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Gas Reservoir Characterization Using Lp-Norm Constrained High-Resolution Seismic Spectral Attributes

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Abstract

Reservoir prediction is often a primary objective in seismic exploration, especially for deep hydrocarbon detection with its potential for assessing oil and gas resources. Time–frequency analysis has become a successful method for detecting subsurface features and can also be used to identify potential hydrocarbon reservoirs. However, current applications for hydrocarbon detection often occur at low resolution, due to the influence of the windowing functions and lack of prior constraints, thereby affecting gas prediction for thin reservoirs. To rectify this issue, we investigate the use of sparse inverse spectral decomposition (SISD) based on the wavelet transform, which adopts an lp norm to constrain the time–frequency spectra, and thereby provides a more highly concentrated time–frequency representation than conventional spectral decomposition techniques. The main objective of this paper is to investigate the performance of spectral attributes derived from lp-norm constrained SISD and its application to the characterization of deep-marine dolomite reservoirs in southwest China. The gas-bearing zones in target reservoir are predicted well by extracting and analyzing the spectral amplitude responses of different frequency components. The predicted favorable gas-bearing areas are closely related to local structures in the target reservoir, which are also in good agreement with gas-testing results at three well locations and may be used to guide subsequent exploration well development in this region.

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References

  • Askari, R., & Siahkoohi, H. R. (2008). Gound roll attenuation using the S and x-f-k transforms. Geophysics Prospecting, 56, 105–114.

    Google Scholar 

  • Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2, 183–202.

    Article  Google Scholar 

  • Bernáth, G. (2012). Identification of fracture zones in a tight gas reservoir. Geosciences and Engineering, 1, 15–20.

    Google Scholar 

  • Bonar, D. C., & Sacchi, M. D. 2010. Complex spectral decomposition via inversion strategies. SEG Technical Program Expanded Abstracts 1408–1412.

  • Candes, E. J., Wakin, M. B., & Boyd, S. P. (2008). Enhancing sparsity by reweighted l1 minimization. Journal of Fourier Analysis and Applications, 14, 877–905.

    Article  Google Scholar 

  • Castagna, J. P., & Backus, M. (1993). Offset-dependent reflectivity: The theory and practice of AVO analysis. Tulsa: Society of Exploration Geophysicists.

    Book  Google Scholar 

  • Castagna, J. P., & Sun, S. (2006). Comparison of spectral decomposition methods. First Break, 24, 75–79.

    Google Scholar 

  • Castagna, J. P., Sun, S., & Siegfried, R. (2003). Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22, 120–127.

    Article  Google Scholar 

  • Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001). Atomic decomposition by basis pursuit. SIAM Review, 43, 129–159.

    Article  Google Scholar 

  • Debeye, H. W. J., & Riel, P. V. (1990). Lp-norm deconvolution. Geophysical Prospecting, 38, 381–403.

    Article  Google Scholar 

  • Duan, Y., Peng, Z., Zeng, L., & Bi, M. (1949). Carbonate reservoir and gas-bearing property detection using sweetness. SEG Technical Program Expanded.

  • Ebrom, D. (2004). The low-frequency gas shadow on seismic sections. The Leading Edge, 23, 772.

    Article  Google Scholar 

  • Gabor, D. (1946). Theory of communication. Journal of IEEE, 93, 429–441.

    Google Scholar 

  • Gao, J. H., Chen, W. C., Li, Y. M., et al. (2003). Generalized S transform and seismic response analysis of thin interbeds surrounding regions by Gps. Chinese Journal of Geophysics, 46, 759–768.

    Article  Google Scholar 

  • Han, L., Han, L. G., & Li, Z. (2012). Inverse spectral decomposition with the SPGL1 algorithm. Journal of Geophysics and Engineering, 9, 423–427.

    Article  Google Scholar 

  • Herrmann, F. J., Friedlander, M. P., & Yilmaz, O. (2012). Fighting the curse of dimensionality: Compressive sensing in exploration seismology. IEEE Signal Processing Magazine, 29, 88–100.

    Article  Google Scholar 

  • Kiani, A. (2010). Energy attenuation analysis and quality factor in direct hydrocarbon detection. EAGE conference & exhibition.

