The Identification Method of Igneous Rock Lithology Based on Data Mining Technology

Jun Zhang; Jun Li; Yao Hu; Jin Yu Zhou

doi:10.4028/www.scientific.net/AMR.466-467.65

Paper Titles

The Application and Simulation Research of the Fuzzy PID Control Used in PVC Polymerization Temperature Control System
p.47

The Application of PID Based on Optimized RBF in Thickness Control of Strip Steel
p.52

The Strength and Elongation of the SP-PVA/Combed Cotton Blended Yarn
p.57

On Pumping Ultra-Early Strength Concrete of Artificial Sand
p.61

The Identification Method of Igneous Rock Lithology Based on Data Mining Technology
p.65

An Analysis on Electrical Conduction Spatial Characteristics in Saline-Alkali Soil
p.70

Aluminum Semi-Solid Slurry Viscosity Measured Steady-State and Computer Simulation Compared
p.75

The Auto-Control of pH Value Based on Labview
p.79

The Applications of Molecularly Imprinted Polymer in Immunoassay, Biosensor and Enzyme Mimic Catalyst-A Critical Review
p.84

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467The Identification Method of Igneous Rock...

The Identification Method of Igneous Rock Lithology Based on Data Mining Technology

Abstract:

Igenous rock is featured with complex and multivariant lithology, its logging response is multiplicity, therefore, it is difficult to identify igneous rock lithology with logging data. To Lay the foundation for fine logging evaluation of igneous reservoir in Songnan gas field, the identification method of igneous rock lithology is researched. Common crossplot method identify lithology with only two logging parameters, its precision is not high. To improve the accuracy of identification, the data mining software, named as weak, is used, three data mining methods, including Association Rule, Decision Tree, Support vector machine, are applied in lithology identification. The results show that these methods can improve the accuracy of lithology identification, in particular, Decision Tree model has the highest recognition accuracy, while it is relatively easy to understand, so it can be used as auxiliary tools for recognition of igneous rocks. Decision Tree model is used to process logging data of exploratory well, and its computation results are well consistent with the core thin section data.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

65-69

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.65

Citation:

Cite this paper

Online since:

February 2012

Authors:

Jun Zhang, Jun Li, Yao Hu, Jin Yu Zhou

Keywords:

Crossplot Method, Data Mining (DM), Decision Tree, Lithology Identification

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] Wang Pujun, Feng Zhiqiang, Basin Volcanic Rocks: Lithology · Lithofacies · Reservoir Formation · Gas Reservoir · Exploration, Beijing: Science Press, (2009).

Google Scholar

[2] China Petroleum Exploration and Production Company, Logging Evaluation Technology and Application of Volcanic Rocks Gas Reservoir, Beijing: Petroleum Industry Press, (2009).

Google Scholar

[3] Li Ning, Tao Honggen, Liu Chuanping, Logging Interpretation Theory, Method and Application of Acid Volcanic Rocks, Beijing: Petroleum Industry Press, (2009).

Google Scholar

[4] Pan Baozhi, Li Zhoubo, Fu Yousheng, Wang Hongjian, Yang Xiaoling, Xu Shumei, Application of logging data in lithology identification and reservoir evaluation of igneous rock in Songliao basin, Geophysical Prospecting for Petroleum, vol. 48, Jan. 2009, pp.48-52.

Google Scholar

[5] Yang Shengu, Liu Xiaocui, Hu Zhihua, Fan Zhen, Xu Jiangtao, Cao Zuo, Volcanic Logging Recognition in Reservoir Analysis, Journal of Oil and Gas Technology, vol. 29, Dec. 2007, pp.33-37, doi: CNKI: SUN: JHSX. 0. 2007-06-008.

Google Scholar

[6] Yang Shengu, Wu Hongzhen, Logging Recognition Method of Mesozoic and Cainozoic Group Volcanites in Dawa Oilfield, Journal of Jianghan Petroleum Institute, vol. 25, Jun. 2003, pp.58-59, doi: cnki: ISSN: 1000-9752. 0. 2003-02-030.

Google Scholar

[7] Pan Baozhi, Yan Guijing, Wu Haibo, Determining Lithology of Igneous Rocks in Deep Northern Songliao Basin Using Correspondence Analysis, Petroleum Geology & Oilfield Development in Daqing, vol. 22, Feb. 2003, pp.7-9.

Google Scholar

[8] Luo Jinglan, Lin Tong, Yang Zhisheng, Liu Xiaohong, Zhang Jun, Liu Shuyun, Lithofacies and reservoir quality control factors of volcanics in the Yingcheng Formation in the Shengping gas field in the Songliao Basin, Oil & Gas Geology, vol. 29, Dec. 2008, pp.748-757.

Google Scholar

[9] Xie Xiaoan, Zhou Zhuoming, Exploration practices and directions for deep natural gas in the Songliao Basin, Oil & Gas Geology, vol. 29, Feb. 2008, pp.113-119, doi: CNKI: SUN: SYYT. 0. 2008-01-020.

Google Scholar

[10] Hong Youmi, Well logging Principles and Comprehensive Interpretation, Dongying: Petroleum University Press, (1993).

Google Scholar

[11] Sun Jianmeng, Wang Yonggang, Comprehensive Interpretation of Geophysical Data, Beijing: Petroleum University Press, (2001).

Google Scholar

[12] Witten I H, Frank E,. Data mining: practical machine learning tools and techniques, Translators: Dong Lin, Qiu Quan, Yu Xiaofeng, et al Beijing: Machinery Industry Press, (2006).

Google Scholar

[13] Groth R, Data mining: Building Competitive Advantage, " Translators: Hou Di, Song Qinbao et al. Xi'an: Xi, an Jiaotong University Press, (2001).

Google Scholar

[14] Vapnik V N, The nature of statistical learning theory, NY: Springer-Verlag, (1995).

Google Scholar