1887

Abstract

Summary

Reservoir modelling is playing an increasingly important role in developing and producing hydrocarbon reserves. Detection and stochastic modeling of facies bodies such as channels has always posed a challenge for geologists and geophysicists. With modern seismic technology it has become standard practice to identify geobodies with the help of powerful seismic volume attributes such as spectral decomposition. Seismic attributes is used to provide stratigraphic features and when combined with geobody extraction, object modeling and stochastic facies modeling. Then it can provide the Geostatistical facies model with multiple realisations, which can be further used to calculate uncertainties associated with the geomodeling and hydrocarbon volume calculations. Geological background is given by . Based on seismic and well data they describe stacked channel-overbank systems with a large slopefan system. The channel deposits which can be easily observed on seismic time slices are characterized by thick sand-rich turbidites whereas over bank deposits generally show an aggradation mixture of sandstones, mudstones and siltstones.

The goal of this study is to develop techniques for capturing stratigraphic features identified by seismic attributes and use them as a guide for facies modelling. This workflow will also address the uncertainty of the size of the seismic geobodies.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601017
2016-05-30
2024-03-28
Loading full text...

Full text loading...

References

  1. Tan, C.H. and Schulte, L.
    [2015] Strategy for capturing the hydrocarbon volume uncertainty in case of few wells. 39th IPA Annual Convention & Exhibition, Extended Abstracts.
    [Google Scholar]
  2. Pacht, J.A., Beard, J.H., Weisser, G., Bouma, A.H., Bowen, B.E. and Vail, P.R.
    [1989] Seismic-Stratigraphic Analysis of Plio-Pleistocene Depositional Facies, East And West Cameron, Offshore Louisiana. SEG Technical Program, Expanded Abstracts, 798–800.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601017
Loading
/content/papers/10.3997/2214-4609.201601017
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