Viscosity Modeling and Prediction of Amorphophallus oncophyllus and Sapindus rarak Using Machine Learning Methods

Muhammad Taufiq Fathaddin, Dwi Atty Mardiana, Andrian Sutiadi, Fajri Maulida

Abstract


Viscosity plays an important role in regulating the mobility of fluids injected into the reservoir to increase the efficiency of oil sweeping. This study discusses the application of Machine Learning methods, namely ANN and ANFIS, to model the correlation of physical properties of Amorphophallus oncophyllus and Sapindus rarak solutions. The purpose of this study is to obtain a correlation to determine the viscosity of the polymer solutions. The data used include viscosity measurements for 21 samples of Amorphophallus oncophyllus and Sapindus rarak solutions with variations in concentration and salinity. The data is augmented by digitization for modeling. The results show that both Machine Learning methods can estimate viscosity values well. Very accurate results are achieved by applying ANN and ANFIS with average correlation coefficients of 0.997240 and 0.995124, respectively.


Keywords


Viscosity; chemical flooding; salinity; oil recovery

Full Text:

PDF

References


Z. Zhang, Y. Wang, M. Ding, D. Mao, M. Chen, Y. Han, Y. Liu, & X. Xue, “Effects of viscosification, ultra-low interfacial tension, and emulsification on heavy oil recovery by combination flooding.” Journal of Molecular Liquids, 380, 121698, 2023, https://doi.org/10.1016/j.molliq.2023.121698

H. He, R. Chen, F. Yuan, Y. Tian, & W. Ning, “Influence of viscosity ratio on enhanced oil recovery performance of anti-hydrolyzed polymer for high-temperature and high-salinity reservoir.” Physics of Fluids, 36, 4, 2024, https://doi.org/10.1063/5.0203304.

A. M. Firozjaii, & H. R. Saghafi, “Review on chemical enhanced oil recovery using polymer flooding: Fundamentals, experimental and numerical simulation.” Petroleum, vol. 6, no. 2, 2019, pp. 115–122. https://doi.org/10.1016/j.petlm.2019.09.003.

D. Arab, A. Kantzas, and S. L. Bryant, “Water flooding of oil reservoirs: Effect of oil viscosity and injection velocity on the interplay between capillary and viscous forces,” Journal of Petroleum Science and Engineering, vol. 186, p. 106691, Nov. 2019, doi: 10.1016/j.petrol.2019.106691.

M. T. Fathaddin, D. A. Mardiana, A. Sutiadi, F. Maulida, B. M. Ulfah, “Modeling of Shrimp Chitosan Polymer Adsorption Using Artificial Neural Network.” Journal of Earth Energy Science, Engineering, and Technology, vol. 7, no. 2, 2024, pp. 37-43, https://dx.doi.org/10.25105/jvk2gg02.

J. Siahaya, D. A. Mardiana, & M. T. Fathaddin, “Characterization of addition porang on polyacrylamide polymer for enhanced oil recovery.” Journal of Earth Energy Science Engineering and Technology, vol. 6, no. 3, 2023. https://doi.org/10.25105/jeeset.v6i3.17423.

V. Aprilia, A. Murdiati, P. Hastuti, & E. Harmayani, “Carboxymethylation of Glucomannan from Porang Tuber (Amorphophallus oncophyllus) and the Physicochemical Properties of the Product.” Pakistan Journal of Nutrition, vol. 16, no. 11, pp. 835–842, 2017. https://doi.org/10.3923/pjn.2017.835.842.

M. N. Anissa, S. Rahayoe, E. Harmayani, & K. N. Ulya, “Extraction and Characterization of Glucomannan from Porang (Amorphopallus oncophyllus) with Size Variations of Porang.” Jurnal Agritech, vol. 43, no. 4, pp. 328, 2023. https://doi.org/10.22146/agritech.68886.

A. Yanuriati, D. W. Marseno, R. Rochmadi, & E. Harmayani, “Gel Glukomanan Porang-Xantan dan Kestabilannya Setelah Penyimpanan Dingin dan Beku.” Jurnal Agritech, vol. 37, no. 2, pp. 121, 2017. https://doi.org/10.22146/agritech.10793.

