Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 1 — Methodology

@article{Ulvmoen2010ImprovedRI,
  title={Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 1 — Methodology},
  author={Marit Ulvmoen and Henning Omre},
  journal={Geophysics},
  year={2010},
  volume={75}
}
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 a Bayesian setting where the complete solution is the posterior model. The prior model for the lithology/fluid (LF) characteristics is defined as a profile Markov random-field model with lateral continuity. Each vertical profile is further given as an inhomogeneous Markov-chain model upward through the… Expand

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References

SHOWING 1-10 OF 22 REFERENCES
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
Bayesian lithology/fluid inversion—comparison of two algorithms
TLDR
Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated and it is concluded that the approximate likelihood model preserves 50% to 90% of the information content in the exact likelihood model. Expand
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
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
Bayesian wavelet estimation from seismic and well data
A Bayesian method for wavelet estimation from seismic and well data is developed. The method works both on stacked data and on prestack data in form of angle gathers. The seismic forward model isExpand
Rapid spatially coupled AVO inversion in the Fourier domain
Spatial coupling of the model parameters in an inversion problem provides lateral consistency and robust solutions. We have defined the inversion problem in a Bayesian framework, where the solutionExpand
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
Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir
Hydrocarbon reservoirs are characterized by seismic, welllog, and petrophysical information, which is dissimilar in spatial distribution, scale, and relationship to reservoir properties. We combineExpand
...
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2
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