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X潜山变质岩岩性识别方法研究

Study on lithology identification method of buried hill X metamorphic rock
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摘要 辽河凹陷滩海区X潜山历经多期次构造改造作用,地层厚度与地层产状稳定性差,岩相岩性复杂多样,岩性识别困难。X潜山太古界地层岩性为一套变质岩体系,主要发育有混合花岗岩、混合片麻岩、片麻岩和角闪岩。为了对变质岩类岩性进行准确识别,在对变质岩测井响应机理分析,优选并构造出变质岩敏感测井响应参数的基础上,采用图版法、决策树(Decision Tree,DT)模型及支持向量机(A Library for Support Vector Machine,LIBSVM)模型三种岩性识别方法对研究区单井进行了岩性识别。经与钻井取心和录井分析得出的实际岩性对比结果表明,决策树模型与支持向量机模型识别符合率均高于图版法,为该区变质岩岩性准确识别提供了可行的方法。 Buried hill X in the intertidal area of Liaohe Depression has undergone multiple periods of structural transformation.The stratigraphic thickness and occurrence stability are poor,and the lithofacies and lithology are complex and diverse,so it is difficult to identify the lithology.The Archaeozoic lithology is a set of metamorphic rock system,mainly developed with mixed granite,mixed gneiss,gneiss and amphibolite.In order to accurately distinguish the lithology of metamorphic rocks,on the basis of analyzing the logging response mechanism of metamorphic rocks and optimizing and constructing the sensitive logging response parameters of metamorphic rocks,three kinds of chart methods,decision tree model and support vector machine model are used.The lithology identification method has carried out the lithology discrimination of the single well in the study area,and the actual lithology comparison results from the drilling coring and logging analysis show that the discriminant coincidence rate of the decision tree model and the support vector machine model is higher than that of the chart method.It provides a feasible method for accurately identifying the lithology of metamorphic rocks in this area.
作者 孙钦帅 宋延杰 唐晓敏 SUN Qinshuai;SONG Yanjie;TANG Xiaomin(Well-Tech Department of China Oilfield Services Limited,Langfang,Hebei,065201,China;School of Earth Sciences,Northeast Petroleum University,Daqing,Heilongjiang,163000,China)
出处 《天然气与石油》 2021年第6期89-94,共6页 Natural Gas and Oil
基金 国家自然科学基金项目“骨架导电低阻油层人造岩样实验及导电规律与导电模型研究”(41274110)。
关键词 岩性识别 变质岩 决策树 支持向量机 Lithology identification Metamorphic rock Decision Tree A Library for Support Vector Machine
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