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科技导报
| 研究论文 2011, 29(11-03): 29-33
基于Q型聚类分析和贝叶斯判别算法研究储层分类评价
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付殿敬1,徐敬领2,王贵文2
作者信息
1. 中国地质大学(北京)能源学院,北京 1000832. 中国石油大学(北京)油气资源与探测国家重点实验室,北京 102249
通讯作者:
付殿敬
Reservoir Classification and Evaluation Based on Q Cluster Analysis Combined with Bayesian Discrimination Algorithm
Affiliations
出版时间: 2011-01-28
doi: 10.3981/j.issn.1000-7857.2011.03.003
文章导航
储层分类评价是油藏研究的重要内容,为了使储层分类更准确、合理,本文采用Q型聚类分析和贝叶斯判别算法相结合的方法,进行储层分类评价研究。综合优选砂岩厚度、孔隙度、渗透率、碳酸盐含量及泥质含量等各种储层参数,应用数理统计方法将这些参数集合起来,采用Q型聚类分析的算法对储层进行分类;在此基础上,采用贝叶斯判别算法,建立这些储层参数与储层分类评价的定量判别关系,即建立进行储层分类评价的判别函数,依据此判别函数对非取心井的目的层进行了定量分类评价。实例证明,应用Q型聚类分析和贝叶斯判别算法相结合进行储层分类评价是有效的,其应用效果良好。
储层分类评价
/
储层参数
/
Q型聚类分析
/
贝叶斯判别
The reservoir classification and evaluation is an important topic of study, and in order to make reservoir classification more accurate and reasonable, in this paper, the method of Q cluster analysis combined with Bayesian discrimination algorithm is adopted. First, various reservoir parameters are optimized globally, including sandstone thickness, porosity, permeability, carbonate content, shale content and others, with the mathematical statistics method to assemble the parameters, and the algorithm of Q cluster analysis to do the reservoir classification. On this basis, Bayesian discrimination algorithm is adopted to establish the discriminant relationship between reservoir parameters and reservoir classification and evaluation, together with the discriminant function of reservoir classification and evaluation, based on which, the target beds of non-coring wells are classified and evaluated. Examples show that the method of Q cluster analysis combined with Bayes discrimination algorithm is effective to achieve reservoir classification and evaluation.
reservoir classification and evaluation
/
reservoir parameter
/
Q cluster analysis
/
Bayesian discrimination algorithm
付殿敬;徐敬领;王贵文.
基于Q型聚类分析和贝叶斯判别算法研究储层分类评价.
科技导报,
2011
, 29
(11-03)
: 29
-33
.
DOI: 10.3981/j.issn.1000-7857.2011.03.003
.
Reservoir Classification and Evaluation Based on Q Cluster Analysis Combined with Bayesian Discrimination Algorithm[J].
Science & Technology Review ,
2011
, 29
(11-03)
: 29
-33
.
DOI: 10.3981/j.issn.1000-7857.2011.03.003
2011年第29卷第11-03期
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doi: 10.3981/j.issn.1000-7857.2011.03.003
接收时间:2010-10-21
首发时间:2011-01-28
出版时间:2011-01-28
收稿日期:2010-10-21
修回日期:2010-12-05
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2011.03.003
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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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