Abstract
The heavy mineral analysis is the important content of the oil and gas exploration in sedimentary basin,and the provenance data can be clustered according to the theory of sedimentary heavy mineral composition similar to its characteristic value. But self-organizing neural network can not determine its own clustering number, thus we introduce the immune algorithm, which can better adjust the number of competitive layer neurons and the size of the adjustment of the neighborhood. Classify the provenance data with immune self-organizing neural network, and compare the result of clustering with sedimentary facies, which confirmes the reliability of the method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhao, T.: The Distribtion of Biolimestone and Igneous Rock in Dagang Area and the Research for Its Hydrocarbon Reservoir. Logging Technology 12(3), 41–46 (2001)
Linfu, X., Baozhi, P.: Dentify lithofacies automaticallyusing self-organizing neural network. Journal of Changchun university of science and technology 29(2), 144–147 (1999)
Li, G., Shao, F., et al.: Research of the clustering algorithm based on neural network. Journal of Qingdao University Engineering & Technology Edition 16(4), 21–24 (2001)
Wu, X.: Contrast of SOM Neural Networks and Cluster Analysis Using in Grain-size Analysis, pp. 48-51 (2006)
Jiang, Z.: Sedimentology, pp. 101–102. Petroleum Industry Press, Beijing (2003)
Liu, X., Wen, J., et al.: A Fuzzy Clustering Method to Recognize Coherent Generator Groups Based on Self-Organizing Neural Network. Power System Technology (7), 98–102 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, Y., Bai, Y., Zhang, T., Wang, J. (2011). The Heavy Mineral Analysis Based on Immune Self-organizing Neural Network. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23777-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-23777-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23776-8
Online ISBN: 978-3-642-23777-5
eBook Packages: EngineeringEngineering (R0)