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神经网络方法在储层孔隙度预测中的应用
The Application of Reservoir Porosity Prediction Basing on Artificial Neural Network Method
【摘要】 储层参数预测对油田勘探具有重要意义。文章采用主成分变换优化法对海拉尔盆地某油田的11种地震属性进行标准化、主成分变换,计算结果说明,只需其中4种主成分用于储层预测。利用神经网络方法对储层孔隙度进行预测,预测结果与测井资料十分吻合,取得了较好的预测效果。
【Abstract】 It is very important to oilfield exploration that predicted reservoir parameter. In this paper the principal component transform method is used to standardize and transform the eleven seismic attributes in Hailaer Basin.The result of calculation indicated this reservoir prediction only need four principal component.The reservoir porosity is predicted by the artificial neural network method.The predicted result tallies with well log data and achieved good result.
【关键词】 孔隙度;
储层预测;
人工神经网络;
地震属性;
【Key words】 Porosity; Reservoir Prediction; Artificial Neural Network; Seismic Attribute;
【Key words】 Porosity; Reservoir Prediction; Artificial Neural Network; Seismic Attribute;
- 【文献出处】 内蒙古石油化工 ,Inner Mongolia Petrochemical Industry , 编辑部邮箱 ,2007年12期
- 【分类号】P618.13
- 【被引频次】5
- 【下载频次】179