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1 Acta Geophysica

1 2022

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P-Impedance and Vp/Vs prediction based on AVO inversion scheme with deep feedforward neural network: a case study from tight sandstone reservoir

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Mao X. , Han X. , Wu B. , Wang Z. , Zhang H. , Wang H.

Acta Geophysica

2022

tom Vol. 70, no 2

563--580

The low-frequency component of seismic data is an inevitable part to obtain absolute P-impedance (Ip) and Vp∕Vs ratio of the subsurface, especially for the reservoir sweet spot. In this work, we train the deep feedforward neural network (DFNN) with band-pass seismic data and well log data to obtain favorable low-frequency components. Specifically, the Bayesian inference strategy is first applied to the pre-stack constrained sparse spike inversion process, obtaining an “initial” inverted band-pass parameters, which are subsequently used as input when applying the DFNN algorithm to predict low- and bandpass parameters. Moreover, the high linear correlation coefficient between the DFNN-based inversion results and the realistic well logging curves of the blind wells demonstrates that the DFNN-based inversion scheme exhibits strong robustness and good generalization ability. Ultimately, we apply the proposed DFNN-based inversion strategy to a tight sandstone reservoir located at the Sichuan basin field from onshore China. Both low- and band-pass Ip and Vp∕Vs inverted for the clastic formation of the Sichuan basin show a strong correlation with the corresponding Ip and Vp∕Vs logs.