摘要
在油田开发过程中,剩余油饱和度是一个变量,且受多种因素的共同控制,预测难度较大。针对构造背景相对简单、分层测试资料比较齐全的海上非注水开发的典型边、底水油藏,初步研究出了一套根据主要的多种动、静态参数并应用神经网络技术预测含水饱和度随时间变化速率的方法,进而根据含水饱和度变化率来预测不同时期的剩余油饱和度分布。在我国海上某油田进行了应用实例验证,取得了比较满意的效果。
During the development of oilfield, the remaining oil saturation is a variable dominated by many kinds of subjective and objective factors, so it is difficult to be forecasted. For the classical edge and basal water reservoirs in non-water flooding with relatively simple structure and abundant stratified test data, a new method for forecasting the change rate of water saturation by using dynamic and static parameters as well as artificial neural network technology is proposed. The distribution of remaining oil saturation in different stage can be forecasted according to the change rate of water saturation. An applied example in a offshore oilfield proved the reliability of this method.
出处
《石油学报》
EI
CAS
CSCD
北大核心
2005年第3期60-63,共4页
Acta Petrolei Sinica
基金
国家自然科学基金项目(No.40242015)"应用储层建模技术进行剩余油分布预测的研究"
科技部重大国际合作项目(2002cb713906)"油田开发后期精细油藏描述与提高采收率技术研究"联合资助。
关键词
剩余油分布
含水饱和度
神经网络技术
生产井影响因子
预测方法
remaining oil distribution
water saturation
neural network technology
production wells influencing index
forecast method
