一个受监督的机器学习模型来选择一个具有成本效益的定向钻井工具
Muhammad Nour1, Said K Elsayed1, Omar Mahmoud2
1Department of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez, 11252, Egypt.
Scientific reports
|November 4, 2024
概括
使用机器学习优化定向钻井工具选择可以显著降低现场开发成本. XGBoost模型准确地预测了截面时间和成本,并考虑了非常具体的因素来减少人类偏差.
科学领域:
- 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
- 机器学习 机器学习
背景情况:
- 在石油和天然气行业,定向钻探至关重要,需要高效的规划和运营优化.
- 选择合适的定向钻井工具,如旋转可引导系统 (RSS) 或正位移电机 (PDM),是成本效益的关键.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以自动化最佳选择定向钻井工具.
- 根据历史的偏移井数据,预测新井的钻井段时间和成本.
主要方法:
- 利用历史的偏移井数据,包括石质学,定向,钻井性能,触发和外运行信息.
- 开发并测试了各种ML算法,XGBoost被确定为最准确的预测器.
- 该模型的设计是为了考虑形成厚度和钻井环境的变化.
主要成果:
- 与其他算法相比,XGBoost ML模型在预测截面时间和成本方面表现出卓越的准确性.
- 该模型成功地根据特定的井因素调整了工具建议,表明没有普遍偏好RSS或PDM.
- 数据驱动的方法有效地减少了人类在决策中的偏见.
结论:
- 机器学习为优化定向钻井工具选择提供了强大的数据驱动方法.
- 工具的选择高度依赖于具体的地质和操作因素,而不是一种适合所有人的解决方案.
- 实施这种方法可以大幅降低现场开发成本,特别是在广泛的钻探活动中.
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