基于数据驱动的方法,对狭窄的砂岩气库中的水平井的初始生产预测
Jian Sun1,2, Jianwen Gao3, Kang Tang3
1College of Petroleum Engineering, Xi'an Shiyou University, Xi'an, 710065, Shanxi, China. xjkelsj@163.com.
Scientific reports
|August 4, 2025
概括
机器学习准确地预测了用于紧密的砂岩气库的水平井的初始产量. 这种方法通过克服传统方法的局限性来加强水库管理和开发规划.
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
- 储水池的表征 储水池的表征
背景情况:
- 准确的水平井初始产量预测对于紧密的砂岩气库至关重要.
- 传统的预测方法受到水库异质性和不利的石化物理性质的限制.
- 机器学习为提高预测准确性提供了一个有希望的替代方案.
研究的目的:
- 开发和验证机器学习模型,用于预测水平井的初始产量,目标是紧密的砂岩气库 (IPHTSG).
- 为IPHTSG预测系统地分析工程和生产参数.
- 为紧密气体储管理提供数据智能决策框架.
主要方法:
- 通过编译工程和生产参数建立了一个IPHTSG数据库.
- 通过相关性分析和通过网格搜索和10倍交叉验证优化模型参数来减少数据维度.
- 采用并比较了六种机器学习算法,其中XGBoost因其卓越的性能而被选中.
主要成果:
- XGBoost模型实现了高精度 (95%的训练,93.33%的测试) 和强大的性能指标 (精度,回忆,F1分数).
- 开发的模型在预测IPHTSG方面表现出可靠性.
- 该研究确定了关键特征参数,包括有效水库长度,垂直厚度和开放流量容量.
结论:
- 机器学习,特别是XGBoost模型,为IPHTSG预测提供了一种有效可靠的方法.
- 这种方法克服了异质密封气体储库中的传统方法的局限性.
- 该方法提供了一个可复制的模板,用于优化开发计划和生产参数的数据驱动决策.
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