  • Li, Q., Di, B. R., Wei, J. X., Yuan, S. Y., & Shi, W. P. (2016). The identification of multi-cave combinations in carbonate reservoirs based on sparsity constraint inverse spectral decomposition. Journal of Geophysics and Engineering, 13, 940–952.

    Article  Google Scholar 

  • Li, F. Y., Zhang, B., Zhai, R., Zhou, H. L., & Marfurt, K. J. (2017). Depositional sequence characterization based on seismic variational mode decomposition. Interpretation 5, SE97–SE106.

  • Li, Y. D., & Zheng, X. D. (2008). Spectral decomposition using Wigner-Ville distribution with applications to carbonate reservoir characterization. The Leading Edge, 27, 1050–1057.

    Article  Google Scholar 

  • Liu, B. Y., Gao, J. H., & Li, S. J. (2017). Thin gas reservoir detection using high resolution spectra attenuation attributes. International geophysical conference 1474–1477.

  • Liu, C. C., Han, L., Zhang, Y. M., & Ye, Y. F. (2015). Application of seismic complex decomposition on hydrocarbon detection. EAGE conference & exhibition.

  • Liu, H. Q., Zhang, F. C., Zhang, M. Q., & Shao, B. C. (2014). The improvement of dynamic matching pursuit algorithm and its application of gas reservoir. EAGE conference & exhibition.

  • Luo, C., Li, X. Y., & Huang, G. T. (2018). Hydrocarbon identification by application of improved sparse constrained inverse spectral decomposition to frequency-dependent AVO inversion. Journal of Geophysics and Engineering, 15, 1446–1459.

    Article  Google Scholar 

  • Marfurt, K. J., & Kirlin, R. L. (2001). Narrow-band spectral analysis and thin-bed tuning. Geophysics, 66, 1274–1283.

    Article  Google Scholar 

  • Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory-Part I: Complex signal and scattering in multilayered media. Geophysics, 47, 203–221.

    Article  Google Scholar 

  • Oldenburg, D. W., Scheuer, T., & Levy, S. (1983). Recovery of the acoustic impedance from reflection seismograms. Geophysics, 48, 1318–1337.

    Article  Google Scholar 

  • Ostrander, W. J. (1984). Plane-wave reflection coefficients for gas sands at nonnormal angles of incidence. Exploration Geophysics, 15, 193.

    Article  Google Scholar 

  • Partyka, G., Gridley, J., & Lopez, J. (1999). Interpretational applications of spectral decomposition in reservoir characterization. The Leading Edge, 18, 353–360.

    Article  Google Scholar 

  • Portniaguine, O., & Castagna, J. P. (2004). Inverse spectral decomposition. SEG Technical Program Expanded Abstracts 1786–1789.

  • Puryear, C. I., Portniaguine, O. N., Cobos, C. M., & Castagna, J. P. (2012). Constrained least-squares spectral analysis: application to seismic data. Geophysics, 77, V143–V167.

    Article  Google Scholar 

  • Sacchi, M. D. (1997). Reweighting strategies in seismic deconvolution. Geophysical Journal International, 129, 651–656.

    Article  Google Scholar 

  • Sancevero, S. S., Remacre, A. Z., & de Souza Portugal, R. (2006). O papel da inversão para a impedância acústica no processo de caracterização sísmica de reservatórios. Revista Brasileira de Geofisica, 24, 495–512.

    Article  Google Scholar 

  • Sinha, S., Routh, R., Anno, P., & Castagna, J. (2005). Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, 70, P19–P25.

    Article  Google Scholar 

  • Stockwell, R. G., Mansinha, L., & Lowe, R. P. (1996). Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44, 998–1001.

    Article  Google Scholar 

  • Sun, S., Castagna, J., & Siegfried, R. (2002). Examples of wavelet transform time-frequency analysis in direct hydrocarbon detection. SEG Technical Program Expanded Abstracts 457–460.

  • Tayyab, N. M., & Shazia, A. (2018). Characterization of shallow-marine reservoirs of Lower Eocene carbonates, Pakistan: Continuous wavelet transforms-based spectral decomposition. Journal of Natural Gas Science and Engineering, 56, 629–649.

    Article  Google Scholar 

  • Wang, Y. C., Huang, H. D., Yuan, S. Y., Zhang, S., & Li, B. W. (2016a). Gas prediction using low-frequency components of variable-depth streamer seismic data applied to the deepwater area of the South China Sea. Journal of Natural Gas Science and Engineering, 34, 1310–1320.

