sd and ant tracking carbonate

73rd EAGE Conference & Exhibition incorporating SPE EUROPEC 2011 Vienna, Austria, 23-26 May 2011

B035Application of Spectral Decomposition and AntTracking to Fractured Carbonate ReservoirsD.S. Sun* (BGP-CNPC), Y. Ling (BGP-CNPC), Y. Bai (BGP-CNPC), X. zhang(BGP-CNPC) & X.Y. Xi (BGP-CNPC)

SUMMARYThis paper uses the integrated application of ant tracking and spectral decomposition to detect minor faultsand fractures based on the VSP-driven processing seismic data in fractured carbonate reservoirs. A newmethod is developed to improve imaging of faults and fractures using “discrete frequency ant trackingdata”. Discrete frequency ant tracking data is computed using discrete frequency phase data obtained fromspectral decomposition. Discrete frequency ant tracking data is more effective at detecting faults andfractures than full spectrum data. The different discrete frequency ant tracking attribute maps show that thehigh-frequency ant tracking data is more effective at detecting faults and fractures. When compared withthe full spectrum data, the reconstructed ant tracking data produces more detailed images of faults andfractures. The results of our case study shows, this has the potential to be a highly effective and veryvaluable seismic method.


Introduction

Seismic attributes play an important role in detecting the faults and fractures since the coherence cube was first developed (Bahorich and Farmer, 1995). Other coherence algorithms (e.g. Marfurt et al., 1998; Gersztenkorn and Marfurt, 1999) have been applied, and curvature attributes (Roberts, 2001) were developed to improve the quality of images of faults and fractures. Now, the “ant tracking algorithm” (Pedersen et al., 2003) is widely used to enhance the resolution of the faults and fractures. All these investigations, however, are based on full or dominant spectrum seismic data. Spectral decomposition (Partyka et al., 1999) provides a novel means of utilizing seismic data and a method called “time-frequency 4-D cube” was developed to extract seismic information from seismic data based on the different geologic scales in different frequency bands. Using these methods, discrete frequency coherence cubes (Sun et al., 2010) can be applied to detect faults and favourable fracture zones that cannot be easily detected using the full spectrum coherence data. By integrating the advantages of ant tracking and spectral decomposition, we developed another method to improve the imaging of faults and fractures called “discrete frequency ant tracking,” that permits the detection of fractured carbonate reservoirs. This method is computed using discrete frequency phase data obtained from spectral decomposition and the results of our case study show this has the potential to be a highly effective and very valuable seismic method.

Methodology and Processing Flow

Discrete frequency ant tracking is a new method that uses seismic data and the discrete Fourier transform (DFT) to detect faults and fractures. Theory analysis shows that the high frequency component is more sensitive to subtle perturbations in the seismic character, and therefore it is ideal for detecting lateral geological discontinuities (minor faults and fractures). This method allows us to detect the subtle faults and fractures that are not easily observed through dominant spectrum seismic data and is simply calculated in the following three steps. 1) Computation is done via running spectral analysis, which involves calculating the amplitude spectrum and phase spectrum of the input seismic data. The spectral components are then sorted into common frequency component data. 2) The discrete frequency ant tracking data are calculated using the discrete frequency cubes calculated above via spectral decomposition. 3) After interpreting the multi-scale frequency ant tracking data, discrete ant tracking data that reveal faults and fractures distribution better are selected for volume merging and reconstruction to create a new set of ant tracking data.

Application and Results

• Geologic and Seismic Background The study area is located at NP sag, in Bohaiwan basin of Northeast China. It is a carbonate buried hill, and the main rock types are limestone and dolomite. The reservoir exhibits lower matrix porosities and is impermeable. The late-formed unfilled tectonic fractures and faults are the dominant reservoir space for oil migration and accumulation. Fractures can not only act as reservoir spaces, but can connect the pore spaces between the different rock types and promote carbonate dissolution at later stages of the reservoir. 4 wells have been drilled, and 2 of the wells are very effective in producing hydrocarbon. We employed a VSP-driven and reservoir focused pre-stack seismic processing flow to process the 3D seismic data. The processing flow includes: time-frequency domain spherical spreading and


absorption compensation for the near-surface and earth absorption effects, two-step statistical deconvolution of both shot and receiver gathers (Ling et al. 1998) aimed at suppressing land reverberation and the removal of the spatial wavelet difference. A geophysical QC procedure (Gao et al., 2009) is implemented in all the major processing steps. After caring out the above analysis of the carbonate buried hill in the study area, the amplitude attribute was extracted from the relevant horizon in the seismic data (Figure 1). The results show that the amplitude attribute is closely related to the fractures and can be used to detect favourable fracture zones. In figure 1, the low amplitude stripes (dark red) are representative of the small faults.

