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相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jun 8, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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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
PubMed
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.

关键词:
数字钻探数字钻探定向钻探是指向性的钻探.机器学习 机器学习泥土发动机 泥土发动机这就是为什么RSS是RSSRSSRSS.XGBoost回归可以实现.

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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.