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Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case

Author

Listed:
  • Jestril Ebaga-Ololo

    (Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea)

  • Bo Hyun Chon

    (Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea)

Abstract

Many previous contributions to methods of forecasting the performance of polymer flooding using artificial neural networks (ANNs) have been made by numerous researchers previously. In most of those forecasting cases, only a single polymer slug was employed to meet the objective of the study. The intent of this manuscript is to propose an efficient recovery factor prediction tool at different injection stages of two polymer slugs during polymer flooding using an ANN. In this regard, a back-propagation algorithm was coupled with six input parameters to predict three output parameters via a hidden layer composed of 10 neurons. Evaluation of the ANN model performance was made with multiple linear regression. With an acceptable correlation coefficient, the proposed ANN tool was able to predict the recovery factor with errors of <1%. In addition, to understand the influence of each parameter on the output parameters, a sensitivity analysis was applied to the input parameters. The results showed less impact from the second polymer concentration, owing to changes in permeability after the injection of the first polymer slug.

Suggested Citation

  • Jestril Ebaga-Ololo & Bo Hyun Chon, 2017. "Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case," Energies, MDPI, vol. 10(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:844-:d:102488
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    Citations

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    Cited by:

    1. Pablo Druetta & Francesco Picchioni, 2018. "Numerical Modeling and Validation of a Novel 2D Compositional Flooding Simulator Using a Second-Order TVD Scheme," Energies, MDPI, vol. 11(9), pages 1-30, August.
    2. Olalekan Alade & Dhafer Al Shehri & Mohamed Mahmoud & Kyuro Sasaki, 2019. "Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)," Energies, MDPI, vol. 12(12), pages 1-13, June.
    3. Timur Imankulov & Yerzhan Kenzhebek & Samson Dawit Bekele & Erlan Makhmut, 2024. "Enhancing Oil Recovery Predictions by Leveraging Polymer Flooding Simulations and Machine Learning Models on a Large-Scale Synthetic Dataset," Energies, MDPI, vol. 17(14), pages 1-21, July.

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