权力法委员会机器的应用将五种机器学习算法结合起来,用于增强的石油回收选
Reza Yousefzadeh1, Alireza Kazemi2, Rashid S Al-Maamari1
1Department of Petroleum and Chemical Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman.
Scientific reports
|April 22, 2024
概括
一种新的权力法委员会机器 (PLCM) 方法有效地解决了增强石油回收 (EOR) 选中的阶级不平衡. 这种方法结合了多个机器学习模型,显著提高了EOR技术的预测准确性.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
- 数据科学数据科学数据科学
背景情况:
- 对增强石油回收 (EOR) 技术的选面临着由于类不平衡的挑战,每个EOR方法的实例数量差异很大.
- 这种不平衡阻碍了用于预测特定水库适合的EOR技术的数据驱动模型的性能和概括.
- 现有的方法很难有效地处理EOR技术数据的不平等分布,影响预测准确度.
研究的目的:
- 提出和评估一种新的方法,即通过粒子优化 (PSO) 优化的权力法律委员会机器 (PLCM),以克服EOR选中的阶级不平衡.
- 整合各种机器学习模型,包括人工神经网络 (ANN),CatBoost,随机森林 (RF),K-最近邻居 (KNN) 和支持矢量机器 (SVM),以提高预测.
- 证明PLCM技术在提高EOR查模型的准确性方面的有效性,特别是在存在不平衡数据集的情况下.
主要方法:
- 编制了2563个全球成功的EOR经验的综合数据集,以确保稳健的模型概括并防止过度拟合.
- 使用了五种不同的机器学习算法 (ANN,CatBoost,RF,KNN,SVM),并使用五倍交叉验证调整了超参数.
- 通过粒子优化 (PSO) 优化的权力法委员会机器 (PLCM) 技术被用来结合单个模型的预测.
主要成果:
- 单个机器学习模型在未见病例中获得了0.868的平均预测得分,KNN (0.894) 和SVM (0.892) 显示了最高的性能.
- 使用PLCM方法整合这些模型导致预测准确度大幅提高,得分为0.963.
- 特性重要性分析确定了油的重力和形成孔隙性是影响EOR查结果的最关键参数.
结论:
- 权力法律委员会机器 (PLCM) 技术通过协同结合各种数据驱动模型,有效地解决了EOR选中的阶级失衡问题.
- 拟议的PLCM-PSO方法在预测合适的EOR技术方面取得了重大进展,优于单个机器学习模型.
- 石油的重力和形成的孔隙性是应优先考虑的关键因素,在水库的特征有效的EOR方法选择.
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