Abstract
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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This research work is supported by the National Science and Technology Major Project (No. 2011 ZX05007-006), the 973 Program of China (No. 2013CB228604), and the Major Project of Petrochina (No. 2014B-0610).
Gui Jin-Yong, engineer, received his B.S. in Geophysics from the Jianghan Petroleum Institute in 2009 and his M.S. in Geophysics from China University of Petroleum (Huadong) in 2012. He is presently at the Research Institute of Petroleum Exploration & Development-Northwest Branch, Petrochina. His main research interests are prestack seismic inversion, reservoir prediction, and fluid identification.
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Gui, JY., Gao, JH., Yong, XS. et al. Reservoir parameter inversion based on weighted statistics. Appl. Geophys. 12, 523–532 (2015). https://doi.org/10.1007/s11770-015-0523-z
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DOI: https://doi.org/10.1007/s11770-015-0523-z