多属性回归与神经网络串联反演预测薄储集层

Xinjiang Petroleum Geology(2013)

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Abstract
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。
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Key words
thin reservoir prediction,probabilistic neural network,series inversion,multi-attribute regression
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