Reservoir parameter inversion based on weighted statistics

Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose a...

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Bibliographic Details
Published inApplied geophysics Vol. 12; no. 4; pp. 523 - 532
Main Authors Gui, Jin-Yong, Gao, Jian-Hu, Yong, Xue-Shan, Li, Sheng-Jun, Liu, Bin-Yang, Zhao, Wan-Jin
Format Journal Article
LanguageEnglish
Published Beijing Chinese Geophysical Society 01.12.2015
Springer Nature B.V
Research Institute of Petroleum Exploration & Development-Northwest Branch,Petrochina,Lanzhou 730020,China
Subjects
Bayesian analysis
Computer simulation
Density
Earth and Environmental Science
Earth Sciences
Geophysics
Geophysics/Geodesy
Geostatistics
Geotechnical Engineering & Applied Earth Sciences
Inversions
Markov chains
Mathematical models
Monte Carlo simulation
Natural gas exploration
Objective function
Physical properties
Physics
Reservoirs
Rock
Rocks
Statistical methods
Uncertainty
stochastic simulation
inversion
weighted statistics
Reservoir parameters
Bayesian framework
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Summary:Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.
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ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-015-0523-z