1887

Abstract

Summary

We present a Bayesian framework that facilitates estimation of and uncertainty quantification of sub-resolution reservoir properties from seismic data. Our workflow exploits learning the direct relation between seismic data and reservoir properties by the evidential learning approach to efficiently estimate sub-resolution properties. The major focus of this paper is on a real case study of seismic characterization of reservoir layers significantly below the seismic resolution. We employ deep neural networks as summary statistics within Approximate Bayesian Computation to estimate posterior uncertainty of reservoir net-to-gross from 3D pre-stack seismic data without an explicit inversion.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902209
2019-09-02
2024-04-23
Loading full text...

Full text loading...

References

  1. Blum, M. G. B., 2010, Approximate Bayesian computation: A nonparametric perspective: J. Amer. Statist. Assoc.1051178–1187. MR2752613
    [Google Scholar]
  2. Dejtrakulwong, P., 2012, Rock physics and seismic signatures of sub-resolution sand-shale systems: Ph.D. Thesis, Stanford University
    [Google Scholar]
  3. DeutschC., and JournelA. G., 1992, GSLIB: geostatistical software library and user's guide. Oxford University Press, London
    [Google Scholar]
  4. Fearnhead, P. and PrangleD., 2012, Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(3), 419–474
    [Google Scholar]
  5. Marion, D., Mukerji, T., and Mavko, G., 1994, Scale effects on velocity dispersion: From ray to effective medium theories in stratified media: Geophysics, 59(10), 1613–1619.
    [Google Scholar]
  6. Pradhan, A. and MukerjiT., 2018, Seismic estimation of reservoir properties with Bayesian evidential analysis: SEG Technical Program Expanded Abstracts, 3166–3170
    [Google Scholar]
  7. Scheidt, C., LiL., and CaersJ., 2017, Quantifying Uncertainty in Subsurface Systems. Wichester, UK: Wiley-Blackwell
    [Google Scholar]
  8. Takahashi, I., 2000, Quantifying Information and Uncertainty of Rock Property Estimation from Seismic Data, Ph.D. Thesis, Stanford University.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902209
Loading
/content/papers/10.3997/2214-4609.201902209
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