1887

Abstract

Summary

The framework of reservoir modelling is the information and understanding derived from the 3D seismic data covering the area of interest. Uncertainties associated with the underlying seismic data potentially lead to the inability to draw clear conclusions as to the size and productivity of the reservoir. In the presence of low saturation gas bodies in the overburden sediments, the loss of signal strength, frequency bandwidth and the complex wave kinematics compound the challenge.

We present a case study from a challenging deep-water oil field in southeast Asia. The project comprised of acquisition, processing, interpretation and QI groups working as an integrated team to meet the objectives of the project. The project utilized a multifaceted approach of combining ocean bottom node seismic data, optimizing preprocessing, building a high-resolution earth model, and using high-end imaging techniques, to significantly improve the overall quality of the seismic data beneath the large bodies of gas hydrates, clouds and free gas. The new data allows improved structural and stratigraphic interpretation providing a better understanding of the reservoir model and reducing the uncertainty related to the reservoir volume, connectivity and compartmentalization, thereby contributing significantly to the geological understanding of the field and influencing the future development decisions.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201801004
2018-06-11
2024-04-25
Loading full text...

Full text loading...

References

  1. Kristiansen, P., Dangle, D., Andren, E.P., and Cockrell, N.
    [2015] Deepwater OBN and source designature — Using the information in the data and improving the processing. 77th EAGE Conference and Exhibition.
    [Google Scholar]
  2. Xia, G., Matson, K., and Etgen, J.
    [2006] Multiple attenuation on OBS/OBC data by extended wavefield extrapolation. 68th EAGE Conference and Exhibition, Extended Abstracts.
    [Google Scholar]
  3. Woodward, M. J., Nichols, D., Zdraveva, O., Whitfield, P., and Johns, T.
    , 2008. A decade of tomography. Geophysics, 73 (5).
    [Google Scholar]
  4. Jiao, K., Sun, D., Cheng, X., and Vigh, D.
    , 2015. Adjustive full waveform inversion.SEG Technical Program Expanded Abstracts, 1091–1095.
    [Google Scholar]
  5. Cheng, X., Jiao, K., Sun, D., and Vigh, D.
    , 2015. A new approach of visco-acoustic waveform inversion in the time domain. SEG Technical Program Expanded Abstracts, 1183–1187.
    [Google Scholar]
  6. Zdraveva, O., Hydal, S. and Woodward, M.
    , 2013. Tomography with geological constraints: an alternative solution for resolving of carbonates. 75th EAGE Conference and Exhibition Extended Abstracts.
    [Google Scholar]
  7. Stopin, A., Plessix, R.-E., Kuehl, H., Goh, V., and Overgaag, K.
    , 2016. Application of visco-acoustic full waveform inversion for gas cloud imaging and velocity model building. 78th EAGE Conference & Exhibition Extended Abstracts
    [Google Scholar]
  8. Cavalca, M., I.Moore, L.Zhang, S.L.Ng, R.Fletcher, and Bayly, M.
    , 2011, Ray-based tomography for Q estimation and Q compensation in complex media. SEG Technical Program Expanded Abstracts, 3989–3993.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801004
Loading
/content/papers/10.3997/2214-4609.201801004
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