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 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on the Timing for Subsequent Water Flooding in Alkali-Surfactant- Polymer Flooding in Daqing Oilfield Based on Automated Machine Learning Wenchao Jiang, Zhaowei Hou, Shichun Yao, Xiaolin Wu, Jian Gai, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4810499/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Whether the timing of subsequent water flooding in ASP flooding is reasonable or not directly determines the technical and economic benefit of the block. In order to carry out the research on the reasonable timing for subsequent water flooding, 8 evaluation parameters affecting the technical and economic benefits of ASP flooding blocks were selected, and the selection range of each evaluation parameter was determined according to the completed blocks and actual data. The optimized timing for subsequent water flooding was obtained based on the change of the cumulative net profit in the whole process of ASP flooding. On this basis, using machine learning technology, an optimization design model of reasonable timing for subsequent water flooding was established. The work efficiency was greatly improved, while the timeliness and reliability were guaranteed. The model was used to calculate the optimized timing for the subsequent water flooding of 6 blocks which were in the chemical flooding slug in Daqing oilfield. The injection scheme such as extending slug was adjusted according to the optimization results. The result shows that the cumulative net profit of these block increased by 224.9 million CNY than the original scheme, the predicted final increase will be 752.1 million CNY. Physical sciences/Engineering/Chemical engineering Physical sciences/Mathematics and computing/Computer science Physical sciences/Energy science and technology/Fossil fuels Daqing oilfield Subsequent water flooding ASP flooding Whole process economic evaluation Automatic machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files Dataandcodeusedinthepaper.rar Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviews received at journal 25 Sep, 2024 Reviews received at journal 14 Sep, 2024 Reviewers agreed at journal 07 Sep, 2024 Reviewers agreed at journal 05 Sep, 2024 Reviewers invited by journal 20 Aug, 2024 Editor assigned by journal 20 Aug, 2024 Editor invited by journal 09 Aug, 2024 Submission checks completed at journal 07 Aug, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4810499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347868602,"identity":"3820ebe3-6e1e-4169-831f-6daedc96b859","order_by":0,"name":"Wenchao Jiang","email":"","orcid":"","institution":"Petrochina Daqing Oilfield Company Limited","correspondingAuthor":false,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Jiang","suffix":""},{"id":347868603,"identity":"5c38d719-c5bd-4863-8e0e-9342abf5776a","order_by":1,"name":"Zhaowei Hou","email":"","orcid":"","institution":"Petrochina Daqing Oilfield Company 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In order to carry out the research on the reasonable timing for subsequent water flooding, 8 evaluation parameters affecting the technical and economic benefits of ASP flooding blocks were selected, and the selection range of each evaluation parameter was determined according to the completed blocks and actual data. The optimized timing for subsequent water flooding was obtained based on the change of the cumulative net profit in the whole process of ASP flooding. On this basis, using machine learning technology, an optimization design model of reasonable timing for subsequent water flooding was established. The work efficiency was greatly improved, while the timeliness and reliability were guaranteed. The model was used to calculate the optimized timing for the subsequent water flooding of 6 blocks which were in the chemical flooding slug in Daqing oilfield. The injection scheme such as extending slug was adjusted according to the optimization results. 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