In petrophysical-properties inversion, elastic parameters underground can be obtained by seismic inversion. Petrophysical-properties determine elastic properties and this relation can be depicted by rock-physics model. Since petrophysical-properties estimation is always associated with uncertainties, stochastic inversion plays an important role in petrophysical-properties estimation. The stochastic sampling method provides us a tool for predicting rock and fluid properties from probability distributions function (PDF) of petrophysical-parameters inverted based on rock-physics model. In this paper, we combine geostatistics and a new statistical rock-physics inversion method under Bayesian framework to compute the PDF of petrophysical properties. Then, a Markov-Chain-Monte-Carlo (MCMC) algorithm is used to sample posterior PDF to get multiple realizations thus uncertainty of both petrophysical-properties and elastic parameters inverted can be evaluated. The synthetic data example and real application to seismic data near ODP1144 demonstrated the effectiveness of presented method.

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