基于响应面法优化纳米智能驱油剂提高采收率的研究

Study on optimizing nano-intelligent oil displacement agent based on response surface method to enhance oil recovery

针对低渗透油田的采收率较低的问题,利用长庆油气院研发的纳米智能驱油剂,在室内驱油试验的基础上,通过单因素试验探究地层水矿化度、纳米智能驱油剂质量分数、温度、注入量及渗透率对采收率的影响,并通过SPSS软件的单因素和多重因素显著性检验,筛选出显著性因素,进一步采用软件Design Expert 8.0中Box-Behnken法进行响应面试验设计。结果表明,各因素显著性由大到小依次为:渗透率、纳米智能驱油剂质量分数、地层水矿化度、注入PV数。各因素最优条件为:矿化度30968.57 mg/L,纳米智能驱油剂质量分数0.37%,注入PV数3.14,渗透率1.81×10^3μm^2。在该条件下,采收率提高预测值为14.1606%,与重复性验证试验值误差仅为0.0677个百分点,吻合度良好。

Aiming at the problem of low recovery in low permeability oilfield,the nano-intelligent oil displacement agent developed by Changqing Oil and Gas Institute is used to explore the effects of formation water salinity,mass fraction of nano-intelligent oil displacement agent,temperature,injection volume and permeability on recovery efficiency based on single-factor indoor oil displacement experiments.The significant factors are screened through the single-factor and multiple-factor significance tests of SPSS software,and the Box-Behnken method in Design Expert 8.0 is used to design the response surface experiments.The results show that the significant order of each factor from strong to weak is:permeability,mass fraction,salinity,injection volume.The optimal conditions for each factor are obtained as follows:the formation water salinity 30968.57mg/L,the mass fraction of nano-intelligent oil displacement agent 0.37%,the injection PV volume 3.14,and the permeability 1.81×10^3μm^2.Under the optimum conditions,the predicted value of the enhanced recovery factor is 14.1606%,and the error is only 0.0677 percentage points,which is in good agreement with the repeatability verification experimental value.