沉积相智能地震识别技术研究及应用

doi:10.13810/j.cnki.issn.1000-7210.2023.03.005

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Abstract Generally, sedimentary facies types are described by seismic parameters such as geometry, lateral continuity, amplitude, frequency, and interval velocity. Limited by the interpreters' geological understanding of the target area, the traditional seismic identification method of sedimentary facies using manual interpretation has high work intensity, low efficiency, and multiple solutions. Only few labels can be made in many seismic survey regions, which cannot support the completion of strongly supervised learning. At this time, few-shot learning can be a good solution. In this work, the application effect of few-shot learning in sedimentary facies identification is discussed mainly through the research on the prediction me-thod of carbonate mound-shoal complexes. In terms of weakly supervised learning, a label library for seismic facies of typical seismic profiles is constructed according to seismic reflection configurations and drilling information. A total of 14 seismic profiles are interpreted as training labels in seismic data of Dengying Formation in the central Sichuan Basin, accounting for 2.8% of the total. Then, the intelligent identification method of sedimentary facies controlled by the sequence stratigraphic framework is employed. The implicit scalar field is constructed with the seismic sequence framework, and the spatial change information of seismic horizons is introduced, which avoids the lack of geological information in the prediction of seismic facies and improves the accuracy of intelligent prediction of sedimentary facies. The proposed method is used to depict the carbonate microbial mound-shoal complexes of Dengying Formation in the central Sichuan Basin. The results show that the organic reef is controlled by the platform margin slope, and the prediction results of reef boundaries are highly consistent with seismic information and conform to geologic laws. On the plane, the reefs are distributed in strips, which is consistent with platform margin distribution and sedimentary facies.

Key words： sedimentary facies deep learning label convolutional neural network few-shot learning sequencestratigraphic framework

Received: 24 April 2022

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P631

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	Articles by authors
	YANG Cun
	MENG He
	YE Yueming
	YONG Xueshan
	CHANG Dekuan

Cite this article:

YANG Cun,MENG He,YE Yueming等. Research and application of intelligent seismic identification technology of sedimentary facies[J]. OGP, 2023, 58(3): 528-539.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.03.005 OR http://www.ogp-cn.com/EN/Y2023/V58/I3/528

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