    Article  Google Scholar 

  • Wang, Z. W., Wang, X. L., Min, X., et al. (2012). Reservoir information extraction using a fractional fourier transform and a smooth pseudo wigner-ville distribution. Applied Geophysics, 9, 391–400.

    Article  Google Scholar 

  • Wang, X., Zhang, B., Li, F., Qi, J., & Bai, B. (2016b). Seismic time-frequency decomposition by using a hybrid basis-matching pursuit technique. Interpretation, 4, T263–T272.

    Google Scholar 

  • Xu, Z. H., Zhang, B., Li, F. Y., et al. (2018). The application of well logs decomposition using VMD to the sequence stratigraphic analysis of a conglomerate reservoir. Geophysics, 83(4), B221–B228.

    Article  Google Scholar 

  • Xue, Y. J., Cao, J. X., & Tian, R. F. (2013). A comparative study on hydrocarbon detection using three EMD-based time-frequency analysis methods. Journal of Applied Geophysics, 89, 108–115.

    Article  Google Scholar 

  • Xue, Y. J., Cao, J. X., & Tian, R. F. (2014). EMD and Teager-Kaiser energy applied to hydrocarbon detection in a carbonate reservoir. Geophysical Journal International, 197, 277–291.

    Article  Google Scholar 

  • Yin, X. Y., & Zhang, S. (2014). Bayesian inversion for effective pore-fluid bulk modulus based on fluid-matrix decoupled amplitude variation with offset approximation. Geophysics, 79, R221–R232.

    Article  Google Scholar 

  • Yuan, S. Y., Liu, J. W., Wang, S. X., Wang, T. Y., & Shi, P. D. (2018). Inversion-based 3-D seismic denoising for exploring spatial edges and spatio-temporal signal redundancy. IEEE Geoscience and Remote Sensing Letters, 15, 1682–1686.

    Article  Google Scholar 

  • Yuan, S. Y., Liu, Y., Zhang, Z., Luo, C. M., & Wang, S. Y. (2019a). Prestack stochastic frequency-dependent velocity inversion with rock-physics constraints and statistical associated hydrocarbon attributes. IEEE Geoscience and Remote Sensing Letters, 16, 140–144.

    Article  Google Scholar 

  • Yuan, S. Y., Wang, S. X., Luo, Y. N., Wei, W. W., & Wang, G. C. (2019b). Impedance inversion by using the low-frequency full-waveform inversion result as an a priori model. Geophysics, 84, R149–R164.

    Article  Google Scholar 

  • Yuan, S. Y., Yang, S., Wang, T. Y., Qi, J., & Wang, S. X. (2020). Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies. Petroleum Science. https://doi.org/10.1007/s12182-020-00490-6.

    Article  Google Scholar 

  • Zhang, G. L. (2011). Low-frequency absorption attenuation gradient detection based on improved generalized S transform. Chinese Journal of Geophysics, 54, 2407–2411. (in Chinese).

    Google Scholar 

Download references

Acknowledgements

We thank J. Gao and S. Li for valuable help with the field data. This work was financially supported by the National Key R&D Program of China (2018YFA0702504), the National Natural Science Foundation of China (41974140), the Major Scientific Research Program of Petrochina Science and Technology Management Department “Comprehensive Seismic Prediction Technology and Software Development of Natural Gas” (2019B-0607), the Scientific Research & Technology Development Project of China National Petroleum Corporation (2017D-3504), the National Science and Technology Major Project (2017ZX05005-004), and the Fundamental Research Funds for the Central Universities (2462019QNXZ03).

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Authors and Affiliations

  1. State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Changping, Beijing, 102249, China

    Tieyi Wang, Sanyi Yuan, Shan Yang & Shangxu Wang

  2. Exploration and Production Research Institute, Sinopec, Beijing, 100083, China

    Rui Wang

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  1. Tieyi Wang
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Wang, T., Yuan, S., Wang, R. et al. Gas Reservoir Characterization Using Lp-Norm Constrained High-Resolution Seismic Spectral Attributes. Pure Appl. Geophys. 177, 5417–5433 (2020). https://doi.org/10.1007/s00024-020-02585-y

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