摘要
CO2驱是三次采油中最具潜力的提高采收率方法之一,准确评价和预测CO2驱的采收率成为一项非常重要的工作。由于影响采收率的因素较多,且影响因素与采收率之间是一种非线性、不确定的复杂关系,致使常规预测方法效率及精度不高。针对此问题编写BP神经网络程序,引入影响采收率的5个无因次变量对于这种非线性、不确定的多变量系统进行预测,结果表明,人工神经网络方法具有更好的自适应性,能较好地反映影响CO2驱的各种参数与采收率的内在联系,而且预测精度较高。应用BP神经网络方法预测CO2驱采收率是可行而有效的。
CO2 flooding is one of the most potential EOR methods.It is very important to accurately forecast the recovery factor of CO2 flooding.There are many influential factors on the recovery factor,and there is a nonlinear,uncertain,complex relationship between the influential factors and recovery factor,thus resulting in low accuracy with conventional forecast methods.Accordingly,a program of BP neural network has been developed,five dimensionless variables affecting recovery factor are introduced to forecast such a system with nonlinear and uncertain variables.The result shows that BP neural network has better adaptability,can better reflect the internal relations between various influential factors and recovery factor of CO2 flooding,and has high forecast accuracy.It is feasible and effective to forecast CO2 flooding recovery factor with BP neural network method.
出处
《特种油气藏》
CAS
CSCD
2011年第4期77-79,139,共3页
Special Oil & Gas Reservoirs
基金
"973"国家重点基础研究发展计划"温室气体提高石油采收率的资源化利用及地下埋存研究"(2006CB705800)
关键词
人工神经网络
CO2驱
采收率预测
数值模拟
artificial neural network
CO2 flooding
recovery factor forecast
numerical simulation
