1887

Abstract

Summary

Stochastic inversion of elastic impedance based on Metropolis sampling algorithm is a Monte Carlo based non-linear inversion method, which can effectively integrate the high frequency information of well logging data and have a higher resolution. This method is formulated in a Bayesian theory framework. Firstly, we can get the priori information through Fast Fourier Transform- Moving Average (FFT-MA) and Gradual Deformation Method (GDM). Then we apply Metropolis algorithm in order to obtain an exhaustive description of the posteriori probability density. FFT-MA is a kind of efficient simulation method. Combined with GDM, it can constantly modify the reservoir model and remain the spatial structure unchanged until it matches the observed seismic data. According to the numerical calculations, we can conclude that FFT-MA simulation can reduce the time consumption. Combined with GDM, the inversion result can be converged rapidly, and the final results match the model well and have a higher resolution. In addition, this method adopts two-step method to inverse elastic parameters, so it can improve computational efficiency.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20141632
2014-06-16
2024-03-28
Loading full text...

Full text loading...

References

  1. Kjønsberg, H. et al.
    [2010] Bayesian Monte Carlo method for seismic predrill prospect assessment. Geophysics, 75(2), O9–O19.
    [Google Scholar]
  2. Le Ravalec, et al.
    [2000] The FFT moving average (FFT-MA) generator: An efficient numerical method for generating and conditioning Gaussian simulations. Mathematical Geology, 32(6), 701–723.
    [Google Scholar]
  3. Hu, L.Y.
    [2000] Gradual deformation and iterative calibration of Gaussian-related stochastic models. Mathematical Geology, 32(1), 87–108.
    [Google Scholar]
  4. Oliver, D.S.
    [1995] Moving averages for Gaussian simulation in two and three dimensions. Mathematical Geology, 27(8), 939–960.
    [Google Scholar]
  5. Mosegaard, K. and Tarantola, A.
    [1995] Monte Carlo sampling of solutions to inverse problems. Journal of Geophysical Research, 100(B7), 12431–12.
    [Google Scholar]
  6. Gelman, A. et al.
    [1996] Efficient metropolis jumping hules. Bayesian statistics, 5, 599–608.
    [Google Scholar]
  7. Hu, L.Y. et al.
    [1999] Reducing uncertainties in production forecasts by constraining geological modeling to dynamic data. SPE Annual Technical Conference and Exhibition.
    [Google Scholar]
  8. Tarantola, A.
    [2005] Inverse problem theory and methods for model parameter estimation. Siam.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141632
Loading
/content/papers/10.3997/2214-4609.20141632
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