A hybrid AI-genetic algorithm framework for the optimization of polymer flooding strategies: a numerical simulation-based approach.
Gespeichert in:
| Titel: | A hybrid AI-genetic algorithm framework for the optimization of polymer flooding strategies: a numerical simulation-based approach. |
|---|---|
| Autoren: | 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. |
| Quelle: | Scientific reports [Sci Rep] 2026 Jan 19. Date of Electronic Publication: 2026 Jan 19. |
| Publication Model: | Ahead of Print |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Abstract: | 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: | Velasquez, J. D., Cadavid, L. & Franco, C. J. Intelligence techniques in sustainable energy: analysis of a decade of advances. Energies 16 (19), 6974 (2023). Pali, F. et al. Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends. Computers 14 (5), 190. (2025). Arouri, M. & Gomes, M. Energy and economic growth: introduction and roadmap. In Handbook on Energy and Economic Growth, Edward Elgar Publishing: ; 1–7. (2024). Li, X. et al. A Thermo-Stable Polymeric Surfactant for Enhanced Heavy Oil Recovery via Hot Water Chemical Flooding. Langmuir (2025). Amiri-Ramsheh, B., Nait Amar, M., Shateri, M. & Hemmati-Sarapardeh, A. On the evaluation of the carbon dioxide solubility in polymers using gene expression programming. Sci. Rep. 13 (1), 12505 (2023). Khosravi, R., Chahardowli, M. & Simjoo, M. Smart Water–Polymer Hybrid Flooding in Heavy Oil Sandstone Reservoirs: A Detailed Review and Guidelines for Future Applications (Energy & Fuels, 2024). Saboorian-Jooybari, H., Dejam, M. & Chen, Z. Heavy oil polymer flooding from laboratory core floods to pilot tests and field applications: Half-century studies. J. Petrol. Sci. Eng. 142, 85–100 (2016). Saboorian-Jooybari, H., Dejam, M. & Chen, Z. In Half-century of heavy oil polymer flooding from laboratory core floods to pilot tests and field applications, SPE Canada heavy oil technical conference, OnePetro: (2015). Ekkawong, P., Han, J., Olalotiti-Lawal, F. & Datta-Gupta, A. Multiobjective design and optimization of polymer flood performance. J. Petrol. Sci. Eng. 153, 47–58 (2017). Rabiei, M., Gupta, R., Cheong, Y., Sanchez Soto, G. & Conference In Transforming data into knowledge using data mining techniques: application in water production problem diagnosis in oil wells, SPE Asia Pacific Oil and Gas and Exhibition, SPE: ; pp SPE-133929-MS. (2010). Al-Aghbari, M. & Gujarathi, A. M. Hybrid optimization approach using evolutionary neural network & genetic algorithm in a real-world waterflood development. J. Petrol. Sci. Eng. 216, 110813 (2022). Bahrami, P., Sahari Moghaddam, F. & James, L. A. A review of proxy modeling highlighting applications for reservoir engineering. Energies 15 (14), 5247 (2022). Khosravi, R., Simjoo, M. & Chahardowli, M. An innovative approach to upscale Low-Salinity polymer flooding in heterogeneous sandstone reservoirs: application of particle swarm optimization and automated history matching. Results Eng. 104761. (2025). Xu, L., Wang, X., Wang, Z. & Cao, G. Hybrid quantum genetic algorithm for structural damage identification. Comput. Methods Appl. Mech. Eng. 438, 117866 (2025). Janiga, D., Czarnota, R., Stopa, J., Wojnarowski, P. & Kosowski, P. Performance of nature inspired optimization algorithms for polymer enhanced oil recovery process. J. Petrol. Sci. Eng. 154, 354–366 (2017). Hou, J., Li, Z., Cao, X. & Song, X. Integrating genetic algorithm and support vector machine for polymer flooding production performance prediction. J. Petrol. Sci. Eng. 68 (1–2), 29–39 (2009). Bahrami, P., Kazemi, P., Mahdavi, S. & Ghobadi, H. A novel approach for modeling and optimization of surfactant/polymer flooding based on genetic programming evolutionary algorithm. Fuel 179, 289–298 (2016). Zhang, R. & Chen, H. Multi-objective global and local Surrogate-Assisted optimization on polymer flooding. Fuel 342, 127678 (2023). Lei, Y., Li, S., Zhang, Q., Zhang, X. & Guo, L. In A hybrid genetic algorithm for optimal control solving of polymer flooding, 2010 International Conference on Intelligent Computation Technology and Automation, IEEE: ; pp 122–125. (2010). Al-Dousari, M. M. & Garrouch, A. A. An artificial neural network model for predicting the recovery performance of surfactant polymer floods. J. Petrol. Sci. Eng. 109, 51–62 (2013). Jiang, B. et al. Modeling and optimization for curing of polymer flooding using an artificial neural network and a genetic algorithm. J. Taiwan Inst. Chem. Eng. 45 (5), 2217–2224 (2014). Le Van, S. & Chon, B. H. Artificial neural network model for alkali-surfactant-polymer flooding in viscous oil reservoirs: generation and application. Energies 9 (12), 1081 (2016). Amirian, E., Dejam, M. & Chen, Z. Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel 216, 83–100 (2018). Sun, Q. & Ertekin, T. In Development and application of an artificial-neural-network based expert system for screening and optimization of polymer flooding projects, SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, SPE: ; pp SPE-192236-MS. (2018). Mohammadi, S., Khodapanah, E. & Tabatabaei-Nejad, S. A. Simulation study of salinity effect on polymer flooding in core scale. J. Chem. Petroleum Eng. 53 (2), 137–152 (2019). Siavashi, M. & Yazdani, M. A comparative study of genetic and particle swarm optimization algorithms and their hybrid method in water flooding optimization. J. Energy Res. Technol. 140 (10), 102903 (2018). Brantson, E. T. et al. Development of hybrid low salinity water polymer flooding numerical reservoir simulator and smart proxy model for chemical enhanced oil recovery (CEOR). J. Petrol. Sci. Eng. 187, 106751 (2020). Sun, Q. & Ertekin, T. Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies. J. Petrol. Sci. Eng. 185, 106617 (2020). Javadi, A. et al. A combination of artificial neural network and genetic algorithm to optimize gas injection: a case study for EOR applications. J. Mol. Liq. 339, 116654 (2021). Ng, C. S. W., Jahanbani Ghahfarokhi, A. & Nait Amar, M. Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization. J. Petroleum Explor. Prod. Technol. 11 (7), 3103–3127 (2021). Larestani, A., Mousavi, S. P., Hadavimoghaddam, F., Ostadhassan, M. & Hemmati-Sarapardeh, A. Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: cascade neural network and gradient boosting decision tree. Alexandria Eng. J. 61 (10), 7715–7731 (2022). Khosravi, R., Simjoo, M. & Chahardowli, M. A new insight into pilot-scale development of low-salinity polymer flood using an intelligent-based proxy model coupled with particle swarm optimization. Sci. Rep. 14 (1), 29000 (2024). Berg, A. B. Combining Gmsh and MRST-Developing Software for More Efficient Grid Creation in Two Dimensions (NTNU, 2022). Hemmati-Sarapardeh, A., Larestani, A., Menad, N. A. & Hajirezaie, S. Applications of Artificial Intelligence Techniques in the Petroleum Industry (Gulf Professional Publishing, 2020). Nakutnyy, P., Asghari, K. & Torn, A. In Analysis of waterflooding through application of neural networks, PETSOC Canadian International Petroleum Conference, PETSOC: ; pp PETSOC-2008-190. (2008). Gupta, P. et al. In Genomic Insights into Infertility Using Neural Network, International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, Springer: ; pp 141–157. (2023). Ebrahimi Mood, S., Rouhbakhsh, A. & Souri, A. Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things. Neural Comput. Appl. 37 (6), 4957–4969 (2025). Elman, J. L. Finding structure in time. Cogn. Sci. 14 (2), 179–211 (1990). Razghandi, M., Dehghan, A. & Yousefzadeh, R. Application of particle swarm optimization and genetic algorithm for optimization of a Southern Iranian oilfield. J. Petroleum Explor. Prod. 11 (4), 1781–1796 (2021). Abukhamsin, A. Y. Optimization of well design and location in a real field. Unpublished MS thesis, Stanford University, CA (2009). Thomas, A. Essentials of polymer flooding technique. John Wiley & Sons: (2019). Khosravi, R., Simjoo, M. & Chahardowli, M. Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique. Sci. Rep. 14 (1), 13213 (2024). |
| Contributed Indexing: | Keywords: Artificial intelligence; Elman recurrent neural network; Feedforward neural network; Genetic algorithm; Polymer flooding |
| Entry Date(s): | Date Created: 20260119 Latest Revision: 20260119 |
| Update Code: | 20260120 |
| DOI: | 10.1038/s41598-025-33874-y |
| PMID: | 41554792 |
| Datenbank: | MEDLINE |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science Schreiben Sie den ersten Kommentar!