新疆石油地质

›› 2013, Vol. 34 ›› Issue (3): 1-1.

• 论文 •    

多属性回归与神经网络串联反演预测薄储集层

王晓辉1，2，范素芳2，任毅军1，2，徐宝荣2，刘新利2   

  1. (1.西南石油大学 资源与环境学院,成都 610081;2.中国石油 东方地球物理公司 研究院 乌鲁木齐分院,乌鲁木齐 830016)
  • 出版日期:2019-01-01 发布日期:1905-07-11

Application of Series Inversion of MultiAttribute Regression and Probabilistic Neural Network to Thin Reservoir Prediction

WANG Xiaohui1,2, FAN Sufang2, REN Yijun1,2, XU Baorong2, LIU Xinli2   

  1. (1.College Resource and Environment, Southwest Petroleum University, Chengdu, Sichuan 610081, China; 2.Urumqi Branch, GRI, BGP,CNPC, Urumqi, Xinjiang 830016, China)
  • Online:2019-01-01 Published:1905-07-11

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摘要： 在储集层地球物理响应分析和研究的基础上，应用多属性回归与神经网络串联反演方法，对研究区进行了可以表征薄储集层的自然伽马曲线反演。分析认为，砂体预测结果符合研究区整体沉积特征，纵向分辨率较高，横向砂体边界清晰，能够反映储集层的分布规律，为研究区今后的勘探指明了方向。

Abstract: This paper presents GR curves inversion for thin reservoir characterization in the studied area, using the series inversion of multi? attribute regression (MAR) and probabilistic neural network (PNN) based on the geophysical response analysis of reservoir. The result shows that the sand body prediction accords with the whole sedimentary features in the studied area, with high vertical resolution, clear boundary of lateral sand bodies. It could properly reflect the distribution of reservoirs and can be as a guide for next petroleum exploration in this area

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