1. A novel selection method of seismic attributes based on gray relational degree and support vector machine
- Author
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Zhixiong Li, Yaping Huang, Haijun Yang, Reza Malekian, Olivia Pfeiffer, and Xuemei Qi
- Subjects
Fossil Fuels ,Atmospheric Science ,Support Vector Machine ,010504 meteorology & atmospheric sciences ,Coalbed methane ,Computer science ,lcsh:Medicine ,010502 geochemistry & geophysics ,computer.software_genre ,01 natural sciences ,Immune Receptors ,Biochemistry ,Machine Learning ,Medicine and Health Sciences ,lcsh:Science ,Bandwidth (Signal Processing) ,Multidisciplinary ,Immune System Proteins ,Artificial neural network ,Mathematical model ,Chemistry ,Coal ,Physical Sciences ,Engineering and Technology ,Selection method ,Data mining ,Organic Materials ,Porosity ,Methane ,Algorithms ,Research Article ,Signal Transduction ,Optimization ,Computer and Information Sciences ,Correlation coefficient ,Materials Science ,Material Properties ,Immunology ,Fuels ,Instantaneous phase ,Greenhouse Gases ,Artificial Intelligence ,Support Vector Machines ,Environmental Chemistry ,Humans ,Materials by Attribute ,Artificial Neural Networks ,0105 earth and related environmental sciences ,Computational Neuroscience ,lcsh:R ,Ecology and Environmental Sciences ,Chemical Compounds ,Biology and Life Sciences ,Proteins ,Computational Biology ,Cell Biology ,Support vector machine ,Energy and Power ,Atmospheric Chemistry ,Signal Processing ,Earth Sciences ,lcsh:Q ,Pattern Recognition Receptors ,Gray (horse) ,computer ,Mathematics ,Neuroscience - Abstract
The selection of seismic attributes is a key process in reservoir prediction because the prediction accuracy relies on the reliability and credibility of the seismic attributes. However, effective selection method for useful seismic attributes is still a challenge. This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). The proposed method has a two-hierarchical structure. In the first hierarchy, the primary selection of seismic attributes is achieved by calculating the GRD between seismic attributes and reservoir parameters, and the GRD between the seismic attributes. The principle of the primary selection is that these seismic attributes with higher GRD to the reservoir parameters will have smaller GRD between themselves as compared to those with lower GRD to the reservoir parameters. Then the SVM is employed in the second hierarchy to perform an interactive error verification using training samples for the purpose of determining the final seismic attributes. A real-world case study was conducted to evaluate the proposed GRD-SVM method. Reliable seismic attributes were selected to predict the coalbed methane (CBM) content in southern Qinshui basin, China. In the analysis, the instantaneous amplitude, instantaneous bandwidth, instantaneous frequency, and minimum negative curvature were selected, and the predicted CBM content was fundamentally consistent with the measured CBM content. This real-world case study demonstrates that the proposed method is able to effectively select seismic attributes, and improve the prediction accuracy. Thus, the proposed GRD-SVM method can be used for the selection of seismic attributes in practice.
- Published
- 2018