Abstract：Many literatures prove that sensitive identification factors are successfully used to predict reservoir lithology,fluids,and rock brittleness.However,very few researches focus on the establishment of sensitive identification factors of reservoir and its direct extraction methods.We demonstrate this kind of research in shallow-sandstone reservoirs in Huangjue area.Firstly,a sensitive identification factor in sand reservoirs is designed for SF4 based on high P-wave velocity (vP),high S-wave velocity (vS),and low density (ρ),which effectively avoids numerical overlaps of conventional sand and mud identification factors (velocity multiplied by density).Then the AVO approximation containing the sensitive sand identification factor is derived which decreases accumulative errors caused by indirect calculations.Finally,the sensitive sand identification factor is directly extracted using the Bayesian-based elastic parameter inversion and the low frequency component of the inversion can be compensated by introducing the soft low-frequency constraint.Numerical tests and real data examples prove the validity and practicability of the proposed method.
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