RESEARCH ARTICLE


Data Warehouse Design and Optimization for Drilling Engineering



Ning Jing*, 1, Honghai Fan1, Yinghu Zhai1, Tianyu Liu2
1 China University of Petroleum, Fuxue Road 18, Changping District, Beijing, China;
2 Research Institute of Petroleum Exploration and Development, CNPC, Beijing, China


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© 2012 Jing et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Correspondence: * Address corresspondence to this author at the China University of Petroleum, Fuxue Road 18, Changping District, Beijing, China; Tel: (86)13488704284; Fax: (86)01089733221; E-mails: jingning1222@hotmail.com; jingning1017@126.com


Abstract

With the development of petroleum informatization and increase of drilling data, data storage, analysis and integration is attached to the key planning process. Therefore, a solution is needed which combines the data integration, management, analysis and decision support. Data warehouse is one of the hot topics in computer technology application, which has solved the problem of data using after the application of information system. This paper puts forward the data warehouse design proposal in drilling engineering. The data warehouse project with drilling engineering is narrated, such as system structure, the design realization, the data demonstration and security policy. The authors also present a method of drilling data integration based on ontology. The data warehouse system which has the well drilling project specialized domain characteristics is developed by using data warehouse and communication technology. This system can provide effective decision support analysis for decision-makers in different levels and departments.

Keywords: Drilling Engineering, Data Warehouse, Ontology.