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. |
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| 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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Nájsť tento článok vo Web of Science | 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.<br /> (© 2026. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-33874-y |