| Issue |
E3S Web Conf.
Volume 358, 2022
5th International Conference on Green Energy and Sustainable Development (GESD 2022)
|
|
|---|---|---|
| Article Number | 01018 | |
| Number of page(s) | 5 | |
| Section | Invited Contributions | |
| DOI | https://doi.org/10.1051/e3sconf/202235801018 | |
| Published online | 27 October 2022 | |
Study on intelligent analysis algorithm for achieving standard of polymer flooding well group
No.1 Oil Production Plant of Daqing Oilfield Co., LTD, Daqing 163111, Heilongjiang, China
In order to master the development effect of polymer flooding well group, it is necessary to accurately analyze the influence of different factors in the whole polymer flooding process on the development index. Combined with the principle of big data analysis, based on the neighborhood rough set theory and Kmeans clustering algorithm, an intelligent analysis algorithm is proposed to determine the achievement of development indicators of polymer flooding well group. Firstly, the neighborhood rough set was used to reduce the attributes of the influencing factors of the Wells with and without the standards. Secondly, Kmeans algorithm was used to cluster the reduced influencing factors to delete the data inconsistent with the actual compliance. Finally, the clustering model is used to judge the standard status of other well groups, and the practical application effect is very good.
Key words: Polyflooding well group / Rough set / Kmeans / Intelligent analysis
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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