基于D-S证据理论的地震多属性融合方法在煤层气富集区预测中的应用(英文)
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摘要
D-S证据理论为融合不确定信息提供了一条很好的思路。本文提出将D-S证据理论用于地震多属性融合的方法,首先在钻孔实测煤层气含量值的指导下优选对煤层气含量值变化敏感的地震属性,然后基于D-S证据理论对优选的地震属性进行融合处理,并将融合结果用于煤层气富集区的预测。实际应用效果表明:预测结果与钻孔实测煤层气含量值基本吻合,本文提出的基于D-S证据理论的地震多属性融合方法用于预测煤层气富集区是可行的。
D-S evidence theory provides a good approach to fuse uncertain information. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane (CBM) concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.
D-S evidence theory provides a good approach to fuse uncertain information. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane (CBM) concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.
引文
Beynon, M., Cosker, D., and Marshall, D., 2001, An expert system for multi-criteria decision making using Dempster–Shafer theory: Expert Syst. Appl., 20, 357 – 367.
Chen, S. Q., Wang, S. X., Zhang, Y. G., and Ji, M., 2009, Reservoir prediction using pre-stack inverted elastic parameters: Applied Geophysics, 6(4), 349 – 358.
Cheng, Q. S., 2006, Digital signal processing: Peking University Press, Beijing, 172 – 178.
Dempster, A. P., 1976, Upper and lower probabilities induced by a multi-valued mapping: Annals of Mathematical Statistics, 38(2), 325 – 339.
Deng, Y., Su, X. Y., Wang, D., and Li, Q., 2010, Target recognition based on fuzzy Dempster data fusion method: Defense Science Journal, 60(5), 525 – 530.
Hall, D. L., 1992, Mathematical techniques in multisensor data fusion: Artech House, Boston, London, 12-20.
Hart, B. S., 1999, Geology plays key role in seismic attributes studies:,Oil & Gas Journal, 97( 12), 76 – 80.
He′garat-Mascle, S. L., Richard, D., Ottle′, C., 2003, Multi-scale data fusion using Dempster–Shafer evidence theory: Integrated Comput. Aided Eng., 10, 9 – 22.
Ivan, D M., and Bruce, S. H., 2004, Seismic attribute-based characterization of coalbed methane reservoirs: An example from the Fruitland Formation, San Juan basin, New Mexico: The American Association of petroleum Geologists, 88(11), 1603 – 1621
Kang, Y. H., 2006, Data fusion theory and application: XianUniversity of Electronic Science and Technology Press,Xian, 100 – 105.
Kalkomey, C. T., 1997, Potential risks when using seismic attributes as predictors of reservoir properties: The Leading Edge, 16, 247 – 251.
Liu, W. L., 2009, Geophysical response characteristics ofcoal bed methane: Lithologic Reservoirs (in Chinese),21(2), 113 – 115.
Peng, S. P., Gao, Y. F., and Yang, R. Z., 2005, Theory and application of AVO for detection of coal-bed methane—A case from the Huainan coalfield: Chinese Journal of Geophysics (in Chinese), 48(6), 1475 – 1486.
Ramos, A. C. B., Davis, T. L.,1997, 3-D AVO analysis and modeling applied to fracture detection in coalbed reservoirs: Geophysics, 62, 1683 – 1695.
Shafer, G., 1976, A mathematical theory of evidence: Master’s Thesis, Princeton N. J., Princeton University Press, 1 – 24.
Yang, A. M., 2008, The fuzzy classifi cation model and its integration method: Science Press, Beijing, 10 – 20.
Chen, S. Q., Wang, S. X., Zhang, Y. G., and Ji, M., 2009, Reservoir prediction using pre-stack inverted elastic parameters: Applied Geophysics, 6(4), 349 – 358.
Cheng, Q. S., 2006, Digital signal processing: Peking University Press, Beijing, 172 – 178.
Dempster, A. P., 1976, Upper and lower probabilities induced by a multi-valued mapping: Annals of Mathematical Statistics, 38(2), 325 – 339.
Deng, Y., Su, X. Y., Wang, D., and Li, Q., 2010, Target recognition based on fuzzy Dempster data fusion method: Defense Science Journal, 60(5), 525 – 530.
Hall, D. L., 1992, Mathematical techniques in multisensor data fusion: Artech House, Boston, London, 12-20.
Hart, B. S., 1999, Geology plays key role in seismic attributes studies:,Oil & Gas Journal, 97( 12), 76 – 80.
He′garat-Mascle, S. L., Richard, D., Ottle′, C., 2003, Multi-scale data fusion using Dempster–Shafer evidence theory: Integrated Comput. Aided Eng., 10, 9 – 22.
Ivan, D M., and Bruce, S. H., 2004, Seismic attribute-based characterization of coalbed methane reservoirs: An example from the Fruitland Formation, San Juan basin, New Mexico: The American Association of petroleum Geologists, 88(11), 1603 – 1621
Kang, Y. H., 2006, Data fusion theory and application: XianUniversity of Electronic Science and Technology Press,Xian, 100 – 105.
Kalkomey, C. T., 1997, Potential risks when using seismic attributes as predictors of reservoir properties: The Leading Edge, 16, 247 – 251.
Liu, W. L., 2009, Geophysical response characteristics ofcoal bed methane: Lithologic Reservoirs (in Chinese),21(2), 113 – 115.
Peng, S. P., Gao, Y. F., and Yang, R. Z., 2005, Theory and application of AVO for detection of coal-bed methane—A case from the Huainan coalfield: Chinese Journal of Geophysics (in Chinese), 48(6), 1475 – 1486.
Ramos, A. C. B., Davis, T. L.,1997, 3-D AVO analysis and modeling applied to fracture detection in coalbed reservoirs: Geophysics, 62, 1683 – 1695.
Shafer, G., 1976, A mathematical theory of evidence: Master’s Thesis, Princeton N. J., Princeton University Press, 1 – 24.
Yang, A. M., 2008, The fuzzy classifi cation model and its integration method: Science Press, Beijing, 10 – 20.
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