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使用机器学习方法解和预测天然气偏差因子
Shaoyang Geng1, Shuo Zhai1, Jianwen Ye2
1Chengdu University of Technology, College of Energy, Chengdu, 610059, China.
Scientific reports
|September 16, 2024
概括
预测天然气偏差因子 (Z-因子) 通过一种新的机器学习框架得到了改进. 这种混合模型将信号分解与传统算法相结合,提高了储备估计和管道运输的准确性.
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.
- 数据科学数据科学数据科学
背景情况:
- 准确的天然气偏差因子 (Z-因子) 预测对于储量估计,储量回收和管道运输至关重要.
- 传统的机器学习模型在不同的气体成分和条件上难以进行概括,从而限制了预测准确度.
研究的目的:
- 开发一个高效的机器学习框架,用于准确地预测天然气的Z因素.
- 增强Z因素预测模型的稳定性和概括能力.
主要方法:
- 提出了一种混合方法,利用信号分解算法 (VMD,EFD,EEMD) 来将Z因子解成组件.
- 应用传统的机器学习算法 (SVM,XGBoost,LightGBM,ANN,BiLSTM) 来预测每个分解的组件.
- 评估了分解方法和组件数对模型性能的影响,SVM和VMD显示最佳结果.
主要成果:
- 拟议的框架显著提高了跨多个传统机器学习算法的预测准确性.
- 在10个不同的数据集中实现了平均相关系数>0.99和平均绝对百分比误差<0.83%.
- 与没有分解的模型相比,表现出优越的性能,特别是在不同的气体成分和热力学条件下.
结论:
- 混合机器学习框架为精确的天然气Z因子预测提供了统一而强大的解决方案.
- 这一进步有利于天然气行业,因为它改善了资源估计和储库管理.
- 该方法有效地捕捉了Z因子的变化,使得在各种操作场景中能够进行可靠的预测.
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