ARTICLE

Unconventional reservoir characterization based on spectrally corrected seismic attenuation estimation

FANGYU LI1 HUAILAI ZHOU2 TAO ZHAO1 KURT J. MARFURT1
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1 University of Oklahoma, Norman, OK 73019, U.S.A.,
2 State Key Laboratory of Oil and Gas Reservoir Geology and Exploration, Chengdu University of Technology, Chengdu, Sichuan 610059, P.R. China.,
JSE 2016, 25(5), 447–461;
Submitted: 2 March 2016 | Accepted: 15 July 2016 | Published: 1 October 2016
© 2016 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Li, F., Zhou, H., Zhao, T. and Marfurt, K.J., 2016. Unconventional reservoir characterization based on spectrally corrected seismic attenuation estimation. Journal of Seismic Exploration, 25: 447-461. Fracture characterization is critical in exploration and development in unconventional resource plays. Fluid-filled fractures and cracks directly alter the effective impedance of rocks, attenuate amplitude and distort seismic spectrum, all of which make the seismic attenuation estimation a promising tool for characterizing fracture system. However, existing methods for estimating seismic attenuation are usually based on 'Constant Q' model, which ignores the interference from reflectivity anomalies. For unconventional reservoirs, the spectrum of the reflected wave may be affected by the presence of thin (shales) beds in the formation, which makes Q estimates less reliable. We employ a non-stationary Q model to characterize attenuation, and correct the reflected spectrum by using inverted reflectivity sequence based on well logs to remove local thin-bed effects from seismic reflection data. In synthetic examples, variance in the estimated values and unphysical negative Q values are reduced sgnificantly. Following the workflow, we also applied attenuation estimation on a seismic survey acquired over the Barnett Shale. The recovered Q estimates have a good correspondence with the production data. Though, the attribute is the average over a target formation, this may be sufficient to find evidence of fluid-filled fractures, or variation in lithology.

Keywords
seismic attenuation estimation
localized spectral correction
time-variant Q model
unconventional reservoir characterization
thin beds.
References
  1. Burns, D., Willis, M.E., Minsley, B. and Tokséz, M.N., 2004. Characterizing subsurface fracturesfrom reflected and scattered seismic energy. 112th SEGJ Conf., Tokyo.
  2. Chopra, S. and Marfurt, K.J., 2008. Emerging and future trends in seismic attributes. TLE, 27:298-318.
  3. Dasgupta, R. and Clark, R.A., 1998. Estimation of Q from surface seismic reflection data.Geophysics, 63: 2120-2128.
  4. Grossman, J.P., Margrave, G.F., Lamoureux, M.P. and Aggarwala, R., 2001. Constant-Q waveletestimation via a nonstationary Gabor spectral model. Techn. Rep., CREWES, Univ. ofCalgary, Calgary, Alberta.
  5. Hackert, C.L. and Parra, J.O., 2004. Improving Q estimates from seismic reflection data usingwell-log-based localized spectral correction. Geophysics, 69: 1521-1529.
  6. Hauge, P.S., 1981. Measurements of attenuation from vertical seismic profiles. Geophysics, 46:1548-1558.UNCONVENTIONAL RESERVOIR CHARACTERIZATION 461
  7. Lynn, H. and Beckham, W., 1998. P-wave azimuthal variations in attenuation, amplitude andvelocity in 3D field data: Implications for mapping horizontal permeability anisotropy.
  8. Expanded Abstr., 68th Ann. Internat. SEG Mtg., New Orleans: 193-196.
  9. Lynn, H., Lynn, W., Obilo, J. and Agarwal, V., 2015. Azimuthal pre-stack depth migration forin-situ stress evaluation in a fractured carbonate oil reservoir: predrill prediction of
  10. Instantaneous Shut-In Pressure gradients: GSOC talk.
  11. Pérez, R., 2009. Quantitative petrophysical characterization of the Barnett shale in Newark eastfield, Fort Worth Basin. M.Sc. thesis, University of Oklahoma, Norman, OK.
  12. Quan, Y. and Harris, J.M., 1995. Seismic attenuation tomography using the frequency shift method.Geophysics, 62: 895-905.
  13. Sheriff, R.E. and Geldart, L.P., 1995. Exploration Seismology, 2nd ed., Cambridge UniversityPress, Cambridge.
  14. Verma, S., Roy, A., Perez, R. and Marfurt, K.J., 2012. Mapping high frackability and high TOCzones in the Barnett Shale: supervised probabilistic neural network vs. unsupervisedmulti-attribute Kohonen SOM. Expanded Abstr. , 82nd Ann. Internat. SEG Mtg., Las Vegas.
  15. White, R.E., 1992. The accuracy of estimating Q from seismic data. Geophysics, 57: 1508-1511.
  16. Zhang, C.J. and Ulrych, T.J., 2002. Estimation of quality factors from CMP records. Geophysics,67: 1542-1547.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing