Ying Li
ABSTRACT
In the field of oil and gas exploration, reservoir parameter prediction is often affected by multi-solution of the seismic attribute combination, which leads to low prediction accuracy. In this paper, a feature selection method based on Gamma test is proposed to optimize the attribute combination and then combine it with deep neural network to accomplish reservoir parameter prediction. By computing the value of the statistics, it not only provides the best combination of the corresponding attributes to predict the target but also provides the proper training mean square error of neural network and the proper size of the training set. With this guide, over-fitting can be effectively avoided and the prediction accuracy is improved. The selected seismic attributes combination is used as the optimized network input, then use extreme learning machine to accomplish the regression problem. Through the analysis of the real seismic data experimental results, it is proved that the Gamma test is an effective nonparametric tool for feature selection.
- Quincy Chen, Steve Sidney. 1997. Seismic attribute technology for reservoir forecasting and monitoring. The Leading Edge, 16, 5, 445--448(1997).Google Scholar
Cross Ref
- Jonathan Henderson, Stephen J. Purves, Gaynor Fisher, Chris Leppard. 2008. Delineation of geological elements from RGB color blending of seismic attribute volumes. The Leading Edge, 27, 3, 342--350.Google ScholarCross Ref
- Liu Xingfang, Zheng Xiaodong, Xu Guangcheng, et al. 2010. Locally linear embedding-based seismic attribute extraction and applications. Applied Geophysics, 7, 4,365--375.Google ScholarCross Ref
- Aðalbjörn Stefansson, N. Konar, Antonia J. Jones. 1997. A Note on the Gamma Test. Neural Computing Applications, 5, 131--133.Google ScholarCross Ref
- Dafydd Evans, Antonia J. Jones. 2002. A proof of the Gamma test. The Royal Society, 458, 2759--2799.Google ScholarCross Ref
- Antonia J. Jones. 2004. New tools in non-linear modeling and prediction. Computational Management Science, 1,109--149.Google ScholarCross Ref
- Dafydd Evans, Antonia J. Jones. 2008. Non-parametric estimation of residual moments and covariance. The Royal Society, 464, 2831--2846.Google ScholarCross Ref
- Ursula Iturraran Viveros. 2012. Smooth regression to estimate effective porosity using seismic attributes. Journal of Applied Geophysics, 76, 1--12.Google ScholarCross Ref
- Kybic J, Vnučko I. 2010. Approximate Best Bin First kd Tree All Nearest Neighbor Search with Incremental Updates. 10.Google Scholar
- Hogan S, Jarvis P, Wilson I. 2011. Using the Gamma Test in the Analysis of Classification Models for Time-Series Events in Urodynamics Investigations. Research and Development in Intelligent Systems XXVIII. Springer London, 299--310.Google Scholar
- J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. 2011. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing 17, 2-3, 255--287.Google Scholar
Index Terms
- Reservoir Parameter Prediction Using Optimized Seismic Attributes Based on Gamma Test
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