使用可靠的基于树的机器学习算法,模拟表面活性剂-碳化合物系统的界面张力
Ali Rashidi-Khaniabadi1, Elham Rashidi-Khaniabadi2, Behnam Amiri-Ramsheh3
1Department of Petroleum Engineering, EOR Research Center, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Scientific reports
|July 5, 2023
概括
预测界面张力 (IFT) 对于增强石油回收 (EOR) 至关重要. 基于树的机器学习模型准确地估计了IFT,梯度增强回归树 (GBRT) 显示出最佳性能.
科学领域:
- 石油工程是石油工程中的一个.
- 化学工程是化学工程的重要组成部分.
- 机器学习 机器学习
背景情况:
- 界面张力 (IFT) 是石油工程中增强石油回收 (EOR) 操作的关键参数.
- 实验室测量IFT是劳动密集型和昂贵的,需要替代估计方法.
- 先进的智能技术为准确的IFT预测提供了一个可行的替代方案.
研究的目的:
- 使用基于树的机器学习算法,预测表面活性剂和碳化合物之间的界面张力 (IFT).
- 评估决策树 (DT),额外树 (ET) 和梯度增强回归树 (GBRT) 模型的性能.
- 通过敏感性分析确定影响IFT的关键输入参数.
主要方法:
- 从以前的研究中利用了390个实验数据点进行模型培训和验证.
- 采用DT,ET和GBRT算法,输入包括温度,分子量,表面活性剂度,HLB和PIT.
- 使用统计指标 (例如,R平方,平均绝对相对误差) 和图形方法评估模型性能.
主要成果:
- GBRT实现了最高的预测准确度,平均绝对相对误差为2.71%,R平方值为0.9939.
- DT和ET模型也表现出强的性能,分别有4.12%和3.52%的误差.
- 灵敏度分析表明,相逆转温度 (PIT),表面活性剂度和水-脂平衡 (HLB) 显著影响IFT.
结论:
- 基于树的机器学习模型,特别是GBRT,为在EOR应用中估计IFT提供了准确和高效的替代方案.
- 开发的模型可以通过高精度预测IFT来帮助优化EOR策略.
- 了解PIT,表面活性剂度和HLB等参数的影响对于控制IFT和增强石油回收至关重要.
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