Rendering traditional modeling methods are inadequate for fluvial reservoir characterization due to the reservoir's rapid lateral variations and strong heterogeneity.To address this,we initially employ steerable pyra-mid technology to decompose and reconstruct seismic data,enhancing the geological laws crucial for sedimen-tary facies characterization.Subsequently,we apply Bayesian-sequential Gaussian seismic-constraint modeling to the reservoir,establishing constraints between logging data and seismic attributes to enhance the model's ver-tical resolution.Modeling practices in the fluvial reservoir of the Sha-2 member in Shengli Oilfield demonstrate that,after the steerable pyramid process,seismic data,lateral characterization improves,with effective sand body boundary identification.Compared to traditional Sequential Gaussian methods,the Bayesian-sequential Gaussian approach achieves higher vertical resolution,with an 86％match between sand body thickness and the actual value,making it more effective for sand body identification.This method provides guidance for remai-ning oil recovery.
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