在正常和恶劣条件下的甲,和天然气混合物粘度的机器学习预测
Sayed Gomaa1,2, Mohamed Abdalla3,4, Khalaf G Salem5
1Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt. ElsayedGomaa.2214@azhar.edu.eg.
Scientific reports
|July 2, 2024
概括
机器学习准确地预测了气体粘度,为天然气运营提供了比实验室实验更快,更具成本效益的替代方案. 人工神经网络和随机森林显示出卓越的性能.
科学领域:
- 石油工程是石油工程中的一个.
- 计算科学 计算科学
- 化学热力学化学热力学
背景情况:
- 准确的气体粘度估计对于天然气运营建模至关重要.
- 实验性粘度测量是耗时且昂贵的.
- 机器学习 (ML) 为预测气体粘度提供了一个有效的替代方案.
研究的目的:
- 开发和评估用于预测甲,和天然气混合物粘度的ML模型.
- 为了比较七种不同的ML技术的性能.
- 开发新的基于ANN的气体粘度相关性.
主要方法:
- 利用了超过4304个实验数据集,包括压力,温度和气体密度.
- 应用了七个ML算法:线性回归,随机森林 (RF),决策树,梯度提升,K-最近邻居,Nu-支持向量回归 (NuSVR) 和人工神经网络 (ANN).
- 使用ANN.开发了三种新的粘度相关性.
主要成果:
- 对于预测粘度,ANN,RF和梯度提升模型实现了0.99的确定系数 (R2).
- 这些模型准确地估计了甲,和天然气混合物的粘度.
- 线性回归和NuSVR在粘度预测方面表现不佳 (R2为0.07和-0.01).
结论:
- ML模型提供了一个具有成本效益和快速的工具,用于近似气体粘度.
- 开发的ANN相关性和模型为各种气体组成提供了高精度.
- ML技术显著提高了模拟天然气运营的能力.
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