Figure 1 Fractured zone predicted by amplitude attribute in a 3D map

Figure 2 Fractures detected in ant tracking attribute map and FMI data

The core and FMI data show that all of these 4 wells have well developed factures (figure 2), including single facture and facture clusters, and the main orientation is approximately NEE., The amplitude and ant tracking attribute maps of the buried hill can be used to determine the minor faults with the same orientation as fractures identified in the FMI data at the location of each well (figure 1), with the exception of well NP2-82. Why is this? To address this problem, we used the discrete frequency ant tracking data. • Application of Spectral Decomposition and Ant Tracking Faults and fractures occur on many scales in the Earth. Therefore, we can detect the faults and fractures found at different scales using different frequency bands. Spectral analysis shows that the bandwidth of seismic data of the study area is about 10-45Hz. Therefore 8 discrete frequency bands are calculated by increasing frequency sampling by 5Hz.


In this study, we tested amplitude tuning cubes, phase tuning cubes, discrete frequency ant tracking data computed from discrete frequency amplitude cubes and phase cubes for detecting small faults and fractures. The results show that the small faults are clearest in the discrete frequency ant tracking data computed from discrete frequency phase cubes.

Figure 3 The discrete frequency ant tracking attribute maps of the surface of the carbonate buried hill. (a) 15Hz, (b) 20Hz, (c) 25Hz (d) 30Hz Figure 3 shows the 4 maps of top of the buried hill (15Hz, 20Hz, 25Hz and 30Hz) determined from the 8 discrete frequency ant tracking data computed with the discrete phase data. This data can obtain better spatial resolution for imaging faults, and can determine the spatial extent of these zones of abundant fractures. By comparing these different discrete frequency ant tracking attribute maps, the higher frequency data gives greater detail of faults and fractures. In the 30Hz ant tracking attribute map, small faults or fractures can at last be detected at the location of well NP2-82.

Figure 4 Comparison of original (a) and reconstructed (b) ant tracking attribute of top of carbonate buried hill After the interpretation of multi-scale frequency data, discrete ant tracking data that reveal faults and fractures distribution better (15Hz, 20Hz, 25Hz and 30Hz) were selected to create a new data cube by means of volume merging and reconstruction. Figure 4b shows the reconstructed ant tracking data. Compared with the full spectrum data, it offers a higher level of detail for the faults and fractures.


After integrated study of the seismic attributes, well logs and production data, we determined the favourable zones for a fractured reservoir and a reservoir model for the carbonate buried hill. The main factor controlling the carbonate reservoir is the small faults and fractures. The hydrocarbon reservoirs are located at high position in the buried hill along the large fault and the hydrocarbon is also clustered around small faults and fractures. Two wells were drilled based on the results of this study and very productive in the regions predicted to be enriched in hydrocarbons (figure 5).

Figure 5 Structure map of top of buried hill overlaid on attribute maps (a: amplitude, b: reconstructed ant tracking attribute) and the prediction of hydrocarbon enriching areas (pink colour)

Conclusions

The results reveal that the discrete frequency ant tracking data can be used to detect fault and fracture rich zones that are not recognized by full spectrum data. The different discrete frequency ant tracking attribute maps show that the high-frequency data can detect faults and fractures more effectively. The reconstructed ant tracking data offers a greater level of detail for the faults and fractures than the full spectrum data.

References

Bahorich, M.S., and Farmer, S.L., 1995, 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube: SEG Expanded Abstract, 93-96.

Gao, J., Ling, Y., Sun, D.S. and Lin, J.X., 2009, Geophysical and geological QC in seismic data processing: SEG Expanded Abstracts, 624-628.

Gersztenkorn, A., and K. J. Marfurt, 1999, Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping: Geophysics, 64, 1468–1479.

Ling, Y., J. Gao, and R. J. Zhang, 1998, Sand dune reverberation and its suppression: The Leading Edge , 17, no. 5, 697.

Marfurt, K. J., R. L. Kirlin, S. L. Farmer, and M. S. Bahorich, 1998, 3-D seismic attributes using a semblance-based coherency algorithm: Geophysics, 63, 1150–1165.

Partyka, G., J. Gridley, and J. Lopez, 1999, Interpretational applications of spectral decomposition in reservoir characterization: The Leading Edge, 18, no. 3, 353–360.

Pedersen, S.I., Skov, T., Hetlelid, A., Fayemendy, P., Randen, T., Sonneland, L., 2003, New paradigm of fault interpretation: SEG Expanded Abstracts, 350-353.

Roberts, A., 2001, Curvature attributes and their application to 3D interpreted horizons. First Break, 19, 85-99.

Sun, D.S., Ling, Y., Guo X.Y., Gao, J. and Lin, J.X., 2010, Application of discrete frequency coherence cubes in the fracture detection of volcanic rocks in full-azimuth seismic data: SEG Expanded Abstracts, 1342-1345.

sd and ant tracking carbonate

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