoustic impedance versus shear/ elastic impedance data (Figure 5), and because estimated shear impedance data were less reliable than elastic impedance data, it was determined that both elastic impedance (Whitcombe, 2002) and acoustic impedance data sets could be reliably used to distinguish the gas sands. Further, rock physics relationships and modeled amplitudes indicate that the elastic impedance far-angle data at 38 0 (EI38) gives the best statistics for distinguishing the gas sands by the application of an EI38 cutoff (Figure 6a). A range of EI38 cutoff values can be used to estimate the probability of finding gas sand (Figure 6b), which is also desired for numerical simulations of various reservoir scenarios. For example, if EI38 of 25 500 ft/s.g/cc is selected as a cutoff value (as shown by the vertical line in Figure 6b), then for EI38 values less than this cutoff value there is at least 82% probability of finding gas sand from shale and 83% probability of finding gas sand from brine sand. After the gas sands have been identified in this way, a porosity volume can be estimated using the regression between porosity and acoustic impedance (Figure 6c). The above inversion strategy works well when the reservoir sands are near or above tuning thickness (typically 120 ft in this case). However, at the edges of the channel reservoir bodies, thicknesses will be below tuning thickness, and, therefore, a special approach is required to reliably determine the lateral reservoir extent. For reservoir sand thickness below tuning thickness, inverted impedance data are considered less reliable because of tuning effects (Galbiati et al., 2008), and the original calibrated angle stack amplitude data are considered more reliable. The approach adopted for determining the lateral extent of the Sadewa channel reservoirs is based upon the analysis of model amplitudes for the far-angle amplitude data set for (1) gas sand over shale and (2) shale over shale. Figure 7a shows the tuning curves for the far-angle (high-porosity sand) together with the P0 P50 shale over shale amplitudes (the P50 occurs at zero amplitude because the average shale over average shale causes no reflection). Figure 7b shows the channel sand of the geological model and how seismic amplitude will vary across the channel and the implications of choosing particular amplitude cutoffs. In this case, a P10 shale over shale far-angle stack amplitude cutoff is selected as a conservative scenario, but the implication is that reservoirs with thickness less than 30 ft will be deemed beyond the edge of the reservoir. A more optimistic scenario with P20 shale over shale 316 The Leading Edge March 2009
where inversion data are unreliable, has been addressed using quantitative seismic amplitudes. The complete RPFS strategy for Sadewa also includes the estimation of the downdip limit of gas sands (gas/brine sand). This RPFS workflow is considered the basis for reliable reserves estimation and thus will have a direct impact on field economics. Suggested reading. Elastic impedance normalization by Whitcombe (Geophysics, 2002). Seismic evaluation of reservoir quality and gas reserves of DHI supported deep-water systems, offshore Nile delta by Galbiati et al. (EAGE Extended Abstracts, 2008). Leaves in turbidite sands: The main source of oil and gas in the deep-water Kutei Basin, Indonesia by Saller et al. (AAPG Bulletin, 2006). Figure 8. Application of P10 and P20 shale over shale amplitude cutoffs on example far-angle stack seismic amplitude map. Acknowledgments: The authors thank the other members of the Sadewa subsurface team Yuniyanto, Lothar Schulte, Andrian Elim, and Suwarno. Also thanks to Craig Huber who managed the seismic processing, Steven Leslie who did the conditioning, and Larry Sydora who was instrumental in initiating the RPFS work. The approval to publish from Chevron, INPEX, BPMIGAS, and MIGAS management is also acknowledged. Corresponding author: pault@chevron.com far-angle stack amplitude cutoff would lead to reservoirs of thickness less than 20 ft being excluded. Figure 7c shows the same shale over shale amplitude cutoffs plotted with actual seismic amplitudes penetrated by the wells (color-coded as amplitudes with and without hydrocarbons). The real seismic amplitudes can also be used to determine shale over shale (or nonhydrocarbon) statistics and cutoffs and a similar answer results. Using the real seismic data (seismic amplitudes at sand tops at the wells) for determining the cutoffs is considered less reliable statistically than using the well data (seismic models with a range of layering and a range of rock properties) because the former data set is sparser. The impact of applying a P10 or P20 shale over shale far-angle stack amplitude cutoff on the real seismic amplitude maps is shown in Figure 8. The more conservative P10 cutoff (red outline) gives a more restricted reservoir lateral extent (and more disconnected bodies), whereas the more optimistic P20 cutoff (blue outline) connects more bodies in a perhaps more geologically meaningful manner. Clearly, the choice of cutoff has a direct impact on reservoir body area and thus on reserves estimation. Conclusions An effective and practical approach for distinguishing gas sand from shale/brine sand using elastic impedance data has been shown for the channelized deepwater Sadewa Field. Furthermore, the issue of determining the lateral extent of these reservoirs where sand thickness is below tuning, and March 2009 The Leading Edge 317