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| 启动压力梯度预测的人工神经网络方法 | |
| 引用本文： | 朱长军,郝振纯,赵秀娟,周继红,李树文. 启动压力梯度预测的人工神经网络方法[J]. 西安石油大学学报(自然科学版), 2006, 21(5): 65-67 |
| 作者姓名： | 朱长军 郝振纯 赵秀娟 周继红 李树文 |
| 作者单位： | 1. 河北工程大学,城市建设学院,河北,邯郸,056038;河海大学,水资源环境学院,江苏,南京,210098 2. 河海大学,水资源环境学院,江苏,南京,210098 3. 河北工程大学,城市建设学院,河北,邯郸,056038 |
| 摘 要： | 针对低渗透多孔介质中存在启动压力梯度的问题,分析了影响启动压力梯度的主要因素.采用BP人工神经网络的方法对启动压力梯度进行预测,并结合岩心实际测定的启动压力梯度进行验证.研究结果表明,BP人工神经网络是一种较为有效的预测方法,具有较高的精度.该方法的应用,可以为低渗油田的开发提供可靠的基础数据,节省人力物力. |
| 关 键 词： | 低渗透油藏 启动压力梯度 B-P人工神经网络 预测 |
| 文章编号： | 1673-064X(2006)05-0065-03 |
| 修稿时间： | 2006-04-08 |
Prediction of threshold pressure gradient using artificial neural network |
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| ZHU Chang-jun,HAO Zhen-chun,ZHAO Xiu-juan,ZHOU Ji-hong,LI Shu-wen. Prediction of threshold pressure gradient using artificial neural network[J]. Journal of Xian Shiyou University, 2006, 21(5): 65-67 | |
| Authors: | ZHU Chang-jun HAO Zhen-chun ZHAO Xiu-juan ZHOU Ji-hong LI Shu-wen |
| Abstract: | There is threshold pressure gradient (TPG) in low-permeability porous media. The main factors of influencing TPG are analyzed. It is put forward that TPG is predicted by BP neural network, and the predicted results are verified by practically measured data. The result shows that this prediction approach is very effective and has higher accuracy. The application of this approach can supply basic data for the development of low-permeability oilfields so as to save cost and labor. |
| Keywords: | threshold pressure gradient prediction BP artificial neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |