基于核贝叶斯判别法的储层物性参数预测

doi:10.7623/syxb201607006

石油学报 ›› 2016, Vol. 37 ›› Issue (7): 878-886.DOI: 10.7623/syxb201607006

基于核贝叶斯判别法的储层物性参数预测

刘兴业, 陈小宏, 李景叶, 周林, 郭康康

中国石油大学油气资源与探测国家重点实验室中国石油大学海洋石油勘探国家工程实验室北京 102249

收稿日期:2015-12-22 修回日期:2016-05-23 出版日期:2016-07-25 发布日期:2016-07-28
通讯作者: 陈小宏,男,1962年8月生,1982年获南京大学学士学位,1993年获石油大学(北京)博士学位,现为中国石油大学(北京)教授、博士生导师,主要从事地球物理反演和时移地震研究。Email:chenxh@cup.edu.cn
作者简介:刘兴业,男,1991年1月生,2013年获中国石油大学(北京)学士学位,现为中国石油大学(北京)博士研究生,主要从事储层地球物理方面工作。Email:lwxwyh506673@126.com
基金资助:
国家自然科学基金项目（No.U1262207）和国家重大科技专项（2016ZX05033-003-008）资助。

Reservoir physical property prediction based on kernel-Bayes discriminant method

Liu Xingye, Chen Xiaohong, Li Jingye, Zhou Lin, Guo Kangkang

State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum; National Engineering Laboratory for Offshore Oil Exploration, China University of Petroleum, Beijing 102249, China

Received:2015-12-22 Revised:2016-05-23 Online:2016-07-25 Published:2016-07-28

摘要/Abstract

摘要：

随着油田开发的持续深入，地震勘探技术在储层预测及储层描述方面的要求不断提高。储层的物性参数是描述储层特征的主要参数，但由于影响储层物性参数的因素众多，且关系复杂，为储层物性参数的准确预测带来了巨大困难。基于传统贝叶斯判别的物性参数预测方法能够综合考虑多种参数，在获得预测结果的同时能够给出预测结果的概率分布，从中提取最大后验概率，并对预测结果的不确定性进行定量评价。但在预测过程中条件概率密度函数比较难以估计，一般假设各参数服从特定分布，但当数据分布比较复杂时，不满足这种假设，限制了其应用效果。因此，基于贝叶斯定理，采用核函数估算的方法计算条件概率密度函数，提出了基于核贝叶斯判别法的储层参数预测方法。该方法不需要假设数据服从特定的分布，采用非参数估计方法获取条件概率密度函数，可以计算获得物性参数的最大后验概率，实现了多种物性参数的预测并提供预测结果的置信概率，可用于进行不确定性评价。模型数据和实际资料的应用效果很好地验证了该方法的有效性。该方法在储层物性参数预测、储层描述中有良好的应用前景。

关键词: 核函数, 贝叶斯判别, 条件概率, 储层物性参数, 不确定性

Abstract:

With the constant deepening of oil field development, it is also required to continuously improve the seismic exploration technology in reservoir prediction and description. Reservoir physical properties are the main factors for describing the reservoir characteristics. However, the diversified factors impacting reservoir physical properties with complicated relationships lead to a great difficulty in accurately predicting reservoir physical properties. The physical property prediction method based on traditional Bayes discriminant can not only take various parameters into account, but also present the probability distribution of prediction results while providing prediction results, so as to extract the maximum posterior probability value and quantitatively evaluate the uncertainties in prediction results. However, it is difficult to estimate the conditional probability density function in the prediction process. Generally, it is assumed that various parameters are subject to a specific distribution. This assumption cannot be satisfied duo to the complicated data distribution, thus limiting its application effect. Therefore, based on Bayes theorem, the conditional probability density function is calculated using the kernel function estimation method. On this basis, the reservoir parameter prediction method based on the kernel-Bayes discriminant method is put forward. For this method, it is not required to assume that the data are subject to a specific distribution. Using non-parameter estimation method, the conditional probability density function can be obtained to further calculate the maximum posterior probability of physical property, thus achieving the prediction of multiple physical property parameters and providing the confidence probability of prediction results for uncertainty evaluation. The effectiveness of this method can be well validated by the application effect of model data and actual data, so that this method has a favorable application prospect in the prediction of reservoir physical property parameters and reservoir description.

