利用地震资料反演地层的碳酸盐含量

熊艳; 钟广法; 李前裕; 吴能友; 李学杰; 马在田

利用地震资料反演地层的碳酸盐含量

1.
同济大学海洋地质国家重点实验室, 上海 200092
2.
广州海洋地质调查局, 广东广州 510760

基金项目:

国家自然科学基金项目 40476030

国家自然科学基金项目 40576031

国家重点基础研究发展规划项目 G2000078501

详细信息

作者简介:
熊艳(1972—), 女, 博士研究生, 从事测井地质学和地震地层学方面的研究工作.E-mail：xyan163xy@163.com

中图分类号: P631.4
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出版历程
- 收稿日期: 2006-05-12
- 刊出日期: 2006-11-25

Inversion of Stratal Carbonate Content Using Seismic Data

1.
State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
2.
Guangzhou Marine Geological Survey, Guangzhou 510760, China

摘要

摘要: 基于碳酸盐含量与地层速度、密度之间的关系, 在井资料约束下, 使用人工神经网络方法反演高分辨率地震资料所反映的地层碳酸盐含量, 并应用于南海北部陆坡ODP184航次1146和1148孔区, 取得较好效果.方法的关键是从井旁地震道中提取多种属性, 利用逐步回归法, 确定6种与碳酸盐含量相关性最好的地震属性, 分别是平均频率、道积分绝对振幅、主频、时间、道微分瞬时振幅和瞬时频率, 然后进行地层碳酸盐含量反演.反演结果相对于岩心分析的碳酸盐含量的误差大多在±5%之内, 较为精确地揭示了地震地层剖面上碳酸盐含量的分布.
- 碳酸盐含量反演 /
- 地震资料 /
- 人工神经网络 /
- ODP184航次 /
- 南海北部
Abstract: Based upon the relationship between carbonate content and stratal velocity and density, we attempted to apply the artificial neural network to the inversion of carbonate content summarized from the high-resolution seismic data limited by controlled well measurements. The method was applied to the slope area of the northern South China Sea near ODP Sites 1146 and 1148, with satisfactory results. The key to this method is the collection of several properties from seismic profiles across or near the wells. Then the progressive regression method was primarily applied to the determination of six seismic properties, most closely related to carbonate content variations, which are defined as average frequency, integrated absolute amplitude, dominating frequency, reflection time, derivative instantaneous amplitude, and instantaneous frequency. Finally, the stratal carbonate content is reversed. The reversal results thus obtained, with the errors of carbonate content mostly ranging within ±5% relative to those measured from sediment samples, show a relative accurate picture of carbonate-content distribution along the slope profile.
- carbonate content inversion /
- seismic data /
- artificial neural network /
- ODP Leg 184 /
- northern South China Sea

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图 1 研究区位置略图(水深线单位m)

Fig. 1. Location map of the study area

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图 2 岩心实测的碳酸盐含量、声波速度和密度测井曲线之间的关系

Fig. 2. Relationship between core-measured carbonate content, P-wave velocity and density logs at ODP Site 1146 (a) and ODP Site 1148 (b)

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图 3 岩心实测的碳酸盐含量与速度交会图

Fig. 3. Cross-plots of core-measured P-wave velocity versus carbonate content

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图 4 岩心实测的碳酸盐含量与密度交会图

Fig. 4. Cross-plots of core-measured density versus carbonate content

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图 5 岩心实测碳酸盐含量曲线与根据井旁道反演的碳酸盐含量曲线之间的对比

Fig. 5. Comparison of core-measured carbonate content and inversed carbonate content from seismic data

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图 6 (a) 未解释的地震剖面; (b) 反演的碳酸盐含量剖面(测线AA′位置见图 1)

Fig. 6. (a) Un-interpreted seismic profile, (b) inversed carbonate content profile

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参考文献(24)

