摘要
低阻油藏是一种非常规油藏 ,电性与物性之间存在着一种模湖的非线性关系。如何评价它的产状是研究和生产的难点。本文从分析油井的产能出发 ,导出了影响油藏产状的因素。并利用人工神经网络具有自适应、自学习的特点 ,将神经网络与常规测井、试油、试井等动态资料相结合进行油藏产状评价 ,取得了较好的效果。
Having analysing the production capacity of oil wells ,the factors which affect the reservoir occurrence are concluded.Using the characters of artifical nerve networks,adapt itself,learn itself and antijamming,it is combined with dynamic data,such as,conventional logging,oil production test and well testing,to evaluate reservoir,good effects are received.
出处
《断块油气田》
CAS
2000年第5期35-37,共3页
Fault-Block Oil & Gas Field
关键词
人工神经网络
BP模型
油藏
产状评价
BP networks,Reservoir evaluation, Artifical nerve network
