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A hybrid AI-genetic algorithm framework for the optimization of polymer flooding strategies: a numerical simulation-based approach.

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Název: A hybrid AI-genetic algorithm framework for the optimization of polymer flooding strategies: a numerical simulation-based approach.
Autoři: Nourizadeh M; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran., Khosravi R; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran., Simjoo M; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran. simjoo@sut.ac.ir., Chahardowli M; Department of Petroleum and Geo-energy Engineering, Amirkabir University of Technology, Tehran, Iran.
Zdroj: Scientific reports [Sci Rep] 2026 Jan 19; Vol. 16 (1), pp. 3934. Date of Electronic Publication: 2026 Jan 19.
Způsob vydávání: Journal Article
Jazyk: English
Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Imprint Name(s): Original Publication: London : Nature Publishing Group, copyright 2011-
Abstrakt: Facing declining conventional resources, the oil industry requires advanced methods to maximize recovery. Polymer flooding is a key technique, but its optimization is hindered by complex parameter interactions and the high computational cost of traditional simulation. This study presents a novel solution: a hybrid AI-Genetic Algorithm (GA) framework that integrates numerical simulation with machine learning for efficient optimization. A large dataset of 960 core-scale simulation cases was generated to analyze key parameters like permeability and polymer concentration. The core innovation was the development of two neural networks, a Feedforward Neural Network (FNN) and an Elman Recurrent Neural Network (E-RNN), to act as fast proxy models. The E-RNN proved superior for forecasting dynamic production data, achieving exceptional accuracy (R² > 0.99) by effectively capturing time-dependent behaviors. This high-fidelity E-RNN proxy was then coupled with a GA for multi-objective optimization. Results showed that maximum oil recovery is achieved by maximizing permeability, injection rate, and polymer concentration while minimizing reservoir heterogeneity. Crucially, economic optimization revealed a different strategy, favoring a short, intensive injection period to maximize profit, highlighting a key technical-economic trade-off. The study successfully validated the framework's generalization capability. This work provides a powerful tool for accelerating polymer flooding design, with future efforts aimed at integrating laboratory data for calibration and scaling the application to full-field models.
(© 2026. The Author(s).)
Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References: Sci Rep. 2023 Aug 2;13(1):12505. (PMID: 37532745)
Sci Rep. 2024 Jun 8;14(1):13213. (PMID: 38851823)
Sci Rep. 2024 Nov 22;14(1):29000. (PMID: 39578498)
Langmuir. 2025 Nov 4;41(43):29180-29195. (PMID: 41137808)
Contributed Indexing: Keywords: Artificial intelligence; Elman recurrent neural network; Feedforward neural network; Genetic algorithm; Polymer flooding
Entry Date(s): Date Created: 20260119 Latest Revision: 20260201
Update Code: 20260201
PubMed Central ID: PMC12855200
DOI: 10.1038/s41598-025-33874-y
PMID: 41554792
Databáze: MEDLINE
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  Data: A hybrid AI-genetic algorithm framework for the optimization of polymer flooding strategies: a numerical simulation-based approach.
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  Data: <searchLink fieldCode="AU" term="%22Nourizadeh+M%22">Nourizadeh M</searchLink>; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.<br /><searchLink fieldCode="AU" term="%22Khosravi+R%22">Khosravi R</searchLink>; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.<br /><searchLink fieldCode="AU" term="%22Simjoo+M%22">Simjoo M</searchLink>; Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran. simjoo@sut.ac.ir.<br /><searchLink fieldCode="AU" term="%22Chahardowli+M%22">Chahardowli M</searchLink>; Department of Petroleum and Geo-energy Engineering, Amirkabir University of Technology, Tehran, Iran.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE; PubMed not MEDLINE
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  Data: Facing declining conventional resources, the oil industry requires advanced methods to maximize recovery. Polymer flooding is a key technique, but its optimization is hindered by complex parameter interactions and the high computational cost of traditional simulation. This study presents a novel solution: a hybrid AI-Genetic Algorithm (GA) framework that integrates numerical simulation with machine learning for efficient optimization. A large dataset of 960 core-scale simulation cases was generated to analyze key parameters like permeability and polymer concentration. The core innovation was the development of two neural networks, a Feedforward Neural Network (FNN) and an Elman Recurrent Neural Network (E-RNN), to act as fast proxy models. The E-RNN proved superior for forecasting dynamic production data, achieving exceptional accuracy (R² > 0.99) by effectively capturing time-dependent behaviors. This high-fidelity E-RNN proxy was then coupled with a GA for multi-objective optimization. Results showed that maximum oil recovery is achieved by maximizing permeability, injection rate, and polymer concentration while minimizing reservoir heterogeneity. Crucially, economic optimization revealed a different strategy, favoring a short, intensive injection period to maximize profit, highlighting a key technical-economic trade-off. The study successfully validated the framework's generalization capability. This work provides a powerful tool for accelerating polymer flooding design, with future efforts aimed at integrating laboratory data for calibration and scaling the application to full-field models.<br /> (© 2026. The Author(s).)
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  Data: Declarations. Competing interests: The authors declare no competing interests. Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: Sci Rep. 2023 Aug 2;13(1):12505. (PMID: <searchLink fieldCode="PM" term="%2237532745%22">37532745)</searchLink><br />Sci Rep. 2024 Jun 8;14(1):13213. (PMID: <searchLink fieldCode="PM" term="%2238851823%22">38851823)</searchLink><br />Sci Rep. 2024 Nov 22;14(1):29000. (PMID: <searchLink fieldCode="PM" term="%2239578498%22">39578498)</searchLink><br />Langmuir. 2025 Nov 4;41(43):29180-29195. (PMID: <searchLink fieldCode="PM" term="%2241137808%22">41137808)</searchLink>
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  Data: <i>Keywords: </i>Artificial intelligence; Elman recurrent neural network; Feedforward neural network; Genetic algorithm; Polymer flooding
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