TY  - JOUR
Y1  - 2024///
A1  - Hamdoon, A.
A1  - Mohammed, M.
A1  - Elraies, K.
JF  - Rudarsko Geolosko Naftni Zbornik
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193342951&doi=10.17794%2frgn.2024.2.12&partnerID=40&md5=d30949ce99a49fc2c3665585659f583c
VL  - 39
N2  - 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. © 2024, Faculty of Mining, Geology and Petroleum Engineering University of Zagreb. All rights reserved.
IS  - 2
ID  - scholars20000
KW  - Oil well logging
KW  -  Advanced modeling; Diagnostic plot; Excessive water production; Machine-learning; Oil industries; Operational cycle; Production patterns; Water production; Water-oil ratios; Well logs
KW  -  Machine learning
KW  -  environmental modeling; literature review; machine learning; oil industry; research work; well logging
PB  - Faculty of Mining, Geology and Petroleum Engineering University of Zagreb
SN  - 03534529
EP  - 163
AV  - none
N1  - cited By 0
TI  - A Review of Chan Plot Application and Recent Advanced Models for Diagnosing Excessive Water Production Pregled primjene Chanova dijagrama i nedavno razvijenih naprednih modela za utvrÄ?ivanje prekomjerne proizvodnje slojne vode
SP  - 149
ER  -