摘要：One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.

关键字：Reservoirs; Bayes methods; Rocks; Permeability; Dispersion; Attenuation; Data models; Bayesian inversion; Gas saturation; Markov chain Monte Carlo; rock physics; velocity dispersion

ISSN号：1545598X

卷、期、页：卷: 19

发表日期：2021-01-18

影响因子：3.966000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,地学领域高质量科技期刊分级目录（国外T1）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE Geoscience and Remote Sensing Letters

通讯作者：何港

第一作者：贺艳晓,袁三一,赵建国,王尚旭

论文类型：期刊论文

论文概要：贺艳晓,何港,袁三一,赵建国,王尚旭，Bayesian Frequency-Dependent AVO Inversion Using an Improved Markov Chain Monte Carlo Method for Quantitative Gas Saturation Prediction in a Thin Layer，IEEE Geoscience and Remote Sensing Letters，2021，卷: 19

论文题目：Bayesian Frequency-Dependent AVO Inversion Using an Improved Markov Chain Monte Carlo Method for Quantitative Gas Saturation Prediction in a Thin Layer