[1]	Bishop, C., 1995. Neural networks for pattern recognition. Oxford University Press, New York.
[2]	Chen, X. L., Zhao, Q. H., Jian, Z. M., 2002. Carbonate content changes since the Miocene and paleoenvironmental implications, ODP Site 1148, northern South China Sea. Marine Geology and Quaternary Geology, 22 (2): 69-74 (in Chinese with English abstract).
[3]	Fabricius, I. L., 2003. How burial diagenesis of chalk sediments controls sonic velocity and porosity. AAPG Bulletin, 87: 1755-1778. doi: 10.1306/06230301113
[4]	Hampson, D., Schuelke, J., Quirein, J., 2001. Using multiattribute transforms to predict log properties from seismic data. Geophysics, 66 (1): 220-236. doi: 10.1190/1.1444899
[5]	Hecht-Nielsen, R., 1989. Theory of backpropagation neural networks. Presented at IEEE Proc., Int. Conf. Neural Network, Washington, DC.
[6]	Huang, W., Liu, Z. F., Chen, X. L., et al., 2003. Searching physical indicators of carbonate contents of deep sea sediments. Earth Science—Journal of China University of Geosciences, 28 (2): 157-162 (in Chinese with English abstract).
[7]	Kenter, A. M., Anselmetti, F. S., Kramer, P. H., et al., 2002. Acoustic properties of "YOUNG" carbonate rocks, ODP Leg 166 and boreholes Clino and Unda, western Great Bahama Bank. Journal of Sedimentary Research, 72: 129-137. doi: 10.1306/041101720129
[8]	Li, Q. Y., Wang, P. X., Zhao, Q., et al., 2006. A 33 Ma lithostratigraphic record of tectonic and paleoceanographic evolution of the South China Sea. Marine Geology, 230: 217-235. doi: 10.1016/j.margeo.2006.05.006
[9]	Nikravesh, M., 2004. Soft computing-based computational intelligent for reservoir characterization. Expert Systems with Applications, 26: 19-38. doi: 10.1016/S0957-4174(03)00119-2
[10]	Nikravesh, M., Adams, R. D., Levey, R. A., 2001a. Soft computing: Tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP). Journal of Petroleum Science and Engineering, 29 (3/4): 239-262.
[11]	Nikravesh, M., Aminzadeh, F., Zadeh, L. A., 2001b. Soft computing and earth sciences (Part2). Journal of Petroleum Science and Engineering, 31 (2-4): 67-204. doi: 10.1016/S0920-4105(01)00121-8
[12]	Rosenblatt, F., 1962. Principals of neurodynamics. Spartan, New York.
[13]	Sarg, J. F., Schuelke, J. S., 2003. Integrated seismic analysis of carbonate reservoirs: From the framework to the volume attributes. The Leading Edge, 22 (7): 640-645. doi: 10.1190/1.1599689
[14]	Su, X., Xu, Y., Tu, Q., 2004. Early Oligocene-Pleistocene calcareous nannofossil biostratigraphy of the northern South China Sea (Leg 184, Sites 1146-1148). In: Prell, W. L., Wang, P. X., Blum, P., et al., eds, Proceedings of the Ocean Drilling Program, Scientific Results, Volume 184.
[15]	Trentesaux, A., Recourt, P., Bout-Roumazeilles, V., et al., 2001. Carbonate grain-size distribution in hemipelagic sediments from a laser particle sizer. Journal of Sedimentary Research, 71: 858-862. doi: 10.1306/2DC4096E-0E47-11D7-8643000102C1865D
[16]	Wallace, M. W., Holdgate, G. R., Daniels, J., et al., 2002. Sonic velocity, submarine canyons, and burial diagenesis in Oligocene-Holocene cool water carbonates, Gippsland basin, southeast Australia. AAPG Bulletin, 86: 1593-1607.
[17]	Wang, P. X., Prell, W., Blum, P., et al., 2000. Proceedings of the ocean drilling program, initial reports, Volume 184. Ocean Drilling Program, College Station, 1-77.
[18]	Wong, P. M., Tamhane, D., Wang, L., et al., 1997. Network approach to knowledge-based well interpolation: A case study of a fluvial sandstone reservoir. Journal of Petroleum Geology, 20: 363-372. doi: 10.1111/j.1747-5457.1997.tb00641.x
[19]	Wu, D. K., Li, Y. L., Wu, Z. M., et al., 2004. Research on the neural network algorithm of joint inversion of seismic and log data. Natural Gas Industry, 24 (3): 55-57 (in Chinese with English abstract).
[20]	Zhong, G. F., Li, Q. Y., Chen, Q., et al., 2006. Oligocene mineral component inversion using geophysical well logs from ODP Hole 1148A, South China Sea. Journal of Tongji University (Natural Science), 34 (10) (in press, in Chinese with English abstract).
[21]	陈晓良, 赵泉鸿, 翦知湣, 2002. 南海北部ODP184站中新世以来的碳酸盐含量变化及其古环境意义. 海洋地质与第四纪地质, 22 (2): 69-74. https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ200202013.htm
[22]	黄维, 刘志飞, 陈晓良, 等, 2003. 寻求深海碳酸盐沉积含量的物理标志. 地球科学——中国地质大学学报, 28 (2): 157-162. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX200302007.htm
[23]	吴大奎, 李亚林, 伍志明, 等, 2004. 地震、测井资料联合反演的神经网络算法研究. 天然气工业, 24 (3): 55-57. doi: 10.3321/j.issn:1000-0976.2004.03.016
[24]	钟广法, 李前裕, 陈强, 等, 2006. 根据测井资料反演ODP1148A孔渐新统的矿物组成. 同济大学学报, 34 (10) (待刊).