在水泛滥期间对水库流量场的描述:数据挖掘和模糊逻辑方法
Haicheng Liu1,2, Shikai Tong3, Weiyao Zhu4
1School of Energy Resource, China University of Geosciences (Beijing), Beijing 100083, China.
ACS omega
|June 16, 2025
概括
本研究引入了一个定量框架来表征水库流量场,确定了石油流量和压力等关键因素. 它通过提供标准化的标准和优化生产策略,增强了水淹没中的石油回收.
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
- 模糊的逻辑 模糊的逻辑
背景情况:
- 准确的水库流量场表征对于增强水淹水时的石油回收至关重要.
- 现有的方法缺乏标准化的标准来选择流场特征的有影响因素.
研究的目的:
- 引入一个新的定量框架,将数据挖掘和模糊逻辑集成为水库流域特征.
- 解决目前选择影响因素的方法缺乏标准化标准的问题.
主要方法:
- 主要组件分析 (PCA) 和斯皮尔曼等级相关系数用于确定关键指数.
- 一种双重的主观-客观权重策略 (分析层次过程和权重) 用于全面的指数权重.
- 模糊逻辑和K-means集群用于定量表征和流场分类.
主要成果:
- PCA和斯皮尔曼相关性确定了油表面流量,压力,油和透性作为关键因素.
- 动态因素的权重比静态因素 (0.167) 高 (0.833).
- K-means集群划分了三种流域类型,其轮系数为0.724.
结论:
- 数据驱动的框架提供了一个标准化,定量化的方法来表征水库流量场.
- 该方法提供了可操作的见解,以优化成熟,高水切割油库的生产策略.
- 这种方法提高了水淹水操作中石油回收的效率.
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