首页 | 本学科首页   官方微博 | 高级检索  
     

储层敏感性快速预测软件在大港油田的研究与应用
引用本文:何银花,尤秋彦,钟惠敏,鄢捷年.储层敏感性快速预测软件在大港油田的研究与应用[J].钻井液与完井液,2002,19(1):32-35.
作者姓名:何银花  尤秋彦  钟惠敏  鄢捷年
作者单位:1. 大港油田钻采院,天津,大港
2. 石油大学,北京
摘    要:在储层特征分析,室内实验,机理研究的基础上,利用人工神经BP网络建立预测模型,并编制了预测储层5种敏感性的软件,该软件受人为因素干扰小,所需参数少,准确度高,综合符合率大于80%现场应用结果表明,该技术能为制定保护油气层技术措施提供较可靠的依据,能提高油田滚动开发效益,改善老油田的开发效果,该软件对BP算法进行了改进,(1)从两方面入手使网络摆脱平坦区,一是对输入数据进行归一化处理,使O1的取值在0,1]之间,二是一旦网络陷入平坦区域,局部极小,使连接权值Wkj,Wji和阈值Qk,Qj同时缩小一个因子,λ>1,可使Ok(1-Ok)脱离零值,离开平坦区;(2)加速收敛,方法有自动调整学习因子,添加动量项以及对权值进行批处理,用BP算法预测储层潜在敏感性,首先应确定影响储层敏感性的主要因素,然后根据这些因素有针对性地收集有关资料并进行处理,再根据敏感性预测的要求,设计相应的网络结构进行训练,最后对训练好的网络进行.检验。

关 键 词:神经网络  BP  潜在敏感性  防止地层损害  地质参数  水敏性  大港油田  储层敏感性  快速预测软件
修稿时间:2001年7月23日

Study and application of the formation damage of potential sensitivity fast predicting software in Daqing oil field
HE Yin-hua,YOU Qiu-yan,ZHONG Hui-min,and YAN Jie-nian Drilling and Production Inst. of Dagang Oilfield,Dagang,Tianjin.Study and application of the formation damage of potential sensitivity fast predicting software in Daqing oil field[J].Drilling Fluid & Completion Fluid,2002,19(1):32-35.
Authors:HE Yin-hua  YOU Qiu-yan  ZHONG Hui-min  and YAN Jie-nian Drilling and Production Inst of Dagang Oilfield  Dagang  Tianjin
Abstract:A software for predicting formation damage of 5 kinds of potential sensitivities has been established, based on the theory of artificial neural network, formation characteristics analysis and damage mechanism. The software has such advantages as little interruption, few parameters, high accuracy and coincidence rate of over 80%. Field using shows that it can provide reliable backup for planning the formation protection programs, and improve the oilfield rolling exploration economy and effect. The BP calculating method is modified in this software program, and in this method, the main factors influencing the formation sensitivities have to be determined first, then the related date collected for progressing, and the network structure designed for training. The trained network has to be tested finally.
Keywords:formation damage  geological parameter  water sensitivity  potential sensitivity  artificial neural network
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号