Viscosity Modeling of MES and SLS Using Machine Learning Method

Authors

  • Muhammad Taufiq Fathaddin Universitas Trisakti Indonesia
  • Rini Setiati Universitas Trisakti Indonesia
  • Fahrurrozi Akbar Badan Riset dan Inovasi Nasional Indonesia
  • Iwan Sumirat Badan Riset dan Inovasi Nasional Indonesia
  • Bharoto Badan Riset dan Inovasi Nasional Indonesia
  • Ranggi Sahmura Ramadhan Rutherford Appleton Laboratory United Kingdom
  • Onnie Ridaliani Prapansya Universitas Trisakti Indonesia
  • Arinda Ristawati Universitas Trisakti Indonesia

DOI:

https://doi.org/10.26877/asset.v8i2.2304

Keywords:

concentration, oil, surfactant, temperature, viscosity

Abstract

Viscosity is crucial to improve the efficiency of injected fluids for oil displacement in reservoirs. Traditionally, research has focused on polymers that help reduce the mobility of injected fluids, while surfactant viscosity has received less consideration. This research investigated the viscosity behavior of methyl ester sulfonate (MES) and sodium lauryl sulfate (SLS) surfactant solutions using a machine learning method—adaptive neurofuzzy inference system (ANFIS). This study aimed to predict the viscosity of surfactant solutions. Experimental data included viscosity measurements of 36 MES and SLS samples at various concentrations and temperatures, obtained by digitizing viscosity curves. These data served as input and validation for the ANN and ANFIS models. The results showed that ANFIS predicted viscosity values ​​reliably, yielding only 1.33% and 0.43% differences for MES and SLS, respectively. Comparison of viscosity prediction with Artificial Neural Network (ANN) showed that ANFIS prediction was better, because ANN yielded two deviating predictions.

Author Biographies

  • Muhammad Taufiq Fathaddin, Universitas Trisakti

    Department of Petroleum Engineering, Universitas Trisakti, Jl. Kyai Tapa, Jakarta 11440, Special Capital Region of Jakarta, Indonesia

  • Rini Setiati, Universitas Trisakti

    Department of Petroleum Engineering, Universitas Trisakti, Jl. Kyai Tapa, Jakarta 11440, Special Capital Region of Jakarta, Indonesia

  • Fahrurrozi Akbar, Badan Riset dan Inovasi Nasional

    Nuclear Beam Analysis Technology Research Center, BRIN, Gd. 720-Lt.2 K.S.T. B. J. Habibie Serpong, Tangerang Selatan 15311, Banten, Indonesia

  • Iwan Sumirat, Badan Riset dan Inovasi Nasional

    Nuclear Beam Analysis Technology Research Center, BRIN, Gd. 720-Lt.2 K.S.T. B. J. Habibie Serpong, Tangerang Selatan 15311, Banten, Indonesia

  • Bharoto, Badan Riset dan Inovasi Nasional

    Nuclear Beam Analysis Technology Research Center, BRIN, Gd. 720-Lt.2 K.S.T. B. J. Habibie Serpong, Tangerang Selatan 15311, Banten, Indonesia

  • Ranggi Sahmura Ramadhan, Rutherford Appleton Laboratory

    Science and Technology Facilities Council, Rutherford Appleton Lab., Harwell Campus, Didcot OX11-0QX, United Kingdom

  • Onnie Ridaliani Prapansya, Universitas Trisakti

    Department of Petroleum Engineering, Universitas Trisakti, Jl. Kyai Tapa, Jakarta 11440, Special Capital Region of Jakarta, Indonesia

  • Arinda Ristawati, Universitas Trisakti

    Department of Petroleum Engineering, Universitas Trisakti, Jl. Kyai Tapa, Jakarta 11440, Special Capital Region of Jakarta, Indonesia

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Published

2026-03-31

Issue

Vol. 8 No. 2 (2026): February-April

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