基于地震波阻抗随机反演的岩性模型建立.

The sand bodies of the delta front reservoirs in the WT Sag have small scales and strong heterogeneity, which leads to the difficulty of lithology prediction and characterization for this type of reservoirs. According to core analysis and logging data, muddy sandstone reservoirs are widely developed in the target layer of the study area, but the difference in compressional wave impedance between muddy sandstone, fine sandstone, and mudstone is small, and it is not possible to effectively use compressional wave impedance to identify the three lithologies. Moreover, the well network in the study area is sparse, and it is not possible to effectively extract the variogram, which affects the effect of post-stack seismic stochastic inversion and further affects the prediction of reservoir lithology. To solve these complex problems, the compressional wave impedance curve was reconstructed by using wavelet reconstruction and information statistics weighting methods. Sensitivity analysis of seismic attributes to sand to ground ratio was conducted, and root mean square (RMS) attributes were applied to characterize the sand body and extract a variation function from it. Random inversion of post stack seismic data was performed using reconstructed curves combined with variation functions. The results show that the compressional wave impedance reconstructed by information statistics weighting has a good identification effect on lithology. The lithology model established by post-stack seismic stochastic inversion and Bayesian algorithm matches well with the validation well lithology. The planar distribution of sand ratio made by the lithology model is consistent with geological understanding. It can be seen that the compressional wave impedance stochastic inversion model established by using the above methods has high reliability, and has important exploration guidance significance for predicting the planar distribution of sand bodies and designing horizontal well trajectories. [ABSTRACT FROM AUTHOR]