It is difficult to accurately predict the dolomite tight oil reservoir which has the characteristics of low porosity and low permeability by using the post-stack inversion,due to the small difference in acoustic impedance between the reservoir and its surrounding rock.Therefore,more abundant elastic information is needed.AVO inversion is an effective means to extract elastic information from pre-stack data.However,due to the noise and other factors,the pre-stack inversion equation has a strong ill-posed problem.Bayesian theory allows the construction of a regularization term by introducing a priori information about the model parameters,thereby effectively reducing the ill-posed problem of the inversion.Therefore,the modified Trivariate Cauchy constraint and the modified low-frequency constraint factor is introduced into the objective function,which can improve the ill-posed problem of the inversion,thus upgrading the accuracy of the inversion results.The iterative idea is used to address the non-linear nature of the proposed inverse operator.The P and S-wave velocity is updated in the iterations,which leads to more reliable results when applied to real data.Both the model data tests and the field data applications prove the validity and stability of the proposed method.Statistics show that,by using the proposed inversion method,the prediction accuracy rate of the reservoir thickness is as high as 89.5%.Therefore,this method has important reference significance for the exploration of similar siliceous reservoirs.
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