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相关概念视频

Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

313
Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
313
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

415
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
415
Fluid Pressure01:14

Fluid Pressure

806
In mechanical engineering, fluid pressure plays a critical role in designing systems that utilize liquid flow, such as hydraulic systems, pumps, and valves. When designing these systems, engineers must ensure they can withstand the forces created by fluid pressure to avoid damage or failure.
According to Pascal's law, a fluid at rest will generate equal pressure in all directions. This pressure is measured as a force per unit area, and its magnitude depends on the fluid's specific...
806

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相关实验视频

Updated: Sep 17, 2025

Mechanical Expansion of Steel Tubing as a Solution to Leaky Wellbores
09:32

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Published on: November 20, 2014

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机器学习增强了使用集成井记录和泥记录的形成压力预测.

Jiwen Liang1, Ming Luo1, Wentuo Li1

  • 1Hainan Branch of CNOOC (China) Co., Ltd, Haikou, 570100, Hainan, China.

Scientific reports
|July 3, 2025
PubMed
概括

在石油工程中,准确预测高压形成压力至关重要. 使用井记录和泥记录数据的新综合模型,特别是IWM-GABP机器学习方法,显著提高了预测准确性,降低了钻井风险.

关键词:
造成异常高压的情况.准确的预测准确的预测集成日志数据集成日志数据机器学习是机器学习.孔隙压力是指孔隙压力.

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相关实验视频

Last Updated: Sep 17, 2025

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科学领域:

  • 石油工程是石油工程中的一个.
  • 地质科学地质科学是指地球科学.
  • 机器学习应用 机器学习应用

背景情况:

  • 准确预测异常高压形成压力是石油工程的一个关键挑战.
  • 不准确的预测会导致安全事故,例如由于钻井安全密度窗口狭窄而导致泄漏和爆炸.
  • 提高孔隙压力预测准确度对于安全高效的石油和天然气勘探至关重要.

研究的目的:

  • 开发一个更准确的模型来预测形成孔隙压力.
  • 为了研究各种井记录和泥记录参数和孔隙压力之间的相关性.
  • 建立一个机器学习模型,整合井记录和泥记录数据,以提高预测准确度.

主要方法:

  • 斯皮尔曼相关系数分析以确定孔隙压力和记录参数之间的关系.
  • 开发一个集成的井记录和泥记录数据 (IWM) 机器学习模型.
  • 传统模型与IWM模型的比较,特别是评估IWM-GABP (带有遗传算法的回传播神经网络) 和IWM-RBFN (辐射基函数网络) 模型.

主要成果:

  • 孔隙压力与深度,比特重量和声传输时间等参数有不同的相关性.
  • IWM-GABP模型实现了最高的预测准确度,超过96%.
  • 与传统模型相比,IWM模型的预测准确度平均提高了8.32%,显著减少了预测错误.

结论:

  • 集成的井记录和泥记录数据机器学习模型,特别是IWM-GABP,为形成压力预测提供了卓越的准确性.
  • 建议使用IWM-GABP或基于IWM的反向传播神经网络模型,而不是IWM-RBFN模型来预测形成压力.
  • 这项研究提供了一种强大的方法来提高形成压力预测的准确性,支持有效的现场油田开发.