节点文献
改进的BP神经网络在石油测井解释中的应用
The Application of Improved BP ANN in Oil Well-log Interpretation
【摘要】 通过修正系统误差改进了传统的BP算法,改进后的BP算法具有收敛速度快的特点。在此基础上,利用多种测井解释数据及岩心分析资料作为网络训练样本,通过网络的训练、学习,建立了BP网络孔隙度模型,并利用该模型预测该地区新井的孔隙度值,实验证明用该模型进行孔隙度预测是可行的。
【Abstract】 BP algorithm was improved by fixing the system error,and the improved algorithm of BP has a fast convergence speed.Based on the improved BP algorithm,the porosity model is established after the network training by using a variety of logging and core analysis data as training samples.The porosity model is used to forecast the porosity of new log data in this area,and the results show that this porosity model is feasible.
【关键词】 BP神经网络;
测井解释;
物性参数;
孔隙度;
【Key words】 BP neural networks; well-log interpretation; physical parameters; porosity;
【Key words】 BP neural networks; well-log interpretation; physical parameters; porosity;
- 【文献出处】 北京石油化工学院学报 ,Journal of Beijing Institute of Petro-Chemical Technology , 编辑部邮箱 ,2008年01期
- 【分类号】P631.81
- 【被引频次】33
- 【下载频次】430