Model-data-driven seismic AVO inversion method for small sample data

LIU Jinshui, SUN Yuhang, LIU Yang

doi:10.11698/PED.20220119

Petroleum Exploration and Development >

0 20221014 - 20221014

DOI: https://doi.org/10.11698/PED.20220119

Model-data-driven seismic AVO inversion method for small sample data

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1. CNOOC Shanghai Branch, Shanghai 200335;
2. China University of Petroleum (Beijing), Beijing 102249;
3. Karamay campus of China University of Petroleum (Beijing), Xinjiang 834000

Received date: 2022-02-16

Revised date: 2022-07-15

Online published: 2022-08-10

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Abstract

As thin sandstone layers in thin interbedded section are difficult to identify, conventional model-driven seismic inversion and data-driven seismic methods have low precision in predicting them. To solve this problem, a model-data-driven seismic AVO (amplitude variation with offset) inversion method based on a space-variant objective function has been worked out. In this method, zero delay cross-correlation function and F norm are used to establish objective function; based on inverse distance weighting theory, change of the objective function is controlled according to the location of the target CDP (common depth point), to change the constraint weights of training samples, initial low-frequency models, and seismic data on the inversion. Hence, the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data, and is suitable for identifying thin interbedded sand bodies. Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data, and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick. Tests on the field data of Lishui Sag show that the inversion results of the proposed method have small relative error with well-log data, and can identify thin sandstone layers of about one-15th of the dominant wavelength thick with small sample data.

Key words： small sample data; space-variant objective function; model-data-driven; neural network; seismic AVO inversion; thin interbedded sandstone identification; Lishui Sag

Cite this article

LIU Jinshui, SUN Yuhang, LIU Yang . Model-data-driven seismic AVO inversion method for small sample data[J]. Petroleum Exploration and Development, 0 : 20221014 -20221014 . DOI: 10.11698/PED.20220119

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