期刊文献+

页岩气工程大数据仓库建设与管理系统开发 被引量:4

Development of big data warehouse construction and management system for shale gas engineering
下载PDF
导出
摘要 为深入挖掘和发现页岩气田勘探开发工程数据潜在价值,以涪陵页岩气田及中上扬子页岩气探区为主,利用大数据仓库理念,集成包括钻井、录井、测井、射孔、测试、特种作业、平台设计、产能动态、地质研究、综合研究10个方面的页岩气工程数据资料,按页岩气工程数据类别、专业、平台、工程项目,分级分类建立页岩气工程大数据仓库,并开发基于WEB技术的B/S架构的BDL-1000大数据仓库管理系统。页岩气工程大数据仓库的建设与应用表明:以涪陵为主的页岩气工程数据仓库管理系统各项功能运行稳定可靠,界面设计简洁清晰,通用性强,应用效果良好;大数据仓库分类与建设目标设计合理,管理方式灵活方便,为页岩气勘探开发工程的科研、生产及管理人员提供了丰富的数据信息。 In order to deeply explore and discover the potential value of engineering data for exploration and development of shale gas fields,taking Fuling shale gas field and middle-upper Yangtze shale gas exploration area as the main ones,using the idea of big data warehouse,integrating 10 aspects of shale gas engineering data of drilling,mud logging,well logging,perforating,testing,special operations,platform design,productivity performance,geologic research and comprehensive study,according to shale gas engineering data categories,professional,platform,engineering projects,the shale gas engineering big data warehouse was set up by hierarchical classification,and BDL-1000 big data warehouse management system based on the B/S architecture of WEB technology was developed.The construction and application of the big data warehouse of shale gas engineering indicated that the functions of the data warehouse management system of shale gas engineering mainly in Fuling were stable and reliable,the interface design is concise and clear,and high utilities and the application effect is good.The classification and the construction goals of the big data warehouse were reasonable,the management is flexible and convenient,and it can provide rich data information for scientific research,production and management personnel.
出处 《录井工程》 2017年第3期15-19,共5页 Mud Logging Engineering
基金 中石化石油工程技术服务股份有限公司科技开发项目"中深层页岩品质录井精细评价技术"(编号:SG15-21K) 中国石化集团油田先导项目"涪陵深层页岩地层快速导向技术先导应用"(编号:YTXD-1508) 国家重大科技专项大型油气田及煤层气开发(编号:2016ZX05038-006)
关键词 页岩气工程 大数据仓库 WEB技术 B/S架构 管理系统 shale gas engineering big data warehouse WEB technology B/S architecture management system
  • 相关文献

参考文献13

二级参考文献51

  • 1陶雪娇,胡晓峰,刘洋.大数据研究综述[J].系统仿真学报,2013,25(S1):142-146. 被引量:344
  • 2Paul J.Perrone 张志伟等(译).J2EE构造企业系统- 专家级解决方案[M].北京:清华大学出版社,2001..
  • 3"大数据背景下的国防科技情报研究"学术研讨会在漠河召开[EB/OL].[2012-11-20].http://d.wanfangdata.com.cn/Periodical_qbllysj201209031.aspx.
  • 4Manyika J, McKinsey Global Insti- tute, Chui M, et al. Big data: The next fron- tier for innovation, competition, and produc- tivity[M]. McKinsey Global Institute, 2011.
  • 5Hirt C W, Nichols B D. Volume of fluid(VOF) method for the dynamics of free boundaries[J]. Journal of computational physics, 1981,39(1): 201-225.
  • 6]Chirillo J, Blaul S. Storage Security:Protecting, SANs, NAS and I)AS[M].John Wiley & Sons, Inc., 2002.
  • 7Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters [J]. Communications of the ACM, 2008, 51 (1): 107-113.
  • 8百度百科.大数据[EB/OL].(2013--06--01).http://baike.baidu.com/view/6954399.htm.
  • 9城田真琴.大数据的冲击[M].北京:人民邮电出版社,2013.
  • 10周宝曜,刘伟,范承工.大数据:战略·技术·实践[M].北京:电子工业出版社,2013:306-307.

共引文献42

同被引文献39

引证文献4

二级引证文献26

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部