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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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79491-z |
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| author | 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 |
| author_facet | 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 |
| author_sort | Wenchao Jiang |
| collection | DOAJ |
| description | 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 established parameter ranges based on data from completed blocks and actual field measurements. The optimal timing for subsequent water flooding was determined by evaluating cumulative net profit variations throughout the ASP flooding lifecycle. Given the complexity and high-dimensional nature of evaluating multiple parameters across diverse blocks, a machine learning-driven optimization model was developed. This model enhances work efficiency by automating complex analyses. However, predictive uncertainties and limitations remain due to the variability in oilfield development and the potential for unpredictable changes in reservoir conditions, external market factors and so on, which may affect the model’s results. The model was applied to six blocks in the Daqing oilfield currently in the chemical flooding phase, where injection schemes, such as extending the polymer slug, were adjusted according to the model’s optimized results. These adjustments yielded an increase in cumulative net profit of 224.9 million CNY compared to the original scheme, with a potential total increase of 752.1 million CNY by the end of the flooding process. |
| format | Article |
| id | doaj-art-a8810f90ba464278a5c81b854d932d7c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a8810f90ba464278a5c81b854d932d7c2024-11-17T12:30:19ZengNature PortfolioScientific Reports2045-23222024-11-0114111410.1038/s41598-024-79491-zResearch on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learningWenchao Jiang0Zhaowei Hou1Shichun Yao2Xiaolin Wu3Jian Gai4Chunlin Nie5Xu Su6Shouliang Lu7Yunchao Wang8Bin Huang9Chi Dong10Di Liu11Jiang Jiang12Xiaodan Yu13Yane Wang14Jifeng Zhang15Changjiu Zhao16Erlong Yang17Xiaoru He18Guangming Qi19Jingya Li20Yuxiao Ma21Lei Zhang22Exploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedDaqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedChongqing Institute of Unconventional Oil and Gas Development, Chongqing University of Science and TechnologyMinistry of Education Key Laboratory of Enhanced Oil and Gas Recovery, Northeast Petroleum UniversityExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedExploration and Development Research Institute, Petrochina Daqing Oilfield Company LimitedMinistry of Education Key Laboratory of Enhanced Oil and Gas Recovery, Northeast Petroleum UniversityNo. 2 Oil Production Company, Petrochina Daqing Oilfield Company LimitedNo. 2 Oil Production Company, Petrochina Daqing Oilfield Company LimitedNo. 1 Oil Production Company, Petrochina Daqing Oilfield Company LimitedToutai Oilfield Development Company Limited, Petrochina Daqing Oilfield Company LimitedDaqing Oilfield Natural Gas CompanyAbstract 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 established parameter ranges based on data from completed blocks and actual field measurements. The optimal timing for subsequent water flooding was determined by evaluating cumulative net profit variations throughout the ASP flooding lifecycle. Given the complexity and high-dimensional nature of evaluating multiple parameters across diverse blocks, a machine learning-driven optimization model was developed. This model enhances work efficiency by automating complex analyses. However, predictive uncertainties and limitations remain due to the variability in oilfield development and the potential for unpredictable changes in reservoir conditions, external market factors and so on, which may affect the model’s results. The model was applied to six blocks in the Daqing oilfield currently in the chemical flooding phase, where injection schemes, such as extending the polymer slug, were adjusted according to the model’s optimized results. These adjustments yielded an increase in cumulative net profit of 224.9 million CNY compared to the original scheme, with a potential total increase of 752.1 million CNY by the end of the flooding process.https://doi.org/10.1038/s41598-024-79491-zDaqing oilfieldSubsequent water floodingASP floodingWhole process economic evaluationAutomatic machine learning |
| spellingShingle | 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 Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning Scientific Reports Daqing oilfield Subsequent water flooding ASP flooding Whole process economic evaluation Automatic machine learning |
| title | Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning |
| title_full | Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning |
| title_fullStr | Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning |
| title_full_unstemmed | Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning |
| title_short | Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning |
| title_sort | research on the timing for subsequent water flooding in alkali surfactant polymer flooding in daqing oilfield based on automated machine learning |
| topic | Daqing oilfield Subsequent water flooding ASP flooding Whole process economic evaluation Automatic machine learning |
| url | https://doi.org/10.1038/s41598-024-79491-z |
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