在钻井操作中失去循环强度的表征:利用机器学习和井日志数据.
1Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Heliyon
|January 6, 2025
概括
机器学习模型使用井日志数据准确预测流通强度的损失. 随机森林和硬投票等组合方法显示出高预测性能,提高钻探安全性和减少非生产性时间.
科学领域:
- 地质科学 地质科学
- 石油工程是石油工程中的一个.
- 数据科学数据科学数据科学
背景情况:
- 在钻井过程中,流失的循环带来了重大的财务和运营风险.
- 地质参数,特别是在具有挑战性的构成中,是流失流通事件的主要驱动因素.
研究的目的:
- 开发和评估机器学习模型,用于预测失去的循环强度.
- 为了利用井日志数据来增强流失事件的预测.
主要方法:
- 应用了七个机器学习算法:随机森林,额外树,决策树,XGBoost,k-最近邻居,支持矢量机,和硬投票.
- 从伊朗北部的一个天然气田中利用了九个井日志,将流失的循环分为六个强度类别.
- 进行了严格的探索性数据分析和预处理.
主要成果:
- 随机森林,额外的树木和硬投票在预测流失的循环强度方面表现优异.
- 额外的树和硬投票模型表现出非常高的预测准确度.
- 集合方法在处理问题的多变量性质方面被证明是有效的.
结论:
- 机器学习与井日志数据的整合为实时钻井决策提供了坚实的基础.
- 开发的模型有可能显著降低运营风险,提高钻探安全,并最大限度地减少非生产性时间.
- 集体技术,如硬投票,对于石油运营中的复杂预测任务是非常有效的.
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