%0 Journal Article %T 沾化凹陷古近系低序级不整合定量识别及地质意义
Quantitative Identification of Paleogene Low-Grade Unconformity and Its Geological Significance in Zhanhua Sag %A 巴素玉 %A 金杰华 %A 时瑞坤 %A 刘升余 %J Advances in Geosciences %P 269-277 %@ 2163-3975 %D 2022 %I Hans Publishing %R 10.12677/AG.2022.123028 %X 不整合是认识陆相湖盆地层格架、分析盆地形成与演化的重要界面,且低序级不整合对于认识局部地层接触关系、构造活动具有重要影响。本文以沾化凹陷渤深8井测井数据为基础,首先采用主成分分析法将多个相互联系的测井曲线通过降维重构形成少数包含原始曲线85%以上信息的主成分曲线,然后运用最优分割法将主成分曲线进行组合、判别,定量识别低序级不整合面。最后通过将低序级不整合面识别结果与实际资料进行对比,证实了该方法对低序级不整合面识别的有效性。精确识别低序级不整合,对于寻找不整合遮挡油气藏具有重要的实践意义。
Unconformity is an important interface for understanding the stratigraphic framework and ana-lyzing the formation and evolution of lacustrine basins. And, low-grade unconformity plays an important role in understanding local stratigraphic contact relationship and tectonic activity. In this paper, the principal component analysis method is used to form a few principal component curves containing more than 85% of the original curve information by dimensionality reduction based on logging data of Boshen 8 well in Zhanhua Sag Firstly. And then, the principal component curves are combined and discriminated by the optimal partition algorithm, identifying the low- grade unconformities quantitatively. Finally, by comparing the identification results of low-grade unconformity with the actual data, the effectiveness of the method is verified. It is of great practical significance for finding unconformity reservoirs by identifying the low-grade unconformity accurately. %K 主成分分析,最优分割法,低序级不整合面,沾化凹陷,古近系
Principal Component Analysis %K Optimal Partition Algorithm %K Low-Grade Unconformity %K Zhanhua Sag %K Paleogene %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=49426