波形指示反演在煤层屏蔽薄砂岩分布预测中的应用

doi:10.11720/wtyht.2019.1325

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Abstract

It is difficult to predict the thin sand beneath the coal seam because of the influence of the strong shield.It is difficult for conventional wavelet decomposition techniques to decompose stable wavelet sets,and even the decomposed wavelet sets have no definite geological significance.In this paper,a new seismic meme inversion is proposed,which uses the similarity of seismic waveforms as an indicator factor to drive broadband logging curve simulation and realize high resolution inversion.Firstly,the dynamic clustering analysis of seismic waveform is realized by singular value decomposition,and the sample set of seismic waveform and well curve characteristics is established.Then the common structure in the sample is determined as the initial model in the wavelet domain,and the inversion results with high resolution are obtained by modifying the initial model according to the inversion of the actual seismic waveform structure under the constraint of the Bayesian framework.Through forward modeling and Jurassic case study in Junggar Basin,it is concluded that seismic meme inversion overcomes the limitation of seismic vertical resolution and can better predict thin sand affected by strong shield.Seismic meme inversion provides a new way for predicting thin sand affected by coal shield in Jurassic in Junggar Basin,Ordos Basin and Turpan-Hami Basin.It has a wide range of applications and important practical significance.

Key words： seismic meme inversion coal shield thin sand prediction phase-control inversion Bayes theory

Received: 20 November 2018 Published: 28 November 2019

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	Yan-Hu CHEN
	Jia CHEN

Cite this article:

Yan-Hu CHEN,Jia CHEN. The application of seismic meme inversion to thin sand distribution prediction under coal shield[J]. Geophysical and Geochemical Exploration, 2019, 43(6): 1254-1261.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2019.1325 OR https://www.wutanyuhuatan.com/EN/Y2019/V43/I6/1254

Schematic diagram of seismic meme inversion
a—sample classification based on seismic waveform;b—determine the common features of the sample set

Geologic model and Comparison of different inversion results
a—geologic model;b—synthetic trace;c—sparse pulse inversion;d—seismic meme inversion

Synthetic trace and Seismic meme inversion results with random noise
a—synthetic trace with 10% random noise;b—synthetic trace with 20% random noise;c—synthetic meme inversion result of figure a;d—synthetic meme inversion result of figure b

Pimpdance curves and synthetic trace of well A and B

Histogram of lithologies

The sand features of correlation profiles from wells A to E

Seismic profile and Comparison of different inversion results from well A to E
a—seismic profile;b—sparse pulse inversion result;c—seismic meme inversion result

Sand thickness map of inversion result

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