Key words: kernel function, Bayes discriminant method, conditional probability, reservoir physical property, uncertainty

中图分类号:

P631.4

刘兴业, 陈小宏, 李景叶, 周林, 郭康康. 基于核贝叶斯判别法的储层物性参数预测[J]. 石油学报, 2016, 37(7): 878-886.

Liu Xingye, Chen Xiaohong, Li Jingye, Zhou Lin, Guo Kangkang. Reservoir physical property prediction based on kernel-Bayes discriminant method[J]. Acta Petrolei Sinica, 2016, 37(7): 878-886.

参考文献

[1] 魏友华,郭科,陈聆,等.信息融合在复杂油气储层物性参数综合研究中的应用[J].地球物理学进展,2008,23(1):153-156.
Wei Youhua,Guo Ke,Chen Ling,et al.The application of data fusion in the research of physic parameters of complicated oil-gas reservoir[J].Progress in Geophysics,2008,23(1):153-156.
[2] 李建忠,郑民,陈晓明,等.非常规油气内涵辨析、源-储组合类型及中国非常规油气发展潜力[J]. 石油学报,2015,36(5):521-532.
Li Jianzhong,Zheng Min,Chen Xiaoming,et al.Connotation analyses,source-reservoir assemblage types and development potential of unconventional hydrocarbon in China[J].Acta Petrolei Sinica,2015,36(5):521-532.
[3] 吴媚,符力耘,李维新.高分辨率非线性储层物性参数反演方法和应用[J].地球物理学报,2008,51(2):546-557.
Wu Mei,Fu Liyun,Li Weixin.A high-resolution nonlinear inversion method of reservoir parameters and its application to oil/gas exploration[J].Chinese Journal of Geophysics,2008,51(2):546-557.
[4] McCormack M D.Neural computing in geophysics[J].The Leading Edge,1991,10(1):11-15.
[5] 董恩清,高宏亮,李家金.波阻抗约束反演及储层物性参数计算方法[J].测井技术,1998,22(5):337-340.
Dong Enqing,Gao Hongliang,Li Jiajin.Acoustic impedance constrained inversion and the method of computing reservoir parameters[J].Well Logging Technology,1998,22(5):337-340.
[6] 印兴耀,杨风丽,吴国忱.神经网络在CB油田储层预测和储层厚度计算中的应用[J].石油大学学报:自然科学版,1998,22(2):17-20.
Yin Xingyao,Yang Fengli,Wu Guochen.Application of neural network to predicting reservoir and calculating thickness in CB oil field[J].Journal of the University of Petroleum,China:Edition of Natural Sciences,1998,22(2):17-20.
[7] 吴秋波,吴元,王允诚.混合学习法前向网络多属性储层参数预测[J].西南石油大学学报:自然科学版,2011,33(2):68-72.
Wu Qiubo,Wu Yun,Wang Yuncheng.Reservoir parameter prediction by multi-attribute based on hybrid learning algorithm of feed-forward network[J].Journal of Southwest Petroleum University:Science & Technology Edition,2011,33(2):68-72.
[8] 宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3):216-219.
Song Weiqi.Inversion of reservoir parameters with seismic attributes and logging data[J].Progress in Exploration Geophysics,2003,26(3):216-219.
[9] 刘百红,李建华,魏小东,等.随机反演在储层预测中的应用[J].地球物理学进展,2009,24(2):581-589.
Liu Baihong,Li Jianhua,Wei Xiaodong,et al.The application of stochastic seismic inversion in reservoir prediction[J].Progress in Geophysics,2009,24(2):581-589.
[10] 许建华,张学工,李衍达.应用核Fisher判别技术预测油气储集层[J].石油地球物理勘探,2002,37(2):170-174.
Xu Jianhua,Zhang Xuegong,Li Yanda.Application of kernel Fisher discriminating technique to prediction of hydrocarbon reservoir[J].Oil Geophysical Prospecting,2002,37(2):170-174.
[11] Mukerji T,Avseth P,Mavko G,et al.Statistical rock physics:combining rock physics,information theory,and geostatistics to reduce uncertainty in seismic reservoir characterization[J].The Leading Edge,2001,20(3):313-319.
[12] Eidsvik J,Avseth P,Omre H,et al.Stochastic reservoir characterization using prestack seismic data[J]. Geophysics,2004,69(4):978-993.
