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相关概念视频

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

40
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
40
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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相关实验视频

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Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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使用深度学习的泥损失数据估计形成透率.

Yaser Abdollahfard1, Seyed Morteza Mirabbasi1, Mohammad Ahmadi2

  • 1Petroleum Engineering Department, Amirkabir University of Technology, Tehran, Iran.

Scientific reports
|April 30, 2025
PubMed
概括

这项研究引入了一种使用泥损失数据和深度学习来估计水库透性的新方法. 像1D-CNN和DJINN这样的机器学习模型可以准确地从钻探数据中预测形成的透性.

关键词:
人工智能的人工智能是人工智能.卷积神经网络 (CNN) 是一种神经网络.深度联合信息的神经网络 (DJINN)泥的损失 泥的损失透性 透性的

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科学领域:

  • 石油工程是石油工程中的一个.
  • 机器学习 机器学习
  • 地质科学是地球科学.

背景情况:

  • 透率估计对于储库评估和碳化合物开采至关重要.
  • 现有的透性评估方法可能不准确或无法使用.
  • 泥损失数据经常被忽视,是估计透性的潜在来源.

研究的目的:

  • 开发和验证一种使用泥损失数据估计形成透性的新方法.
  • 应用深度学习技术来准确预测透性.
  • 探索实时钻探数据对水库特征的实用性.

主要方法:

  • 使用水库模拟器生成的泥损失率数据,具有不同的水库和钻井参数.
  • 采用一维卷积神经网络 (1D-CNN) 进行透度估计.
  • 使用了一种新的深度联合信息神经网络 (DJINN) 模型,集成神经网络和决策树.

主要成果:

  • 1D-CNN在透率估计中获得了高准确度 (R2=0.970训练,R2=0.964测试).
  • DJINN模型的表现优于1D-CNN,显示出卓越的准确性 (R2=0.978训练,R2=0.972测试).
  • 验证了生成数据的相关系数,以确保在真实环境下可靠性.

结论:

  • 泥损失数据可以通过深度学习有效地利用,以准确估计形成的透性.
  • 与1D-CNN相比,DJINN模型为此应用提供了更准确的方法.
  • 这种方法为钻探数据提供了新的应用,使石油工程师能够改进水库设计和表征.