Bayesian lithology/fluid inversion—comparison of two algorithms

@article{Ulvmoen2010BayesianLI,
  title={Bayesian lithology/fluid inversion—comparison of two algorithms},
  author={Marit Ulvmoen and Hugo Hammer},
  journal={Computational Geosciences},
  year={2010},
  volume={14},
  pages={357-367}
}
Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated. The inversion is defined in a Bayesian setting where the prior model for the lithology-fluid classes is a Markov chain, and the likelihood model relates seismic data and elastic material properties to these classes. The likelihood model is approximated such that the posterior model can be calculated recursively using the extremely efficient forward–backward algorithm. The… Expand
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References

SHOWING 1-10 OF 13 REFERENCES
Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 1 — Methodology
The focus of our study is lithology/fluid inversion with spatial coupling from prestack seismic amplitude variation with offset (AVO) data and well observations. The inversion is defined in aExpand
Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model
A technique for lithology/fluid (LF) prediction and simulation from prestack seismic data is developed in a Bayesian framework. The objective is to determine the LF classes along 1D profiles throughExpand
Bayesian lithology and fluid prediction from seismic prestack data
A fast Bayesian inversion method for 3D lithology and fluid prediction from prestack seismic data, and a corresponding feasibility analysis were developed and tested on a real data set. The objectiveExpand
Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 2 — Real case study
We have performed lithology/fluid inversion based on prestack seismic data and well observations from a gas reservoir offshore Norway. The prior profile Markov random field model captures horizontalExpand
Stochastic reservoir characterization using prestack seismic data
Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in thisExpand
Seismic inversion combining rock physics and multiple-point geostatistics
A novel inversion technique combines rock physics and multiple-point geostatistics. The technique is based on the formulation of the inverse problem as an inference problem and incorporatesExpand
Spatially Coupled Lithology/Fluid Inversion from Real Seismic Data and Well Observations
Lithology/fluid inversion based on prestack seismic data and well observations from a gas reservoir offshore Norway is made in a Bayesian setting. The prior profile Markov random field model capturesExpand
Bayesian linearized AVO inversion
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions forExpand
Quantitative Seismology: Theory and Methods
TLDR
This work has here attempted to give a unified treatment of those methods of seismology that are currently used in interpreting actual data and develops the theory of seismic-wave propagation in realistic Earth models. Expand
Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk
Preface 1. Introduction to rock physics 2. Rock physics interpretation of texture, lithology and compaction 3. Statistical rock physics: combining rock physics, information theory, and statistics toExpand
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