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Title

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

Year: 1394
COI: JR_IJMGE-49-1_012
Language: EnglishView: 257
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Authors

Moslem Moradi - Simulation and Data Processing Laboratory, Mining Engineering Department, University of Tehran, Iran
Omid Asghari - Simulation and Data Processing Laboratory, Mining Engineering Department, University of Tehran, Iran
Gholamhossein Norouzi - Simulation and Data Processing Laboratory, Mining Engineering Department, University of Tehran, Iran
Mohammad Ali Riahi - Institute of Geophysics, University of Tehran, Iran

Abstract:

Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior information in Bayesian statistics. Data integration leads to a probability density function (named as a posteriori probability) that can yield a model of subsurface. The Markov Chain Monte Carlo (MCMC) method is used to sample the posterior probability distribution, and the subsurface model characteristics can be extracted by analyzing a set of the samples. In this study, the theory of stochastic seismic inversion in a Bayesian framework was described and applied to infer P-impedance and porosity models. The comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more detailed information of subsurface character. Since multiple realizations are extracted by this method, an estimation of pore volume and uncertainty in the estimation were analyzed.

Keywords:

Bayesian theory, geostatistics, stochastic seismic inversion, uncertainty

Paper COI Code

This Paper COI Code is JR_IJMGE-49-1_012. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/488128/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Moradi, Moslem and Asghari, Omid and Norouzi, Gholamhossein and Riahi, Mohammad Ali,1394,Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis,https://civilica.com/doc/488128

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Type of center: دانشگاه دولتی
Paper count: 64,003
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