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Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery
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  • Published: 25 February 2026

Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery

  • Sami Abderraouf Belkhir1,
  • Altamish Ahmed Pakeer1,
  • Mariam Shakeel2,
  • Rizwan Muneer3,
  • Younes Alblooshi1 &
  • …
  • Muhammad Rehan Hashmet1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering
  • Mathematics and computing

Abstract

Polymer flooding is among the most researched and utilized methods to improve oil recovery. Polymer solution viscosity is the primary governing parameter that aids in lowering the mobility of the injected fluid and augmenting the volumetric sweep efficiency. However, the design of a polymer solution is time-consuming and requires intensive resources because of the need to design several precursors, including makeup water salinity, temperature, polymer concentration, etc., to sustain the target solution viscosity. The objective of this study was to develop a fast and reliable approach to predict the viscosity of SAV10 polymer using the necessary input parameters, including polymer concentration, shear rate, temperature, and brine salinity. Four simple machine learning techniques, namely linear regression (LR), support vector machine (SVM), decision tree (DT), and artificial neural network (ANN), were implemented with varying critical model parameters in each technique. Owing to the non-linear relationship between polymer viscosity and input parameters, a simple linear regression model was unable to capture them. The best machine learning model among the classical models used in this study was a Wide ANN model with one fully connected layer of 100 neurons. This model yielded an acceptable viscosity prediction with a coefficient of determination (R²) of 0.998 and a root mean square error (RMSE) of 0.31 for the test data. To further generalize the predictability and reduce the model error, advanced machine learning models, i.e. Gaussian Process Regressor and an ensemble machine learning stacking regressor was employed. However, the stacking regressor dramatically lowered the root mean square error by almost 40% while the model successfully generalized the unseen viscosity data and predicted the high viscosity values with considerable confidence. We developed an SAV10 viscosity predictor from routine inputs where a stacking-based ensemble resulted in an excellent match between the actual and predicted viscosities having R2 value of 0.998 with minimum RMSE of 0.208, and MAE of 0.223, while preserving accuracy on unseen high-viscosity data.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge SNF Floerger for providing polymers for this research. Additionally, the authors would also like to acknowledge the United Arab Emirates University for providing financial support (12N240).

Author information

Authors and Affiliations

  1. Department of Chemical and Petroleum Engineering, United Arab Emirates University, Al Ain, 15551, United Arab Emirates

    Sami Abderraouf Belkhir, Altamish Ahmed Pakeer, Younes Alblooshi & Muhammad Rehan Hashmet

  2. Petroleum Engineering, Texas A&M University at Qatar, Education City, Doha, Qatar

    Mariam Shakeel

  3. College of Engineering and Science, Hamad Bin Khalifa University, Education City, Doha, Qatar

    Rizwan Muneer

Authors
  1. Sami Abderraouf Belkhir
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  2. Altamish Ahmed Pakeer
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  3. Mariam Shakeel
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  4. Rizwan Muneer
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  5. Younes Alblooshi
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  6. Muhammad Rehan Hashmet
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Contributions

M.R.H: Conceptualization, methodology design, supervision, project administration, funding acquisition, writing – review & editing.Y.A: Methodology, validation, resources, writing – review & editing.A.A.P.: Experimental work, data collection, validation, writing – review & editing.S.A.B: Machine learning model development, algorithm implementation, formal analysis, writing – review & editing.M.S.: Machine learning model development, parameter optimization, result interpretation, writing – review & editing.R.M.: Data analysis, sensitivity studies, figure preparation, writing – review & editing.

Corresponding author

Correspondence to Muhammad Rehan Hashmet.

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Belkhir, S.A., Pakeer, A.A., Shakeel, M. et al. Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41045-w

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  • Received: 27 August 2025

  • Accepted: 17 February 2026

  • Published: 25 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-41045-w

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