Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

Feng Gao

doi:10.4028/www.scientific.net/AMR.524-527.180

Paper Titles

Numerical Modeling of Mud Invasion Profile Based on the Well Logging with the Shallow Depth of Investigation
p.148

On the Forward Simulation of the Geophysical Characteristics in Fractured Reservoirs
p.152

Pb Isotopic Geochemistry of Carbonate-Hosted Pb-Zn Deposits, NW Guizhou Pb-Zn Metallogenic District, China
p.160

Petrological and Rock Chemical Characteristics of Ore-Bearing Rock of Xinshan Area in Kafang, Gejiu, Yunnan
p.166

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network
p.180

REE Characteristic of Hydrothermal Calcite in Langdu Skarn Copper Deposit
p.184

Regional Seal as Key Controlled Elements of Petroleum Accumulation in the Rift Basin
p.190

Separability of PI3₃ in X Block of Daqing Oilfield
p.194

Spatial Distribution of Thin Interbeds in Point Bars of Meadering Channels: Case Study of Block X in Daqing Oilfield
p.199

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 524-527Prediction of Sand Thickness Using Seismic...

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

Abstract:

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

You might also be interested in these eBooks

Natural Resources and Sustainable Development II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 524-527)

Pages:

180-183

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.524-527.180

Citation:

Cite this paper

Online since:

May 2012

Authors:

Feng Gao

Keywords:

Artificial Neural Network (ANN), Prediction Model, Seismic Attribute

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Satish Kumar. Neural Networks. Beijing: Tsinghua University Press; 2006. in Chinese.

Google Scholar

[2] Hu Xiaorui, Lin Changchuan. A Preliminary Study on Targets Association Algorithm of Radar and AIS Using BP Neural Network. Procedia Engineering: p.1443.

DOI: 10.1016/j.proeng.2011.08.267

Google Scholar

[3] Qi Dehu, Kang Jichang. On Design of the BP Neural Network. Computer engineering and design: Vol.19 (1998), p.49 in Chinese.

Google Scholar

[4] Kai Cai, Juntao Xia, Longtu Li, Zhilun Gui. Analysis of the electrical properties of PZT by a BP artificial neural network. Computational Materials Science 34 (2005) 166–172.

DOI: 10.1016/j.commatsci.2004.12.066

Google Scholar

[5] MATLAB Chinese Forum. 30 EXAMPLES of MATLAB NEURAL NETWORK. Beijing: BUAA Press; 2010. in Chinese.

Google Scholar