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量子粒子群模糊神经网络碳酸盐岩流体识别方法研究

DOI: 10.6038/cjg20140328, PP. 991-1000

Keywords: 量子粒子群,模糊神经网络,部分角度叠加数据体,流体识别,塔里木盆地

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Abstract:

根据不同流体性质在角度道集上所反映特征的差异,构建了多属性角度叠加数据体组合流体识别因子.并将量子粒子群与模糊神经网络相结合,利用量子粒子群方法来优化模糊神经网络中的连接权值和隶属函数参数,并进行一系列的改进措施,显著提高了算法的全局寻优能力.将近远角度叠加数据体组合流体识别因子作为改进模糊神经网络的输入,流体性质作为输出,同时引入“相控流体识别”的思想,利用碳酸盐岩储集相进行控制,建立了碳酸盐岩流体识别模型.通过塔中实际井区进行验证,证明该方法能够提高流体的识别精度,具有很好的实际应用价值.

References

[1]  Liu L F, Sun Z D, Yang H J, et al. 2010. Modeling of facies-controlled carbonate reservoirs in the Tazhong area and its application. Acta Petrolei Sinica (in Chinese), 31(6): 592-598, doi: 10. 7623/syxb201006013.
[2]  Liu X, Jing B, Sun D, et al. 2011. Seismic reflection characteristics of high efficient wells in carbonate reservoirs in Western Tazhong Area, Tarim Basin. Xinjiang Petroleum Geology (in Chinese), 32(3): 301-304.
[3]  Ning Z H, He Z H, Huang D J. 2006. High sensitive fluid identification based on seismic data. Geophysical Prospecting for Petroleum (in Chinese), 45(3): 239-241, doi: 10. 3969/j. issn. 1000-1441. 2006. 03. 005.
[4]  Russell B H, Hedlin K, Hilterman F J. 2003. Fluid-property discrimination with AVO: A Biot-Gassmann perspective. Geophysics, 68(1): 29-39, doi:10.1190/1.1543192.
[5]  Rutherford S R. 1989. Amplitude versus offset variation in gas sands. Geophysics, 54(6): 680-688.
[6]  Sun D, Pan J G, Pan W Q, et al. 2010. Quantitative forward modelling of cavity volume in carbonate reservoirs in Tazhong area. Oil & Gas Geology (in Chinese), 31(6): 871-878, 882, doi: 10.11743/ogg20100621.
[7]  Sun J, Xu W B. 2004. A Global search strategy of Quantum-Be-haved particle swarm optimization. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, 111-116.
[8]  Yang J M, Kao C Y. 2001. A robust evolutionary algorithm for training neural networks. Neural Computing and Application, 10(3): 214-230.
[9]  Zhou X Y, Wang Z M, Yang H J, et al. 2006. Cases of discovery and exploration of marine fields in China (Part 5): Tazhong ordovician condensate field in tarim basin. Marine Origin Petroleum Geology (in Chinese), 11(1): 45-51.
[10]  Zhou X Y, Yang H J, Wu G H, et al. 2009. The experiences and targets for exploration of large Oil-Gas field in Tazhong area, tarim Basin. Xinjiang Petroleum Geology (in Chinese), 30(2): 149-152.
[11]  Castagna J P, Swan H W. 1997. Princip les of AVO cross plotting. The Leading Edge, 16(4): 337-342.
[12]  Fang W, Sun J, Xie Z P, et al. 2010. Convergence analysis of quantum-behaved particle swarm optimization algorithm and study on its control parameter. Acta Physica Sinica (in Chinese), 59(6): 3686-3693, doi: 10. 7498/aps.59.3686.
[13]  Franchini M. 1996. Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall runoff models. Hydrological Science Journal, 41(1): 21-39, doi: 10. 1080/02626669609491476.
[14]  Gao J. 2004. Introduction to Intelligent Information Processing (in Chinese). Beijing: China Machine Press, 254-280.
[15]  Goodway W, Chen T, Downton J. 1997. Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters form P and S inversion: Expanded Abstracts of 68th Annual Internat SEG Mtg: 183-186.
[16]  He Z H, Wang D. 2009. Expended fluid detection factor and its application. J. Mineral Petrol. (in Chinese), 29(4): 100-103, doi: 10.3969/j. issn. 1001-6872. 2009.04.015.
[17]  Holland J H. 1975. Adaptation in Natural and Artificial Systems. Michigan: University of Michigan Press.
[18]  Hu Z P. 2006. Mechanism and distinction method for the seismic "String Beads" characteristic. West China Petroleum Geosciences (in Chinese), 2(4): 423-426.
[19]  Kennedy J, Eberhar R. 1995. Particle Swarm Optimization: IEEE Int''l Conf on Neural Networks. Perth Australia, 1942-1948.

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