动态聚类下智慧油田企业多源数据整合仿真  

Simulation of Multi-Source Data Integration of Intelligent Oilfield Enterprises under Dynamic Clustering

作　　者：陈亚颐 尹权 罗李黎 戴静 CHEN Ya-yi;YIN Quan;LUO Li-li;DAI Jing(Xi'an Shiyou University,Xi'an Shaanxi 710065,China;Xinjiang Oilfield Company Zhundong Oil Production Plant,Fukang Xinjiang 831500,China)

机构地区：[1]西安石油大学,陕西西安710065 [2]新疆油田公司准东采油厂,新疆阜康831500

出　　处：《计算机仿真》2025年第3期165-169,共5页Computer Simulation

基　　金：2023年准东采油厂公共光缆维护项目(XYJL-2023-FW-062)。

摘　　要：智慧油田企业涉及多个业务领域和多个系统,每个系统都会产生大量的数据。由于数据采集、传输和存储过程中可能存在的误差和异常,导致数据质量参差不齐,且数据之间存在复杂的关联性和依赖性。为了实现不同部门、不同系统之间的数据共享和互通,打破信息孤岛,提高工作效率,提出一种基于动态聚类的多源数据整合方法。利用数据中台构建智慧油田企业数据管理平台,基于K-means聚类、模拟退火算法建立多源数据整合数学模型,经过整合模型多轮迭代运算,找出全局最优解,此解对应整合策略即为最终的整合结果。实验结果表明,所提方法动态聚类企业各业务数据整合效果良好,且轮廓系数大、Calinski-Harabasz指数高。Smart oilfield enterprises involve many business fields and systems,and each system will generate a large amount of data.Due to the possible errors and anomalies in the process of data collection,transmission and storage,the data quality is uneven,and there are complex correlations and dependencies between the data.In order to realize data sharing and intercommunication between different departments and systems,break the information island and improve work efficiency,a multi-source data integration method based on dynamic clustering is proposed.The data management platform of smart oilfield enterprise is built by using a data center,and the mathematical model of multi-source data integration is established based on K-means clustering and simulated annealing algorithm.After several rounds of iterative operation of the integration model,the global optimal solution is found,and the solution corresponding to the integration strategy is the final integration result.The experimental results show that the proposed method has a good effect on the dynamic clustering of enterprise business data integration,with a large contour coefficient and a high Calinski-Harabasz index.

关 键 词：动态聚类 智慧油田企业 多源数据整合 模拟退火 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.9[自动化与计算机技术—控制科学与工程]