预测钻井道建设时间的机器学习方法
Arsalan Mahmoodzadeh1, Hamid Reza Nejati2, Nejib Ghazouani3
1Center of Research and Strategic Studies, Lebanese French University, Erbil, Iraq.
Scientific reports
|August 29, 2025
概括
使用机器学习 (ML) 优化预测钻井和喷气道建设时间. 高斯过程回归实现了高精度,并且一个用户友好的界面有助于实时持续时间的估计.
科学领域:
- 土木工程
- 地质工程
- 计算科学
背景情况:
- 准确预测建设时间对于有效的道项目管理至关重要.
- 钻井和爆炸道涉及复杂的地质和运营因素,影响项目时间表.
- 现有的持续时间估计方法往往缺乏动态项目调整所需的精度.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测钻井和喷气道的建造时间.
- 确定影响道建设时间表的关键参数.
- 创建一个实用的实时持续时间估计和项目监测工具.
主要方法:
- 收集了来自8个道的500个数据点,包括20个不同的参数.
- 选择特征以确定ML模型训练的17个关键参数,重点是突破和截面.
- 包括高斯过程回归在内的各种ML模型的应用和超参数调整.
- 基于机器学习的图形用户界面 (GUI) 的开发,用于实际应用.
主要成果:
- 确定突破和道截面作为对施工持续时间具有高度影响的参数.
- 高斯过程回归表现出卓越的性能,平均R平方值为0.89.
- 开发的基于机器学习的GUI能够准确的实时预测时间和动态更新.
- 超参数调整显著提高了ML模型的预测准确性.
结论:
- 机器学习技术,特别是高斯过程回归,对于预测钻井和喷气道建设时间非常有效.
- 对于准确的时间估计,关键参数如超速和横截面至关重要.
- 开发的GUI提供了一种有价值的实用工具,用于加强道工程的项目管理和实时决策.
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