The Application of Decision Tree Method to the Identification of Formation Types in Tight Gas Reservoirs

Hai Ying Han; Zhi Zhang Wang; Li Chang Wang

doi:10.4028/www.scientific.net/AMM.170-173.969

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 170-173The Application of Decision Tree Method to the...

The Application of Decision Tree Method to the Identification of Formation Types in Tight Gas Reservoirs

Abstract:

Numerous test data accumulates in the process of gas reservoir exploration and development, so it is necessary to apply the data mining technology to this process. Influenced by the geologic factors such as structure, deposition and diagenesis, tight sandstone gas reservoir formation types are so diversified that traditional cross-plot analysis technique hardly identify the formation types. In this paper, the formation types of tight sandstone gas reservoir in Daniudi area are successfully identified using the decision tree algorithm of data mining based on hierarchical decomposition theory, facilitating the development of the gas reservoir.

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Periodical:

Applied Mechanics and Materials (Volumes 170-173)

Pages:

969-974

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.170-173.969

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Online since:

May 2012

Authors:

Hai Ying Han, Zhi Zhang Wang, Li Chang Wang

Keywords:

Data Mining (DM), Gas Formation Identification, Tight Gas Reservoir

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References

[1] Zhonghui Cao. Journal of Southwest Petroleum Institute. Vol. 27(5), p.17–21.(2005,In Chinese).

Google Scholar

[2] Jicheng Yan, Lichun Liu. World Well Logging Technology. Vol. 3, p.16–18. (2009, In Chinese).

Google Scholar

[3] Chunping Wu, Dongping Wu and Chaohua Yang. Natural Gas Industry. S1, p.90–93. (2001, In Chinese).

Google Scholar

[4] Jianwen Yan, Songyang Zhang and Guoqian Huang. Natural Gas Industry. Vol.28(2), p.67–69. (2008, In Chinese).

Google Scholar

[5] Yongjie Li, Jianwei Li. Natural Gas Exploration and Development. Vol. 32(3), p.12–14. (2009, In Chinese).

Google Scholar

[6] Kewen Xia, Jianping Song and Changbiao Li. Information and Control. Vol. 32(4), p.300–303. (2003, In Chinese).

Google Scholar

[7] Fengqi Tan, Hongqi Li and Xu Zhao et al. Oil Geophysical Prospecting. Vol. 45(1), p.85–91. (2010, In Chinese).

Google Scholar

[8] Guiqin Wang, HUANG Dao. Study and application in decision tree algorithm. Microcomputer Application Technology. Vol. 72. p.1–7. (2008, In Chinese).

Google Scholar

[9] Xiaorong Dan, Xuanshu Chen and Fei Liu. Software guide. Vol. 8(2), p.41–43. (2009, In Chinese).

Google Scholar

[10] Rupi Li. Journal of East China Institute of technology(Natural Science). Vol. 33(2), p.192–196. (2010, In Chinese).

Google Scholar