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Prethodno priopćenje

https://doi.org/10.17794/rgn.2024.2.12

A REVIEW OF CHAN PLOT APPLICATION AND RECENT ADVANCED MODELS FOR DIAGNOSING EXCESSIVE WATER PRODUCTION

Ahmed Hamdoon orcid id orcid.org/0009-0007-3122-8031 ; Universiti Teknologi PETRONAS, Petroleum Engineering Department, UTP, 32610 Bandar Seri Iskandar, Perak, Malaysia
Mohammed Mohammed ; United Arab Emirates University
Khaled Elraies ; Universiti Teknologi PETRONAS, Petroleum Engineering Department, UTP, 32610 Bandar Seri Iskandar, Perak, Malaysia


Puni tekst: engleski pdf 2.989 Kb

verzije

str. 149-163

preuzimanja: 32

citiraj


Sažetak

Water is one of the major fluids associated with the operational cycle of the oil industry that must be carefully considered due to its environmental, treatment facility, and economic impacts. Over the years, various methods have been developed to identify excessive water production. These methods range from reliable and expensive ones, such as well-logging records, to less accurate methods that utilize available production and water-oil ratio data, such as the Chan plot. The Chan plot emphasizes that well production can exhibit various patterns of excessive water production, including constant water-oil ratios, normal displacement, channeling, and coning. However, manual interpretation of these plots is often confusing due to the noise present in the actual data. Machine learning models have improved interpretation accuracy, but limitations remain in detecting evolving water production patterns. This paper reviews the application of Chan plots and their integration with existing diagnostic tools for diagnosing excessive water production. It then focuses on a recent advanced model that leverages machine learning specifically designed to improve the interpretation of Chan plots. The review highlights the limitations of traditional interpretation techniques and explores how the recent advanced model can address these limitations. Additionally, the paper briefly discusses the potential of an interactive model for the continuous monitoring of water production patterns. Finally, the paper offers recommendations for future research directions.

Ključne riječi

excessive water production; water oil ratio; diagnostic plot; well log; machine learning

Hrčak ID:

316650

URI

https://hrcak.srce.hr/316650

Datum izdavanja:

28.4.2024.

Podaci na drugim jezicima: hrvatski

Posjeta: 77 *