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基于支持向量回归机的地层孔隙压力预测方法

Pore pressure evaluation method based on support vector machines for regression.
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摘要 测井资料是确定地层孔隙压力的基础性资料,为此,利用测井资料来研究准噶尔盆地某区块的地层孔隙压力,开展地层孔隙压力区域研究,以充分认识异常地层孔隙压力分布规律。在分析地层孔隙压力预测传统方法局限性的基础上,提出了一种基于有效应力定理和声波速度模型的地层孔隙压力预测方法。由相关测井资料计算泥质含量、孔隙度和声波速度,利用声波速度模型计算垂直有效应力,利用密度测井资料计算上覆岩层压力,最后根据有效应力定理计算地层孔隙压力。声波速度模型由支持向量回归机(SVR)通过对相关测井、测压资料的非线性回归得到。实际应用表明,该方法能够以较高精度预测到异常地层孔隙压力,为钻井工程设计提供依据,提高钻井工艺水平,对钻井过程中防止工程事故发生,减少地层污染,节省钻井成本有着重要的应用价值。 Well logging data is the basic data for pore pressure evaluation. The well logging data from an area in Junggar basin was used to study the pore pressure. Through regional study of the pore pressure, abnormal high pressure distribution was recognized. After analyzing the limitation of the traditional pore pressure prediction methods, a new method based on the effective pressure theorem and the acoustic velocity model was proposed. The clay content, permeability and acoustic velocity were calculated from related well logging data and the vertical effective stress was computed from sonic velocity model. In addition, the overburden pressure was also calculated from density logging data. Finally, the pore pressure was calculated according to the effective pressure theorem. The acoustic velocity model was built by regression support vector machine through nonlinear regression of the related logging data and the pressure data. The actual application indicates that the method can predict abnormal pore pressure accurately, which provides foundations for drilling engineering design and improves the level of drilling technology. What is more, it has a significant application value in preventing engineering accidents, decreasing stratum pollution, and saving drilling cost.
出处 《石油物探》 EI CSCD 2007年第2期151-155,共5页 Geophysical Prospecting For Petroleum
基金 中国石油化工股份有限公司重大科技项目"基于钻井工程地质数据库的钻井模拟"(JP04014)资助
关键词 地层孔隙压力 支持向量回归机 声波速度 孔隙度 泥质含量 垂直有效应力 pore pressure support vector machine for regression acoustic velocity porosity clay contend vertical effective stress
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  • 1李克向 解浚昌.钻井手册(甲方)[M].北京:石油工业出版社,1990.. 被引量:15
  • 2邵新军,许昀,吕中锋,毛廷辉.地层流体压力预测方法的讨论[J].石油勘探与开发,2000,27(3):100-102. 被引量:28
  • 3文环明,肖慈珣,甄兆聪,粟英姿,汪华.琼东南盆地高压地层声波测井响应特征[J].石油物探,2003,42(2):265-270. 被引量:2
  • 4Eaton B A,The equation for geopressure prediction from well logs[J].SPE5544.1975.1-5. 被引量:1
  • 5史清江,王延江.用于非线性回归估计的支持向量机[M].山东济南:山东大学出版社,2004.45-48. 被引量:1
  • 6Vapnik V N,The nature of statistical learning theory [M].New York:Springer Verlag.1999.13-25 被引量:1
  • 7Nello Cristianini.John Shawe-Taylor,An introduction to support vector machines and other kernel-based learning methods[M].London:Cambridge University Press.2000.33-41. 被引量:1
  • 8邓乃杨 田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004.. 被引量:37
  • 9樊洪海..地层孔隙压力预测检测新方法研究与应用[D].中国石油大学(北京),2001:
  • 10Eberhart-Phillips D,Han D H.Zoback M D,Empiri cal relationships among seismic velocity ,effective pressure .porosity ,and clay content in sandstone[J].Gepohysics.1989.54(1).82-88. 被引量:1

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