[13] 陈波,胡少华,毕建军.地震属性模式聚类预测储层物性参数[J].石油地球物理勘探,2005,40(2):204-208.
Chen Bo,Hu Shaohua,Bi Jianjun.Prediction of physical reservoir parameters by seis-mic attributes pattern cluster[J].Oil Geophysical Prospecting,2005,40(2):204-208.
[14] Grana D,Rossa E D.Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion[J].Geophysics,2010,75(3):O21-O37.
[15] Bosch M,Carvajal C,Rodrigues J,et al.Petrophysical seismic inversion conditioned to well-log data:methods and application to a gas reservoir[J].Geophysics,2009,74(2):O1-O15.
[16] 印兴耀,崔维,宗兆云,等.基于弹性阻抗的储层物性参数预测方法[J].地球物理学报,2014,57(12):4132-4140.
Yin Xingyao,Cui Wei,Zong Zhaoyun,et al.Petrophysical property inversion of reservoirs based on elastic impedance[J].Chinese Journal of Geophysics,2014,57(12):4132-4140.
[17] Ma Y Z,La Pointe P R.Uncertainty analysis and reservoir modeling[M].Tulsa:American Association of Petroleum Geologists,2011.
[18] 王孝超.油气储层建模中的不确定性研究[D].西安:西安石油大学,2014.
Wang Xiaochao.Research the uncertainty in reservoir modeling[D].Xi'an:Xi'an Shiyou University,2014.
[19] Vicente M G,Mustieles F J,Limeres A C,et al.Uncertainty evaluation in emerging gas projects in North Africa using geological modeling,reservoir simulation and experimental design[R].SPE 127853,2010.
[20] Caers J.Modeling uncertainty in the earth sciences[M].Chichester:Wiley-Blackwell,2011.
[21] 边肇祺,张学工.模式识别[M].2版.北京:清华大学出版社,2000.
Bian Zhaoqi,Zhang Xuegong.Pattern recognition[M].2nd ed.Beijing:Tsinghua University Press,2000.
[22] 林承焰,陈仕臻,张宪国,等.多趋势融合的概率体约束方法及其在储层建模中的应用[J].石油学报,2015,36(6):730-739.
Lin Chengyan,Chen Shizhen,Zhang Xianguo,et al.Probability constraint method based on multiple trend integration and its application in reservoir modeling[J].Acta Petrolei Sinica,2015,36(6):730-739.
[23] 胡华锋,印兴耀,吴国忱.基于贝叶斯分类的储层物性参数联合反演方法[J].石油物探,2012,51(3):225-232.
Hu Huafeng,Yin Xingyao,Wu Guochen.Joint inversion of petrophysical parameters based on Bayesian classification[J].Geophysical Prospecting for Petroleum,2012,51(3):225-232.
[24] 张玉敏.基于不同核函数的概率密度函数估计比较研究[D].保定:河北大学,2010.
Zhang Yumin.The comparative study of probability density function estimation based on the different kernel functions[D].Baoding:Hebei University,2010.
[25] 陈相兵.基于多元密度核估计的Bayes判别法[J].数学的实践与认识,2015,45(10):185-189.
Chen Xiangbing.Bayes multivariate discriminant method based on kernel density estimation[J].Mathematics in Practice and Theory,2015,45(10):185-189.
[26] 王芳芳.基于多点地质统计学的储层表征方法研究[D].北京:中国石油大学(北京),2014.
Wang Fangfang.Reservoir characterization based on multiple point geostatistics[D].Beijing:China University of Petroleum (Beijing),2014.
[27] Grana D,Mukerji T,Dvorkin J,et al.Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method[J].Geophysics, 2012,77(4):M53-M72.
[28] 曹丹平.基于Backus等效平均的测井资料尺度粗化方法研究[J].石油物探,2015,54(1):105-111.
Cao Danping.The upscaling method of the well logging data based on Backus equivalence average method[J].Geophysical Prospecting for Petroleum,2015,54(1):105-111.

基于核贝叶斯判别法的储层物性参数预测

Reservoir physical property prediction based on kernel-Bayes discriminant method

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