通过采用机器学习技术模拟井头窒息的液体速率
Mohammad-Saber Dabiri1, Fahimeh Hadavimoghaddam2, Sefatallah Ashoorian3
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. m.s_dabiri97@yahoo.com.
Scientific reports
|March 24, 2024
概括
准确预测生产井中的流体流速对于碳化合物回收至关重要. 这项研究引入了数据驱动模型和新的相关性,Adaboost-SVR在预测井头塞的流量方面表现出卓越的性能.
科学领域:
- 石油工程是石油工程中的一个.
- 流体动力学 流体动力学
- 机器学习应用 机器学习应用
背景情况:
- 在生产井中精确预测流体流速对于优化碳化合物回收和确保稳定的流动模式至关重要.
- 井头堵塞有很大影响流量,使得它们的准确建模对于生产管理至关重要.
研究的目的:
- 开发和评估数据驱动模型和新的实证相关性,用于预测通过井头塞的流体流量.
- 将拟议模型的性能与现有的相关性进行比较,并分析流速对关键参数的敏感性.
主要方法:
- 利用数据驱动的方法,包括自适应提升支持向量回归 (Adaboost-SVR),多变量自适应回归分线 (MARS),辐射基函数 (RBF) 和多层感知子 (MLP).
- 根据井头压力 (Pwh),气流比率 (GLR) 和窒息尺寸 (Dc) 开发了一个新的经验相关性.
- 使用565个数据点的数据集评估模型性能,并将结果与已确定的相关性进行比较.
主要成果:
- 阿达布斯特-SVR模型显示了最高的准确性,平均绝对百分比相对误差 (AAPRE) 为5.15%,相关系数为0.9784.
- 开发的相关性在预测准确性方面超过了以前的经验模型.
- 灵敏度分析表明,塞尺寸对流动率的影响最大,而Pwh和GLR的影响较小.
结论:
- 拟议的数据驱动模型,特别是Adaboost-SVR和开发的相关性,为预测井头窒息流量提供了更高的准确性.
- 准确的流量预测可以提高碳化合物回收和生产管理策略.
- 该研究确定了影响流速的关键参数,有助于更好地优化井井性能.
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