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.
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).
Cite this article:
Gui Jin-Yong,Gao Jian-Hu,Yong Xue-Shan et al. Reservoir parameter inversion based on weighted statistics[J]. APPLIED GEOPHYSICS, 2015, 12(4): 523-532.
[1]
Bachrach, R., 2006, Joint estimation of porosity and saturation using stochastic rock-physics modeling: Geophysics, 71(5), O53-O63.
[2]
Blangy, J. P., 1992, Integrated seismic lithologic interpretation: The petrophysical basis: Ph.D. thesis, Stanford University.
[3]
Doyen, P. M., 1988, Porosity from seismic data: A geostatistical approach: Geophysics, 53, 1263-1275.
[4]
Fan, J. J., and Liu, P., 2008, Research on Naive Bayesian Classifier’s independence assumption: Computer Engineering and Applications (in Chinese), 44, 131-141.
[5]
Fournier, F., 1989, Extraction of quantitative geologic information from seismic data with multidimensional statistical analysis: Part I, methodology, and Part II, a case study: 59th Annual International Meeting, SEG, Expanded Abstracts, 726-733.
[6]
Friedman, N., Geiger, D., and Goldszmidt, M., 1997, Bayesian Network Classifiers: Machine Learning, 29, 131-163.
[7]
Grana, D., and Rossa, E. D., 2010, Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion: Geophysics, 75(3), O21-O37.
[8]
Hastie, T., Tibshirani, R., and Friedman, J., 2002, The elements of statistical learning: Springer.
[9]
Marion, D., and Jizba, D., 1997, Acoustic properties of carbonate rocks: Use in quantitative interpretation of sonic and seismic measurements, in I. Palaz, and K. J. Marfurt, eds., Carbonate Seismology: Geophysical Developments, SEG, 75-93.
[10]
Mavko, G., Mukerji, T., and Dvorkin, J., 2003, The rock physics handbook: Cambridge University Press, UK, 150-151.
[11]
Kabir, N., Lavaud, B., and Chavent G., 2000, Estimation of the density contrast by AVO inversion beyond the linearized approximation: an indicator of gas saturation. 70th Annual International Meeting, SEG, Expanded Abstracts, 243-246.
[12]
Russell, B. H., Gray, D., and Hampson, D. P., 2011, Linearized AVO and poroelasticity: Geophysics, 76(3), C19-C29.
[13]
Spikes, K., Mukerji, T., Dvorkin, J., and Mavko, G., 2008, Probabilistic seismic inversion based on rock-physics models: Geophysics, 72(5), R87-R97.
[14]
Tarantola, A., 2005, Inverse problem theory and methods for model parameter estimation: Society for Industrial and Applied Mathematics Press, USA, 20.
[15]
Yin, X. Y., Sun, R. Y., Wang, B. L., and Zhang, G. Z., 2014, Simultaneous inversion of petrophysical parameters based on geostatistical a priori information: Applied Geophysics, 11(3), 311-320.
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