开发一种成本效益高的工具,用于在次临界油井中使用油井头数据预测压缩流量
Zhiwei Xun1, Farag M A Altalbawy2, Prakash Kanjariya3
1China University of Geosciences (Beijing), Beijing, 100083, China. Zhiwei0914@outlook.com.
Scientific reports
|July 16, 2025
概括
机器学习模型准确地预测了井头塞的石油生产率,使用塞大小和井头压力作为关键因素. 这种数据驱动的方法提高了石油和天然气生产的优化.
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:
- 准确预测石油生产速度对于优化原油产量和石油行业运营效率至关重要.
- 井头窒息器显著影响流量性能,使其准确的建模对于生产管理至关重要.
研究的目的:
- 开发和优化机器学习 (ML) 模型,通过井头塞来预测石油产量.
- 确定影响干流性能的关键参数,并提供可解释的预测框架.
主要方法:
- 利用来自中东石油生产设施的综合数据集,包括GOR,窒息尺寸,BS&W,THP和API等参数.
- 使用蒙特卡罗异常检测 (MCOD) 应用了强大的数据预处理和训练的梯度增强机 (GBM) 模型,使用198个数据点进行5倍交叉验证.
- 优化GBM模型使用先进的算法,包括自适应差分进化 (SADE),进化策略 (ES),贝叶斯概率改进 (BPI) 和批量贝叶斯优化 (BBO).
主要成果:
- 自适应差分进化 (SADE) 算法在优化梯度增强机 (GBM) 模型方面表现出卓越的性能.
- 关键性能指标,如平均绝对相对误差 (AARE%),R平方 (R2) 和平均平方误差 (MSE) 用于模型评估.
- 沙普 (沙普利添加式测量) 分析显示,口大小和井头压力 (THP) 是预测石油产量速度最有影响力的参数.
结论:
- 开发了一个数据驱动的框架,通过井头窒息来准确和可解释地预测石油产量.
- 该研究强调了管大小和THP的重大影响,为生产优化提供了有价值的见解.
- 拟议的ML方法为提高石油和天然气行业运营效率提供了强大的解决方案.
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