1887

Abstract

Summary

In this study we present an application of geostatistical AVA seismic inversion method for characterization of a unconventional Lower Paleozoic shale reservoir in Northern Poland. The target formations are of a small thickness (up tp 25 meters) and deeply buried (ca. 3 km) what makes their delineation and characterization especially difficult. An application of the iterative geostatistical AVA inversion method allowed for obtaining the high-resolution density, P-wave and S-wave velocity models together with the assessment of the uncertainty on the predictions. The obtained elastic property models were compared with the results of the deterministic simultaneous Amplitude-versus-Offset inversion proving that the application of a such sophisticated (geostatistical) inversion technique is a must while dealing with the thin and highly variable layers.

The inverted elastic models where further used to improve the prediction of a spatial distribution of the brittleness index with a machine learning (PSVM) algorithm by integrating well-log data and seismic rock property volumes.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201900691
2019-06-03
2024-03-29
Loading full text...

Full text loading...

References

  1. Azevedo, L., Nunes, R., Soares, A., Neto, G. S., & Martins, T. S.
    [2018] Geostatistical seismic Amplitude-versus-angle inversion. Geophysical Prospecting, 66(S1), 116–131
    [Google Scholar]
  2. Cyz, M., Mulińska, M, Pachytel, R., & Malinowski, M.
    [2018] Brittleness prediction for the Lower Paleozoic shales in northern Poland. Interpretation, 6(3), SH13–SH23
    [Google Scholar]
  3. Cyz M, Malinowski M
    . [2018] Seismic Azimuthal Anisotropy Study Of The Lower Paleozoic Shale Play In Northern Poland, Interpretation6 (3), SH1–SH12
    [Google Scholar]
  4. FrancisA.
    [2014] A Simple Guide to Seismic Inversion, GEOExPro,Vol. 10, No. 2
    [Google Scholar]
  5. Fung, G M., and O. L.Mangasarian
    [2005] Multicategory proximal support vector machine classifiers: Machine Learning, 59, 77–97
    [Google Scholar]
  6. Koren, Z., and I.Ravve
    [2011] Full-azimuth subsurface angle domain wavefield decomposition and imaging. Part I— Directional and reflection image gathers: Geophysics,76, no. 1, S1–S13
    [Google Scholar]
  7. Kowalski, H., P.Godlewski, W.Kobusinski, W.Makarewicz, M.Podolak, A.Nowicka, Z.Mikolajewski, D.Chase, R.Dafni, A.Canning, and Z.Koren
    [2014] Imaging and characterization of a shale reservoir onshore Poland, using full-azimuth seismic depth: First Break, 32, 101–109
    [Google Scholar]
  8. Soares, A.
    [2001], Direct sequential simulation and cosimulation: Mathematical Geology, 33, 911–926
    [Google Scholar]
  9. Zhang, B., T.Zhao, X.Jin, and K. J.Marfurt
    [2015] Brittleness evaluation of resource plays by integrating petrophysical and seismic data analysis: Interpretation, 3, no. 2, T81-T92, doi: 10.1190/INT‑2014‑0144.1
    https://doi.org/10.1190/INT-2014-0144.1 [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201900691
Loading
/content/papers/10.3997/2214-4609.201900691
Loading

Data & Media loading...

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