AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025370287
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ORIGINAL RESEARCH ARTICLE

Neural network-based prediction of drilling fluid leakage

Wenbing Wu1,2 ,  Tao Liu2 ,  Lianlu Huang2 ,  Jian Wang1* ,  Chenxin Wang1* ,  Hua Li2 ,  Justine Kiiza1,3 ,  Moussa Camara4 ,  Jie Zhong1* ,  Jiafang Xu1,5*
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1 Department of Marine Oil, Gas and Hydrate, School of Petroleum Engineering, China University of Petroleum (Qingdao Campus), Qingdao, Shandong, China
2 Drilling Company, China National Petroleum Corporation Offshore Engineering Company Limited, Tianjin, China
3 College of Natural Sciences, Makerere University, Kampala, Uganda
4 School of Chemistry, Petroleum and Energy, Institut National Polytechnique Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
5 State Key Laboratory of Deep Oil and Gas, China University of Petroleum (Qingdao Campus), Qingdao, Shandong, China
AJWEP, 025370287 https://doi.org/10.36922/AJWEP025370287
Received: 8 September 2025 | Revised: 25 September 2025 | Accepted: 30 October 2025 | Published online: 19 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

During the drilling process, reservoir fractures may lead to drilling-fluid loss, thereby slowing drilling progress and reducing well productivity. Therefore, it is necessary to choose the appropriate materials and formulations for plugging, and the leakage amount and rate are the most important indicators for selecting plugging agents. In this study, the amount of rigid mineral particles and plant fibers commonly used in drilling, as well as the width of formation fractures, were used as input variables, while leakage volume served as the output variable. By combining the multiple-population genetic algorithms (MPGA) and the backpropagation neural network (BPNN), an MPGA–BPNN prediction model was established to predict the leakage amount under different plugging formulations. The results showed that the correlation coefficient of the established prediction model reached 0.9741, indicating strong predictive accuracy for leakage volume and plugging performance under varying formulation conditions, providing useful reference and guidance for the optimization of plugging agents.

Graphical abstract
Keywords
Drilling fluid
Lost circulation control and plugging
Leakage amount
Neural networks
Multi-population genetic algorithm
Funding
This research was supported by the Fundamental Research Funds for the Central Universities (grant number: 24CX02013A) and the Science and Technology Project of China National Petroleum Corporation Offshore Engineering Company Limited (grant number: 202303-0101).
Conflict of interest
Wenbing Wu, Tao Liu, Lianlu Huang, and Hua Li were employed by the Drilling Company, China National Petroleum Corporation (CNPC) Offshore Engineering Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The Drilling Company, CNPC Offshore Engineering Company Limited, had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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