1887

Abstract

Summary

Conventionally, NTG is generated based on the lithofacies model (static model) that is typically driven or controlled by wells when populating the property. High uncertainty in lithofacies model when away from wells engenders the NTG model to be questionable.

From geophysical view, attempts had been performed to estimate NTG using seismic data through derivation of single linear relationship between the calculated NTG from well and single attribute of the seismic data. The single linear relationship is then applied onto that single attribute over the seismic volume to generate seismic NTG. However, such conventional approach has resulted in low correlation coefficient between the actual NTG and predicted NTG, causing the generated seismic NTG to be unreliable for predicting NTG when away from the well.

To overcome the above problem, the authors are looking for an improved method in generating reliable seismic-driven NTG cube for predicting NTG in between wells for Field A. In this paper, multi-attributes transform technique was used for estimating and generating 3D NTG cube where the produced results are promising. The outcome can be used for reservoir characterization, hydrocarbon volumetric calculation and upside potential exploration.

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/content/papers/10.3997/2214-4609.201700913
2017-06-12
2024-04-25
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References

  1. Conolly, P.A.
    [2005] Net pay estimation from seismic attributes. EAGEExtended Abstracts.
    [Google Scholar]
  2. [2007] A simple robust algorithm for seismic net pay estimation. The Leading Edge.
    [Google Scholar]
  3. Hampson, D.P., Schuelke, J.S. and Quirein, J.A.
    [2001] Use of multiattibute transforms to predict log properties from seismic data. Geophysics, 66, 220–236.
    [Google Scholar]
  4. Simm, R.
    [2009] Simple net pay estimation from seismic: a modeling study. First Break, 27.
    [Google Scholar]
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