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

• Autorzy: Mao Xinjun , Han Xuehui , Wu Baohai , Wang Zhenlin , Zhang Hao , Wang Hongliang

Identyfikatory

DOI

10.1007/s11600-021-00720-4

• Języki publikacji: EN

Abstrakty

EN

The low-frequency component of seismic data is an inevitable part to obtain absolute P-impedance (I_p) and V_p∕V_s 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 I_p and V_p∕V_s inverted for the clastic formation of the Sichuan basin show a strong correlation with the corresponding I_p and V_p∕V_s logs.

Słowa kluczowe

EN

AVO inversion; deep feedforward neural network; P-Impedance; Vp∕Vs Ratio

PL

inwersja AVO

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 2
• Strony: 563--580
• Opis fizyczny: Bibliogr. 32 poz.

Twórcy

autor

Mao Xinjun

• Exploration department of Xinjiang Oilfeld Company, PetroChina, Karamay 834000, China

autor

Han Xuehui

• School of Geoscience, China University of Petroleum (East China), Qingdao 266580, China

autor

Wu Baohai

• CGG, Beijing 100016, China

autor

Wang Zhenlin

• Research Institute of Petroleum Exploration and Development, Xinjiang Oilfeld Company, PetroChina, Karamay 834000, China

autor

Zhang Hao

• Exploration department of Xinjiang Oilfeld Company, PetroChina, Karamay 834000, China

autor

Wang Hongliang

• CNPC West Drilling Engineering Company Limited, Ürümqi 830011, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).