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Erschienen in:

2024 | OriginalPaper | Buchkapitel

Oriented Oilfield Structured Data Quality Assessment Model

verfasst von : Xue-song Su, Wang Mei, Hui-fang Song, Jia Liu, Shan Huang

Erschienen in: Proceedings of the International Field Exploration and Development Conference 2023

Verlag: Springer Nature Singapore

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Abstract

Since the utilization of data analytics in oil field industry, data mining has become increasingly important. Various decision-making algorithms derived from data are closely related to the quality of data, which makes data quality assessment an indispensable part of the intelligent construction of oilfield. General data quality assessment models are not suitable for centralized oilfield scenarios because the quality of datasets depends on their usage rather than a simple stacking of individual data units. For example, datasets containing data units with good quality yet serious homogeneity cannot meet the data requirements in deep learning. This paper is based on the theoretical model of process measurement and adopts the second-level fuzzy comprehensive evaluation model. We calculate the member-ship degree of each factor set based on the business demand by the AHP. The oriented oilfield structured data quality assessment model is then established. This model provides theoretical basis and technical support for oilfield data preprocessing, decision-making and staged evaluation of data governance.

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Titel: Oriented Oilfield Structured Data Quality Assessment Model
verfasst von: Xue-song Su
Wang Mei
Hui-fang Song
Jia Liu
Shan Huang
Verlag: Springer Nature Singapore
Buch: Proceedings of the International Field Exploration and Development Conference 2023
Print ISBN: 978-981-9702-71-8

Electronic ISBN: 978-981-9702-72-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-0272-5_28