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Oriented Oilfield Structured Data Quality Assessment Model

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Proceedings of the International Field Exploration and Development Conference 2023 (IFEDC 2023)

Part of the book series: Springer Series in Geomechanics and Geoengineering ((SSGG))

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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.

Copyright 2023, IFEDC Organizing Committee

This paper was prepared for presentation at the 2023 International Field Exploration and Development Conference in Wuhan, China, 20-22 September 2023.

This paper was selected for presentation by the IFEDC Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the IFEDC Technical Team and are subject to correction by the author(s). The material does not necessarily reflect any position of the IFEDC Technical Committee its members. Papers presented at the Conference are subject to publication review by Professional Team of IFEDC Technical Committee. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of IFEDC Organizing Committee is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of IFEDC. Contact email: paper@ifedc.org.

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References

  1. Liang, W.F.: Constructed achievements and prospects of the intelligent application system for the oilfield development. Daqing Petrol. Geol. Dev. 38(5), 283–289 (2019)

    Google Scholar 

  2. Liu, S.F., Gao, W.X., Xu, W.Y.: Research and improvement of a software process measurement model. Microcomput. Inform. 26(33), 17–19 (2010)

    Google Scholar 

  3. La, Z.: A Fuzzy-set-theoretic approach to the compositionality of meaning: propositions, dispositions and canonical forms. J. Seman. 2(3–4) (1983)

    Google Scholar 

  4. Saaty, R.W.: The analytic hierarchy process—what it is and how it is used. Mathem. Model. 9(3), 161–176 (1987)

    Article  MathSciNet  Google Scholar 

  5. Chen, S.H.: Research on application of image enhancement and recognition technology based on deep learning in oilfield work area. University of Electronic Science and Technology of China (2021)

    Google Scholar 

  6. Wang, Q., Li, A.R., Ren, J.W., et al.: Exploration of deep learning technology in remaining oil prediction of Y oilfield. Surv. Mapping Geol. 4(3), 15–18 (2022)

    Google Scholar 

  7. Wang, H.L., Mu, L.X., Shi, F.G., et al.: Production prediction at ultra-high water cut stage via recurrent neural network. Petrol. Explor. Dev. 2020107 (2020)

    Google Scholar 

  8. Zhong, Y.H., Wang, S.N., Luo, L., et al.: Knowledge mining for oilfield development index prediction model using deep learning. J. Southwest Petrol. Univ. 42(6), 63 (2020)

    Google Scholar 

  9. Wang, R.Y.: A product perspective on total data quality management. Commun. ACM 41(2), 58–65 (1998)

    Article  Google Scholar 

  10. Laws, R., Gillespie, S., Puro, J., et al.: The community health applied research network (CHARN) data warehouse: a resource for patient-centered outcomes research and quality improvement in underserved, safety net populations. EGEMS 2(3) (2014)

    Google Scholar 

  11. Fang, Y.L., Yang, D.Q., Tang, S.W., et al.: Data quality managements in data warehouse. Comput. Eng. Appl. 39(13), 1–4 (2003)

    Google Scholar 

  12. Fang, Y.L., Yang, D.Q., Tang, S.W., et al.: Data schedule serialization in data transformations. Comput. Eng. Appl. 39(17), 4–6 (2003)

    Google Scholar 

  13. Liu, Z.H., Zhang, Q.L.: Research overview of big data technology. J. Zhejiang Univ. 48(6), 957–972 (2014)

    Google Scholar 

  14. Constantinescu, R., Iacob, I.M.: Capability maturity model integration. J. Appl. Quantit. Methods 2(1), 31–37 (2007)

    Google Scholar 

  15. Batini, C., Cabitza, F., Cappiello, C., et al.: A comprehensive data quality methodology for web and structured data. In: Proceedings of the 2006 1st International Conference on Digital Information Management. IEEE (2006)

    Google Scholar 

  16. Boretti, E.: AIB-ISTAT statistics: the first time for Italian public libraries. Perform. Measur. Metrics 6(1), 32–38 (2005)

    Article  Google Scholar 

  17. Joseph, K., Mmath, J.F.: Validation of perinatal data in the Discharge Abstract Database of the Canadian Institute for Health Information. Chronic Diseases Injur. Canada 29(3) (2009)

    Google Scholar 

  18. Lee, Y.W., Strong, D.M., Kahn, B.K., et al.: AIMQ: a methodology for information quality assessment. Inform. Manag. Commun. ACM 40(2), 133–146 (2002)

    Google Scholar 

  19. Marchetti, C., Mecella, M., Scannapieco, M., et al.: Data Quality in Cooperative Information Systems. Encyclopedia of Data Warehousing and Mining, pp. 297–301. IGI Global (2005) 

    Google Scholar 

  20. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM. 45(4), 211–218 (2002)

    Article  Google Scholar 

  21. Sitawati, H.D., Ruldeviyani, Y., Hidayanto, A.N., et al.: Data quality improvement: case study financial regulatory authority reporting. In: proceedings of the 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE). IEEE (2022)

    Google Scholar 

  22. Su, Z., Jin, Z.: A methodology for information quality assessment in the designing and manufacturing processes of mechanical products. Information Quality Management: Theory and Applications, pp. 190–220. IGI Global (2007)

    Google Scholar 

  23. Woodall, P., Parlikad, A.K., Lebrun, L.: Approaches to information quality management: State of the practice of uk asset-intensive organisations. Asset Condition Informat. Syst. Decision Models, 1–18 (2012)

    Google Scholar 

  24. Chen, C., Chen, H., Zhang, Y., et al.: TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 13(8), 1194–1202 (2020)

    Article  MathSciNet  Google Scholar 

  25. Liu, H.: The research on key issues of data quality management, assessment and detection in big data environment. Jilin University (2019)

    Google Scholar 

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Acknowledgments

The research is supported by Sinopec Shengli Oilfield Intelligent Engine for Oilfield Safety and Environmental Protection project under grant number YKB2302.

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Authors and Affiliations

  1. Technology Inspection Center of Shengli Oilfield, Dongying, China

    Xue-song Su, Wang Mei, Hui-fang Song, Jia Liu & Shan Huang

Authors
  1. Xue-song Su
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  2. Wang Mei
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  3. Hui-fang Song
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  4. Jia Liu
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  5. Shan Huang
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Corresponding author

Correspondence to Xue-song Su .

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Editors and Affiliations

  1. College of Petroleum Engineering, Xi'an Shiyou University, Xi’an, Shaanxi, China

    Jia'en Lin

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Su, Xs., Mei, W., Song, Hf., Liu, J., Huang, S. (2024). Oriented Oilfield Structured Data Quality Assessment Model. In: Lin, J. (eds) Proceedings of the International Field Exploration and Development Conference 2023. IFEDC 2023. Springer Series in Geomechanics and Geoengineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0272-5_28

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  • DOI: https://doi.org/10.1007/978-981-97-0272-5_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0271-8

  • Online ISBN: 978-981-97-0272-5

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