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2. Geophysics and Oil Resource Institute, Yangtze University, Wuhan 430100, China;
3. Jilin Division, Daqing Well-logging Company, Songyuan 138000, China, fund=null, authors=ZHANG Chong1,2 , ZHANG Zhansong1,2 , CHEN Yulong1,2 , XING Yanjuan3 , LI Chaowei3 , authorsList=ZHANG Chong, ZHANG Zhansong, CHEN Yulong, XING Yanjuan, LI Chaowei), CN=ArticleExt(id=1242132471010108398, articleId=1242132466580923354, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=致密砂砾岩岩性测井识别方法及应用, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=以王府断陷小城子地区登娄库组和营城组为例,研究了致密砂砾岩岩性测井识别方法及其应用。利用录井岩性资料总结登娄库组、营城组各种岩性的组分,并提取2 个层组的主要岩性,登娄库组主要岩性为砂砾岩、砂岩和泥岩,营城组主要岩性为砾岩、砂砾岩和泥岩;分析不同岩性的测井响应特征,并提取2 个层组的岩性敏感参数,登娄库组为自然伽马、中子、声波时差和电阻率,营城组为自然伽马、中子和密度。利用SPSS 软件建立登娄库组和营城组的岩性Fisher 判别模型,将该模型应用于未参与建模的5 口井储层岩性的识别,识别准确率达86%,证明了该方法的可靠性。, correspAuthors=null, authorNote=张冲,副教授,研究方向为复杂储层测井评价理论与方法,电子信箱:yzlogging@163.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=bAXiD7SgC4JnJ0q077vuUg==, pdfFileSize=3243293, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 长江大学油气资源与勘探技术教育部重点实验室, 武汉 430100;
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科技导报
| 研究论文 2014, 32(25): 59-62
致密砂砾岩岩性测井识别方法及应用
全屏
张冲1,2 , 张占松1,2 , 陈雨龙1,2 , 邢艳娟3 , 李超炜3
作者信息
1. 长江大学油气资源与勘探技术教育部重点实验室, 武汉 430100;
2. 长江大学地球物理与石油资源学院, 武汉 430100;
3. 大庆钻探工程公司测井公司吉林事业部, 松原 138000
Logging Lithology Identification of Tight Sandy Conglomerate and Its Application
Affiliations
出版时间: 2014-09-08
doi: 10.3981/j.issn.1000-7857.2014.25.010
文章导航
以王府断陷小城子地区登娄库组和营城组为例,研究了致密砂砾岩岩性测井识别方法及其应用。利用录井岩性资料总结登娄库组、营城组各种岩性的组分,并提取2 个层组的主要岩性,登娄库组主要岩性为砂砾岩、砂岩和泥岩,营城组主要岩性为砾岩、砂砾岩和泥岩;分析不同岩性的测井响应特征,并提取2 个层组的岩性敏感参数,登娄库组为自然伽马、中子、声波时差和电阻率,营城组为自然伽马、中子和密度。利用SPSS 软件建立登娄库组和营城组的岩性Fisher 判别模型,将该模型应用于未参与建模的5 口井储层岩性的识别,识别准确率达86%,证明了该方法的可靠性。
Taking the sandy conglomerate reservoirs of Denglouku and Yingcheng Formations in Xiaochengzi area, Wangfu Fault as an example, the logging lithology identification is studied in this paper. Based on the logging lithology data, various lithologic compositions of objective intervals are analyzed and the main lithology components of Denglouku and Yingcheng Formations are extracted firstly. The main components of Denglouku Formation are the sandy conglomerate, the sandstone and the shale and those of Yingcheng Formation are the conglomerate, the sandy conglomerate and the shale. Then the logging response characteristics are analyzed, the lithologic parameters of these two formations are extracted and it is shown that the sensitive parameters of Denglouku Formation are the natural gamma ray (GR ), the neutron (CNL ),the interval transit time (AC ) and the resistivity (RT ) and that the parameters of Yingcheng Formation are the GR , the density (DEN ) and the CNL . Finally, the Fisher model of the lithology identification is established by using the SPSS software, and the application of the model to the reservoir lithology identification of five wells not included in the process of building the model shows that the discriminant accuracy rate reaches 86%, which indicates a high reliability of the method.
sandy conglomerate
/
lithology identification
/
Fisher discriminant
张冲, 张占松, 陈雨龙, 邢艳娟, 李超炜.
致密砂砾岩岩性测井识别方法及应用.
科技导报,
2014
, 32
(25)
: 59
-62
.
DOI: 10.3981/j.issn.1000-7857.2014.25.010
ZHANG Chong, ZHANG Zhansong, CHEN Yulong, XING Yanjuan, LI Chaowei.
Logging Lithology Identification of Tight Sandy Conglomerate and Its Application[J].
Science & Technology Review ,
2014
, 32
(25)
: 59
-62
.
DOI: 10.3981/j.issn.1000-7857.2014.25.010
2014年第32卷第25期
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文章信息
doi: 10.3981/j.issn.1000-7857.2014.25.010
接收时间:2014-02-21
首发时间:2014-09-16
出版时间:2014-09-08
收稿日期:2014-02-21
修回日期:2014-06-26
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2014.25.010
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2种不同金属材料的力学参数
科 Family 属数 Number of genus 种数 Number of species 占总种数比例 Percentage of total species (%) 属 Genus 种数 Number of species 占总种数比例 Percentage of total species (%) 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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