The Application of Data Mining in the Field of Energy

Wen Long Zang

doi:10.4028/www.scientific.net/AMM.448-453.3772

Paper Titles

Study on Vibroseis Data Preprocessing Method of Metal Mining Seismic Exploration
p.3751

Analysis of Distribution of Coalbed Methane and Geological Controlling Factors in Huanghebei Mining Area
p.3757

Forward Modeling of Controlled-Source Electromagnetic Using Finite Element Method
p.3762

Research on Modeling and Visualization Method of Geological Objects Based on VRML
p.3766

The Application of Data Mining in the Field of Energy
p.3772

Pure P-Wave Equation Modeling in Vertical Transverse Isotropic (VTI) Medium Using Pseudo-Analytical Method
p.3776

Experimental Research on Transient Electromagnetic Multi-Turn Minor Loop Device
p.3780

Electrical Structure Revealed by Magnetotelluric Data at the East Part of Central Asian Orogenic Belt, Central Inner Mongolia
p.3788

Geophysical - Geochemical Anomaly Characteristics and Prospecting Marks of Dachang Gold Deposit in Qinghai
p.3792

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 448-453The Application of Data Mining in the Field of...

The Application of Data Mining in the Field of Energy

Abstract:

Data mining technology is information from large amounts of data to dig out the unknown can be a practical and effective information, so provide richer information, or to provide decision support. Using data mining techniques handle large amounts of seismic data, analyze data to solve the reservoir characteristics of the intrinsic link scattered distribution problem is not easy to extract, and through Bayesian learning method to extract the hidden reservoir characteristics, based on completion of reservoir parameters of reservoir characterization data accurate modeling. Experimental results show that this method can effectively extract the reservoir characteristics of the data and accurately complete reservoir modeling, and achieved satisfactory results.

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Periodical:

Applied Mechanics and Materials (Volumes 448-453)

Pages:

3772-3775

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.448-453.3772

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Online since:

October 2013

Authors:

Wen Long Zang*

Keywords:

Bayesian Learning, Data Mining (DM)

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* - Corresponding Author

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