井下作业数据中心数据质量全程监控模型研究

发布时间：2019-01-03 18:56

【摘要】：在很大程度上讲，石油企业属于知识密集型企业，数据作为企业的重要资产之一必须是高质量的，只有高质量的数据才能用于企业生产、管理决策以及科学研究，而垃圾数据对于企业没有任何价值。如何提高并保障企业的数据质量一直是国际与国内研究的一个热点问题，提高企业的数据质量不仅是技术层面要解决的问题，同时，在管理层面上也显得特别重要。因此，只有从技术与管理两个方面同时入手，才能从根本上解决企业的数据质量问题。首先，，本文从油田井下作业数据中心中的数据质量管理需求出发，从技术层面对井下作业数据中心中的数据质量约束规则进行研究，并定义出十三种数据质量约束规则，依据这些约束规则采用元数据技术实现了对井下作业数据中心中的数据项进行约束规则的定义。接着，为实现对数据中心数据质量的评估，定义了七类数据质量评估指标，采用元数据技术建立了用于描述与存储这些评估指标的元数据模型。其次，从管理层面出发，分析并研究了从数据采集、数据审核到质量评估等环节，构建了对数据质量进行监控的流程及相关元数据模型，实现从管理层面对数据质理的管理与监控。然后，构建了数据质量全程监控体系结构模型，该体系结构包括数据层、元数据层、质量监控层和展示层，每层定义了各自详细的功能。通过该体系结构模型将数据质量监控分布到数据处理的三个阶段中，其中采集阶段，利用数据质量约束规则来对数据进行监督；审核阶段，对数据进行审核，并针对数值型的数据进行异常数据的检测，提高了审核的准确率；质量评估阶段，对进入数据中心的数据利用数据质量评估指标进行质量评估，并得出质量分析评估结果。最后，利用所研究的成果，并结合井下作业数据中心质量监控的需求设计并开发了数据质量监控系统，该系统不仅实现了对数据中心中数据质量的全程监控，而且还实现了对其中的数据质量的评估。该系统在大庆油田井下作业分公司信息中心项目中得到初步应用，结果表明，该系统具有良好的适用性。
[Abstract]:To a large extent, petroleum enterprises are knowledge-intensive enterprises. As one of the important assets of enterprises, data must be of high quality. Only high-quality data can be used in enterprise production, management decisions and scientific research. Garbage data is of no value to the enterprise. How to improve and guarantee the data quality of enterprises has always been a hot issue in international and domestic research. Improving the data quality of enterprises is not only a technical problem to be solved, but also especially important in the management level. Therefore, only from the two aspects of technology and management at the same time, we can fundamentally solve the problem of data quality. First of all, according to the requirement of data quality management in the data center of downhole operation in oil field, this paper studies the constraint rules of data quality in the data center of downhole operation from the technical level, and defines thirteen kinds of constraint rules of data quality. According to these constraint rules, metadata technology is used to define the data items in the data center of underground operation. Then, in order to evaluate the data quality of the data center, seven kinds of data quality evaluation indexes are defined, and the metadata model used to describe and store these evaluation indexes is established by using metadata technology. Secondly, from the management level, this paper analyzes and studies the links from data collection, data audit to quality assessment, and constructs the process of monitoring data quality and related metadata model. To realize the management and monitoring of data quality from the management level. Then, a data quality monitoring architecture model is constructed. The architecture includes data layer, metadata layer, quality monitoring layer and presentation layer, each layer defines its own detailed functions. The data quality monitoring is distributed into three stages of data processing through the architecture model, in which the data quality constraint rules are used to supervise the data. In the audit stage, the data is audited, and the abnormal data is detected according to the numerical data, which improves the accuracy of the audit. In the stage of quality assessment, the data entering the data center are evaluated by using the data quality evaluation index, and the results of quality analysis and evaluation are obtained. Finally, a data quality monitoring system is designed and developed, which not only realizes the whole process of monitoring the data quality in the data center, but also combines with the requirement of the quality control of the data center. It also realizes the evaluation of the data quality. The system has been applied in the information center project of Daqing oil field downhole operation branch. The results show that the system has good applicability.
【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP308;TP311.13

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