Please be advised that the site will be down for maintenance on Sunday, September 1, 2024, from 08:00 to 18:00, and again on Monday, September 2, 2024, from 08:00 to 09:00. We apologize for any inconvenience this may cause.
dc.contributor.author | Huang, Yaping![]() |
|
dc.contributor.author | Yang, Haijun![]() |
|
dc.contributor.author | Qi, Xuemei![]() |
|
dc.contributor.author | Malekian, Reza![]() |
|
dc.contributor.author | Pfeiffer, Olivia![]() |
|
dc.contributor.author | Li, Zhixiong![]() |
|
dc.date.accessioned | 2018-03-28T08:43:02Z | |
dc.date.available | 2018-03-28T08:43:02Z | |
dc.date.issued | 2018-02-02 | |
dc.description | S1 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S2 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S3 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S4 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S5 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S6 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S7 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S8 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description | S9 File. The data is used in ªapplication to seismic dataº section. | en_ZA |
dc.description.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. | en_ZA |
dc.description.department | Electrical, Electronic and Computer Engineering | en_ZA |
dc.description.librarian | am2018 | en_ZA |
dc.description.sponsorship | Tthe Natural Science Foundation of China (Grant No. 41704104), the Chinese Postdoctoral Science Foundation (Grant No. 2014M551703), the Fundamental Research Funds for the Central Universities (Grant No. 2012QNA62), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) to YH. VC fellowship of UOW to ZL. The Institute of China Petroleum Tarim Oilfield Company provided support in the form of salaries for author Haijun Yang. | en_ZA |
dc.description.uri | http://www.plosone.org | en_ZA |
dc.identifier.citation | Huang Y, Yang H, Qi X, Malekian R, Pfeiffer O, Li Z (2018) A novel selection method of seismic attributes based on gray relational degree and support vector machine. PLoS ONE 13(2): e0192407. https://DOI.org/ 10.1371/journal.pone.0192407. | en_ZA |
dc.identifier.issn | 1932-6203 (online) | |
dc.identifier.other | 10.1371/journal.pone.0192407 | |
dc.identifier.uri | http://hdl.handle.net/2263/64326 | |
dc.language.iso | en | en_ZA |
dc.publisher | Public Library of Science | en_ZA |
dc.rights | © 2018 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. | en_ZA |
dc.subject | Accuracy | en_ZA |
dc.subject | Algorithm | en_ZA |
dc.subject | Calculation | en_ZA |
dc.subject | Correlation coefficient | en_ZA |
dc.subject | Mathematical analysis | en_ZA |
dc.subject | Mathematical models | en_ZA |
dc.subject | Prediction | en_ZA |
dc.subject | Validity | en_ZA |
dc.subject | Seismic attributes | en_ZA |
dc.subject | Gray relational degree (GRD) | en_ZA |
dc.subject | Support vector machine (SVM) | en_ZA |
dc.subject | Coalbed methane (CBM) | |
dc.title | A novel selection method of seismic attributes based on gray relational degree and support vector machine | en_ZA |
dc.type | Article | en_ZA |