Abstract：Pre-stack seismic inversion is an important method to obtain spatial distribution characteristics of several elastic parameters and identify sweet spots in tight sandstone.As sweet spots of tight sandstone are all located in the inner sandstone, and sand bodies and sweet spots often have different sensitive parameters, inversion parameters usually need to be converted, which reduces the inversion accuracy and efficiency.Thus, this paper proposes a direct prediction method of sweet spots in tight sandstone based on Bayesian probability inversion.Firstly, the Zeoppritz approximation equation of lithology and sensitive elastic parameters of sweet spots is derived, and then the convolution model is combined to construct the relationship between sensitive parameters and seismic records.Secondly, under the framework of Bayesian theory, the posterior probability distribution expression of sensitive parameters is established, and the Markov chain-Monte Carlo method is adopted to sample the posterior probability distribution, with the correlation between inversion parameters during the sampling being fully considered.Finally, the improved Bayesian probability inversion method is employed to directly predict the sweet spots in tight sandstone.The proposed method introduces a sampling method based on conditional probability distribution to constrain the sampling space of inversion parameters and improve prediction efficiency and accuracy.The feasibility of the method is verified by the model data and actual data.
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ZHAO Chen, JIN Fengming, HAN Guomeng, GUO Shuwen, XING Xing, LIU Hongzhou. Direct prediction of sweet spots in sandstone reservoirs based on pre-stack probability inversion. Oil Geophysical Prospecting, 2023, 58(5): 1211-1219,1230.
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