Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning

Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning

Abstract Determining the optimal timing for subsequent water flooding in Alkali-Surfactant-Polymer (ASP) flooding is essential to maximizing both the technical and economic outcomes of oilfield blocks. This study identified eight critical parameters that influence the benefits of ASP flooding and es...

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Main Authors: Wenchao Jiang, Zhaowei Hou, Shichun Yao, Xiaolin Wu, Jian Gai, Chunlin Nie, Xu Su, Shouliang Lu, Yunchao Wang, Bin Huang, Chi Dong, Di Liu, Jiang Jiang, Xiaodan Yu, Yane Wang, Jifeng Zhang, Changjiu Zhao, Erlong Yang, Xiaoru He, Guangming Qi, Jingya Li, Yuxiao Ma, Lei Zhang
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Daqing oilfield
Subsequent water flooding
ASP flooding
Whole process economic evaluation
Automatic machine learning
Online Access:https://doi.org/10.1038/s41598-024-79491-z
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https://doi.org/10.1038/s41598-024-79491-z

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