Geofluid identification is critical to reservoir forecast. Many fluid identifications mean released in recent years use conventional linear pre-stack inversions to obtain the majority of the fluid indicators, resulting in low fluid identification accuracy. Another disadvantage is that fluid indicators always incorporate mixed factors such as fluid parameters and porosity, which means that fluid indicators are no longer only influenced by fluid parameters. Therefore, considering that the fluid bulk modulus of can represent the basic properties of pore fluid, the interference caused by the mixing effect of porosity can be significantly reduced, which enhanced the reservoir fluid’s identification accuracy, we construct the nonlinear elastic impedance equation of the pore fluid bulk modulus using the exact Zoeppritz equation. Then, based on the non-stationary convolution model in the frequency domain, a nonstationary pre-stack nonlinear direct inversion approach is presented, which can significantly increase fluid identification resolution and reservoir forecast accuracy. The precision analysis and the field data examples verify the effectiveness of the proposed method.

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