基于x梯度提升算法的聚合物驱参数对提高采收率影响敏感性研究

T. Erfando, Rizqy Khariszma
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引用次数: 1

摘要

实施水驱有时不能有效提高采收率,需要额外的方法来提高采收率。聚合物驱是一种常用的化学提高采收率方法,在过去的几十年里一直在应用,它在提高采收率方面具有良好的效果,并且可以减少注入储层的注入液量。鉴于聚合物驱提高采收率的成功,有必要了解影响聚合物驱成功的参数,以便在制定聚合物驱提高采收率的新方案时进行评估和考虑。本研究测试的参数包括:注射速率、注射时间、注射压力、吸附量、不可达孔体积、残余阻力系数。本研究使用X-Gardient Boosting算法来研究聚合物驱中最具影响的参数。在本研究中,对聚合物驱性能影响最大的参数为:注入时间0.452632,注入速率0.430075,注入压力0.064662,吸附量0.025564,RRF为0.021053,IPV为0.006014,在训练数据与测试数据对比比为0.7的情况下,对3种变化进行x梯度增强,得到准确的预测模型:0.3得到的R2列为0.9886,R2检验为0.9645,0.8:0.2得到的R2列为0.9891,R2检验为0.9579,0.9:0.1得到的R2列为0.9890,R2检验为0.9660。
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Sensitivity Study of The Effect Polymer Flooding Parameters to Improve Oil Recovery Using X-Gradient Boosting Algorithm
Implementation of waterflooding sometimes cannot increase oil recovery effectively and requires additional methods to increase oil recovery. Polymer flooding is a common chemical EOR method that has been implemented in the last few decades and provides good effectiveness in increasing oil recovery and can reduce the amount of injection fluid injected into the reservoir. Seeing the success of polymer flooding in increasing oil recovery, it is necessary to know the parameters that influence the success of polymer flooding so that it can be evaluated and taken into consideration in creating a new scheme to increase oil recovery with polymer flooding. The parameters tested in this study include Injection Rate, Injection Time, Injection Pressure, Adsorption, Inaccessible Pore Volume, Residual Resistance Factor. This research uses the X-Gardient Boosting Algorithm to look at the most influential parameters in polymer flooding. The parameters that most influence the performance of polymer flooding on the value of oil recovery with the importance level of each parameter in this study are injection time of 0.452632, injection rate of 0.430075, injection pressure of 0.064662, Adsorption of 0.025564, RRF of 0.021053, IPV of 0.006014 and produce accurate predictive modeling using x-gradient boosting where with 3 variations of the comparison ratio of training and testing data obtained at a ratio of 0.7 : 0.3 obtained an R2 train of 0.9886 and an R2 test of 0.9645, a ratio of 0.8 : 0.2 obtained an R2 train of 0.9891 and an R2 test of 0.9579, and a ratio of 0.9: 0.1 obtained R2 train of 0.9890 and R2 test of 0.9660.
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