[1]苗 涛,王凤琴,高利军,等.基于数据挖掘技术的碎屑岩岩性识别方法及应用[J].复杂油气藏,2021,14(01):39-44.[doi:10.16181/j.cnki.fzyqc.2021.01.008]
 MIAO Tao,WANG Fengqin,GAO Lijun,et al.Lithology identification method for clastic rock based on data mining technology and its application[J].Complex Hydrocarbon Reservoirs,2021,14(01):39-44.[doi:10.16181/j.cnki.fzyqc.2021.01.008]
点击复制

基于数据挖掘技术的碎屑岩岩性识别方法及应用()
分享到:

《复杂油气藏》[ISSN:1674-4667/CN:31-2019/TQ]

卷:
14卷
期数:
2021年01期
页码:
39-44
栏目:
油气开发
出版日期:
2021-03-25

文章信息/Info

Title:
Lithology identification method for clastic rock based on data mining technology and its application
作者:
苗 涛123王凤琴12高利军3乔林胜3段涛涛3
1.西安石油大学,陕西 西安 700065;2.全国石油石化行业致密油气地质重点实验室,陕西 西安 700065;3.延长气田采气二厂,陕西 靖边 718500
Author(s):
MIAO Tao123WANG Fengqin12GAO Lijun3QIAO Linsheng3DUAN Taotao3
1.Xi’an Shiyou University,Xi’an 700065,China;2.National Key Laboratory of Tight Oil and Gas Geology in the Petroleum and Petrochemical Industry,Xi’an 700065,China; 3.Second Gas Production Plant of Yanchang Gas Field,Jingbian 718500,China
关键词:
碎屑岩测井数据交会图数据挖掘岩性识别
Keywords:
clastic rocklogging dataintersection diagramdata mininglithology identification
分类号:
TE321
DOI:
10.16181/j.cnki.fzyqc.2021.01.008
文献标志码:
A
摘要:
克拉玛依油田某井区八道湾组油藏储层岩性复杂,非均质性强,如何对该区岩性进行准确识别成为该区域油气资源再开发的关键问题之一。通过岩心观察、薄片鉴定等资料,利用测井交会图技术基于常规测井曲线建立了克拉玛依油田某井区八道湾组油藏岩性识别图版。由于该图版中岩性存在重叠区域,导致对该区的主要含油岩性识别不准确。为了提高岩性识别的准确性,利用数据挖掘方法计算出各测井参数在岩性识别模型中的权重大小,确定各参数对岩性变化的敏感性,优选出原状地层电阻率、中子孔隙和声波时差等敏感参数并建立RT-lg(CNL×AC)岩性识别图版,有效提升了岩性识别精度。
Abstract:
The reservoir of Badaowan Formation in a certain well area of Karamay Oilfield has complex lithology and strong heterogeneity. How to accurately identify the lithology in this area has become one of the key issues in the redevelopment of oil and gas resources in this area. Based on core observation,thin slice identification and other data,the lithology identification map of the Badaowan Formation reservoir in a certain well area of Karamay Oilfield was established by using the logging cross-graph technology based on conventional logging curves. Due to the overlapping areas of lithology in this map,the identification of the main oil-bearing lithology in this area is not accurate. In order to improve the accuracy of lithology identification,data mining method is used to calculate the weight of each logging parameter in the lithology identification model,and determine the sensitivity of each parameter to lithology changes. The lithology identification plate with strong sensitivity parameters is then established and analyzed,to select out sensitive parameters such as undisturbed formation resistivity,neutron porosity and acoustic time difference,and so on,and establish the RT-lg(CNL×AC) lithology identification plate,which effectively improves the accuracy of lithology identification.

参考文献/References:

[1]胡文瑞.中国石油二次开发技术综述[J].特种油气藏,2007,14(16):1-4,16.
[2]徐恒.克拉玛依油田八区530井区砾岩油藏特征及调整对策研究[D].成都:西南石油大学:1-15.
[3]王宏波,姚军,李双文,等.利用对应分析法校正火成岩岩性识别图版——以黄骅凹陷为例[J].天然气地质学.2013.24(4):719-724.
[4]韩琳,潘保芝.应用ECS测井资料丰富岩性识别图版信息[J].吉林大学学报(地球科学版).2008.38(增刊):110-112.
[5]王国勋.基于多目标决策的数据挖掘模型选择研究[D].成都:电子科技大学:10-20.
[6]李旭.五种决策树算法的比较研究[D].大连:大连理工大学,2011:1-20.
[7]BREIMANL,FRIEDMANJH,OLSHEN,etal.Cla-ssificationandregressiontrees[M].California:Wa-dsworthInternationalGroup,1984:1-2.
[8]ADAMA,SHAPIAIMI,IBRAHIMZ,etal.Artificialneuralnetwork—Naivebayesfusionforsolvingclassi-ficationproblemofimbalanceddataset[C]//2011FourthInternationalConferenceonModeling,SimulationandAppliedOptimization,KualaLumpur,Malaysia:IEEE,2011:1-5.
[9]DOMINGOSP,PAZZANIM.OntheoptimalityofthesimpleBayesianclassifierunderzero-oneloss[J].MachineLeaming,1997,29(2):103-130.
[10]ADAMA,SHAPIAIMI,IBRAHIMZ,etal.Artificialneuralnetwork—Naivebayesfusionforsolvingclass-ificationproblemofimbalanceddataset[C]//2011FourthInternationalConferenceonModeling,Sim-ulationandAppliedOptimization,KualaLumpur,Malaysia:IEEE,2011:1-5.

相似文献/References:

[1]张杰,曲占庆,刘少军,等.碎屑岩油藏DL-1堵剂性能评价及应用[J].复杂油气藏,2012,(02):63.
[2]何星,吴文明,李亮,等.聚合物微球在高温高盐碎屑岩水平井堵水中的应用[J].复杂油气藏,2012,(04):82.

备注/Memo

备注/Memo:
收稿日期:2020-09-10 ;改回日期:2020-10-30 。
第一作者简介:苗涛(1995—),硕士在读,主要从事油气地质分析研究工作。 E-mail: 305311745@qq.com。
更新日期/Last Update: 2021-03-25