發(fā)布時(shí)間：2019-01-03 18:56

【摘要】：在很大程度上講，石油企業(yè)屬于知識(shí)密集型企業(yè)，數(shù)據(jù)作為企業(yè)的重要資產(chǎn)之一必須是高質(zhì)量的，只有高質(zhì)量的數(shù)據(jù)才能用于企業(yè)生產(chǎn)、管理決策以及科學(xué)研究，而垃圾數(shù)據(jù)對于企業(yè)沒有任何價(jià)值。如何提高并保障企業(yè)的數(shù)據(jù)質(zhì)量一直是國際與國內(nèi)研究的一個(gè)熱點(diǎn)問題，提高企業(yè)的數(shù)據(jù)質(zhì)量不僅是技術(shù)層面要解決的問題，同時(shí)，在管理層面上也顯得特別重要。因此，只有從技術(shù)與管理兩個(gè)方面同時(shí)入手，才能從根本上解決企業(yè)的數(shù)據(jù)質(zhì)量問題。 首先，，本文從油田井下作業(yè)數(shù)據(jù)中心中的數(shù)據(jù)質(zhì)量管理需求出發(fā)，從技術(shù)層面對井下作業(yè)數(shù)據(jù)中心中的數(shù)據(jù)質(zhì)量約束規(guī)則進(jìn)行研究，并定義出十三種數(shù)據(jù)質(zhì)量約束規(guī)則，依據(jù)這些約束規(guī)則采用元數(shù)據(jù)技術(shù)實(shí)現(xiàn)了對井下作業(yè)數(shù)據(jù)中心中的數(shù)據(jù)項(xiàng)進(jìn)行約束規(guī)則的定義。接著，為實(shí)現(xiàn)對數(shù)據(jù)中心數(shù)據(jù)質(zhì)量的評估，定義了七類數(shù)據(jù)質(zhì)量評估指標(biāo)，采用元數(shù)據(jù)技術(shù)建立了用于描述與存儲(chǔ)這些評估指標(biāo)的元數(shù)據(jù)模型。 其次，從管理層面出發(fā)，分析并研究了從數(shù)據(jù)采集、數(shù)據(jù)審核到質(zhì)量評估等環(huán)節(jié)，構(gòu)建了對數(shù)據(jù)質(zhì)量進(jìn)行監(jiān)控的流程及相關(guān)元數(shù)據(jù)模型，實(shí)現(xiàn)從管理層面對數(shù)據(jù)質(zhì)理的管理與監(jiān)控。 然后，構(gòu)建了數(shù)據(jù)質(zhì)量全程監(jiān)控體系結(jié)構(gòu)模型，該體系結(jié)構(gòu)包括數(shù)據(jù)層、元數(shù)據(jù)層、質(zhì)量監(jiān)控層和展示層，每層定義了各自詳細(xì)的功能。通過該體系結(jié)構(gòu)模型將數(shù)據(jù)質(zhì)量監(jiān)控分布到數(shù)據(jù)處理的三個(gè)階段中，其中采集階段，利用數(shù)據(jù)質(zhì)量約束規(guī)則來對數(shù)據(jù)進(jìn)行監(jiān)督；審核階段，對數(shù)據(jù)進(jìn)行審核，并針對數(shù)值型的數(shù)據(jù)進(jìn)行異常數(shù)據(jù)的檢測，提高了審核的準(zhǔn)確率；質(zhì)量評估階段，對進(jìn)入數(shù)據(jù)中心的數(shù)據(jù)利用數(shù)據(jù)質(zhì)量評估指標(biāo)進(jìn)行質(zhì)量評估，并得出質(zhì)量分析評估結(jié)果。 最后，利用所研究的成果，并結(jié)合井下作業(yè)數(shù)據(jù)中心質(zhì)量監(jiān)控的需求設(shè)計(jì)并開發(fā)了數(shù)據(jù)質(zhì)量監(jiān)控系統(tǒng)，該系統(tǒng)不僅實(shí)現(xiàn)了對數(shù)據(jù)中心中數(shù)據(jù)質(zhì)量的全程監(jiān)控，而且還實(shí)現(xiàn)了對其中的數(shù)據(jù)質(zhì)量的評估。該系統(tǒng)在大慶油田井下作業(yè)分公司信息中心項(xiàng)目中得到初步應(yīng)用，結(jié)果表明，該系統(tǒng)具有良好的適用性。
[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.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP308;TP311.13

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