核主成分分析法在测井浊积岩岩性识别中的应用

doi:10.13810/j.cnki.issn.1000-7210.2019.03.021

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Abstract It is difficult to identify the lithology on well-logging data in the evaluation of complex lithologic reservoirs.Due to complexity of turbidite reservoirs in Dongji Sag, Dongying Depression, conventional cross-plot and principal component analysis methods fail to identify their lithology.In order to solve this problem, based on particle swarm optimization and kernel function theory and combining with log response characteristics of the area, an improved principal component analysis method is used to establish a new principal component calculation.Five principal component variables are constructed with measured natural gamma-ray logging (GR), acoustic logging (AC), compensated neutron porosity logging (CNL), density logging (DEN), and virgin zone resistivity (RT) of reservoirs.The accumulate contribution rate of the first two principal component variables reached 93.83%, which can effectively replace the original multi-dimensional logging information.The proposed method is tested in the study area.Based on our application results, this proposed method can effectively identify the lithology of turbidite reservoirs, and its identification rate reaches up to 90%.

Key words： turbidite principal component analysis kernel function lithology identification particle-swarm algorithm

Received: 08 October 2017

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P631

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	Articles by authors
	ZHOU You
	ZHANG Guangzhi
	GAO Gang
	ZHAO Wei
	YI Yuanping
	WEI Hongmei

Cite this article:

ZHOU You,ZHANG Guangzhi,GAO Gang等. Application of kernel principal component analysis in well logging turbidite lithology identification[J]. OGP, 2019, 54(3): 667-675.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2019.03.021 OR http://www.ogp-cn.com/EN/Y2019/V54/I3/667

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