1887
Volume 73, Issue 3
  • E-ISSN: 1365-2478

Abstract

Abstract

Wide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high‐density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude‐preserving processing, we obtained the high‐quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P‐wave impedance and /. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short‐axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short‐axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value.

© 2025 European Association of Geoscientists & Engineers. © 2024 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13609
2025-02-27
2026-02-19

  • From This Site

    /content/journals/10.1111/1365-2478.13609
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aki, K. & Richards, P. (1980) Quantitative seismology: W. H. Freeman &Co.
     [Google Scholar]
  2. Alemie, W. &Sacchi, M.D. (2011) High‐resolution three‐term AVO inversion by means of a trivariate Cauchy probability distribution. Geophysics, 76(3), R43–R55.
     [Google Scholar]
  3. Benshan, Y. & Naida, S. (2015) Current development situations and suggestion on the techniques of ocean bottom seismic acquisition. Offshore Oil, 35(2), 1–5.
     [Google Scholar]
  4. Buland, A. & Omre, H. (2003) Bayesian linearized AVO inversion. Geophysics, 68, 185–198.
     [Google Scholar]
  5. Castagna, J. P. (1993) Petrophysical imaging using AVO. The Leading Edge, 12, 172–179.
     [Google Scholar]
  6. Chen, C., Liu, Y., Wang, X. et al. (2019) Difference between OBC and OBN seismic exploration. EGP, 29(2), 180–193.
     [Google Scholar]
  7. Chen, H.Z., Yin, X.Y., Zhang, J.Q. & Zhang, G.Z. (2014) Seismic inversion for fracture rock physics parameter using azimuthally anisotropic elastic impedance. Chinese Journal of Geophysics, (in Chinese) 57(10), 3431–3441.
     [Google Scholar]
  8. Connolly, P. (1999) Elastic impedance. Leading Edge, 18(4), 438–452.
     [Google Scholar]
  9. Dash, R., Spence, G., Hyndman, R., Grion, S., Wang, Y. & Ronen, S. (2009) Wide‐area imaging from OBS multiples. Geophysics, 74(6), Q41–Q47.
     [Google Scholar]
  10. Downton, J.E. & Lines, L.R. (2004) Three‐term AVO waveform inversion. 74th Annual International Meeting, SEG, Expanded Abstracts. Houston, TX, SEG. pp. 215–219.
     [Google Scholar]
  11. Duan, W.S., Li, F., Wang, Y.C. & Wang, C.H. (2013) Offset vector tile for wide‐azimuth seismic processing. Oil Geophysical Prospecting, 48(2), 206–213.
     [Google Scholar]
  12. Feng, L., Kui, W. & Bingjie, W. (2017) Strike‐slip structure characteristics and genesis analysis of A tectonic, south of Liaozhong sag in the Liao‐dong bay depression. Petroleum Geology and Engineering, 31(6), 1–6.
     [Google Scholar]
  13. Feng, L., Kui, W. & Lin, W. (2016) Physical characteristics and controlling factors of reservoir of section three of Dongying formation in X oilfield, Liaozhong sag. Journal of Northeast Petroleum University, 40(6), 9–17.
     [Google Scholar]
  14. Handong, H., Jianjun, Z. & Wei, L. (2019) Application of chaos inversion controlled by seismic facies in prediction of lithologic reservoirs: a case study of M well area in RY depression. CT Theory and Applications, 28(5), 549–557.
     [Google Scholar]
  15. Huajian, Z. (2019) Prediction method of channel sand body based on prestack migration in OVT domain. Lithologic Reservoirs, 31(4), 112–120.
     [Google Scholar]
  16. Huang, H., Yuan, S., Zhang, Y., Zeng, J. & Mu, W. (2016) Use of nonlinear chaos inversion in predicting deep thin lithologic hydrocarbon reservoirs: a case study from the Tazhong oil field of the Tarim Basin China. Geophysics, 81(6), B221–B234.
     [Google Scholar]
  17. Li, B., Feng, Q.K., Zhang, Y.B. et al. (2019) Summary of development and key issues of offshore OBC‐OBN technology. Geophysical and Geochemical Exploration, 43(6), 1277–1284.
     [Google Scholar]
  18. Ma, J.F. & Morozov, I.B. (2005) The exact elastic impedance as a ray path and angle of incidence function. 75th Annual Internation Meeting, SEG, Expanded Abstracts, Houston, TX, SEG. pp. 269–272.
     [Google Scholar]
  19. Nur, A. & Simmons, G. (1969) Stress‐induced velocity anisotropy in rock: an experimental study. Journal of Geophysical Research, 74(27), 6667–6674.
     [Google Scholar]
  20. Qin, S., Liu, L., Li, X., Xie, L. & Liu, Y. (2021) Application of prestack geostatistical inversion of sub‐azimuth Markov chain to thin sandstone prediction in block Q of an oilfield. Geology and Exploration, 57(1), 0156–0165.
     [Google Scholar]
  21. Quan, L., Changsong, L. & Wei, W. (2011) The Characteristics of sequence stratigraphy and depositional systems of the Paleogene Dongying Formation in Liaozhong sag. Journal of Southwest Petroleum University, (Science & Technology Edition), 33(2), 43–50.
     [Google Scholar]
  22. Smith, G.C. & Gidlow, P.M. (1994) Weighted stacking for rock property estimation and detection of gas. Geophysical Prospecting, 35, 993–1014.
     [Google Scholar]
  23. Thomsen, L. (1986) Weak elastic anisotropy. Geophysics, 51(10), 1954–1966.
     [Google Scholar]
  24. Thomsen, L. (1988) Reflection seismology over azimuthally anisotropic media. Geophysics, 53(2), 304–313.
     [Google Scholar]
  25. Xingyao, Y., Hongxue, Z. & Zhaoyun, Z. (2018) Research status and progress of 5D seismic data interpretation in OVT domain. Geophysical Prospecting for Petroleum, 57(2), 155–178.
     [Google Scholar]
  26. Zhiqiang, W., Xunhua, Z. & Weina, Z. (2021) Ocean Bottom Station Nodes (OBN): progress and achievement. Progress in Geophysics(in Chinese), 36(1), 0412–0424.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13609
Loading
Prestack reservoir prediction method in ray parameter domain based on wide azimuth ocean bottom node seismic data
Geophysical Prospecting 73, 942 (2025); https://doi.org/10.1111/1365-2478.13609
/content/journals/10.1111/1365-2478.13609
/content/journals/10.1111/1365-2478.13609
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): anisotropy; Bayesian prestack inversion; ellipse fitting; ray parameter domain; wide azimuth

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error