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Abstract

Geostatistical seismic inversion techniques are conventional tools for seismic reservoir characterization and able to integrate well and seismic reflection data while inferring highly variable petro-elastic models from both post- and pre-stack seismic reflection data. In this framework the model parameter space is perturbed by stochastic sequential simulation and co-simulation. While it allows assessing the spatial uncertainty for each property, the use of stochastic sequential simulation and co-simulation as the perturbation engine of the model parameter space assumes: a given spatial continuity pattern for each property of interest, as revealed by a variogram model; that the distribution of each property is known as interpreted from the existing well log-data; and that there are no uncertainties during seismic interpretation used for the regionalization of inversion area based on main seismic reflections and statistical properties. Here we show a brief overview of geostatistical seismic inversion methodologies able to invert seismic reflection data directly for subsurface elastic and rock properties discussing its advantages and disadvantages. Then, we point the road ahead and how these approached can be coupled with adaptive stochastic sampling to quantify uncertainty in geological parameters which are frequently assumed to be true.

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/content/papers/10.3997/2214-4609.201801928
2018-06-10
2020-08-04
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801928
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