F. Wijayanti, M. Sari, R. Suprayitno, & D. Aminin, “The Gel Soap with Raw Materials of Lerak Fruit (Sapindus rarak DC).” Stannum Jurnal Sains Dan Terapan Kimia, vol. 2, no. 1, pp. 1–6, 2020. https://doi.org/10.33019/jstk.v2i1.1618.

S. Rai, E. Acharya-Siwakoti, A. Kafle, H. P. Devkota, & A. Bhattarai, “Plant-Derived Saponins: A review of their surfactant properties and applications.” Sci, vol. 3, no. 4, pp. 44, 2021. https://doi.org/10.3390/sci3040044.

M. H. Mondal, S. Malik, A. Garain, S. Mandal, & B. Saha, “Extraction of Natural Surfactant Saponin from Soapnut (Sapindus mukorossi) and its Utilization in the Remediation of Hexavalent Chromium from Contaminated Water.” Tenside Surfactants Detergents, vol. 54, no. 6, pp. 519–529, 2017. https://doi.org/10.3139/113.110523.

A. Mortensen et al., “Re‐evaluation of konjac gum (E 425 i) and konjac glucomannan (E 425 ii) as food additives,” EFSA Journal, vol. 15, no. 6, Jun. 2017, doi: 10.2903/j.efsa.2017.4864.

F. Maulida et al., “Evaluation of The Characteristics of Sapindus Rarak Surfactant Injection To Enhance Oil Recovery,” Scientific Contributions Oil and Gas, vol. 47, no. 3, pp. 233–243, Oct. 2024, doi: 10.29017/scog.47.3.1637.

L. C. Hawa, N. L. Farhanrika, and A. M. Ahmad, “Utilization of Lerak Juice (Sapindus rarak DC) as Natural Surfactant in the Liquid Washing Soap Production,” Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), vol. 11, no. 1, p. 24, Mar. 2022, doi: 10.23960/jtep-l.v11i1.24-34.

N. Nabipour, J. Sasanipour, A. Baghban, and A. H. Mohammadi, “Towards ANFIS-PSO strategy for estimating viscosity of ternary mixtures containing ionic liquids,” Journal of Molecular Liquids, vol. 298, p. 111802, Oct. 2019, doi: 10.1016/j.molliq.2019.111802.

Y. Kassem, H. Çamur, and E. Esenel, “Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K,” Procedia Computer Science, vol. 120, pp. 521–528, Jan. 2017, doi: 10.1016/j.procs.2017.11.274.

T. Eryilmaz, M. K. Yesilyurt, A. Taner, and S. A. Celik, “Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks,” Arabian Journal for Science and Engineering, vol. 40, no. 12, pp. 3745–3758, Sep. 2015, doi: 10.1007/s13369-015-1831-6.

S. Belmadani, M. Hamadache, C. Si-Moussa, M. Laidi, and S. Hanini, “Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis,” Kemija U Industriji, vol. 69, no. 7–8, pp. 355–364, Jan. 2020, doi: 10.15255/kui.2019.053.

Han, S., K. W. Kim, S. Kim, & Y. C. Youn, “Artificial Neural Network: Understanding the Basic Concepts without Mathematics.” Dementia and Neurocognitive Disorders, vol. 17, no. 3, Jan. 2018, p. 83, doi:10.12779/dnd.2018.17.3.83.

M. Chen, U. Challita, W. Saad, C. Yin, & M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial.” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, Jan. 2019, pp. 3039–71, doi:10.1109/comst.2019.2926625.

R. Dastres & M. Soori, “Artificial Neural Network Systems.” International Journal of Imaging and Robotic, vol. 21, no. 2, sep. 2021, pp. 13-25.

M. Babanezhad, A. Masoumian, A. T. Nakhjiri, A. Marjani, & S. Shirazian, “Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS).” Scientific Reports, vol. 10, no. 1, Sept. 2020, doi:10.1038/s41598-020-73175-0.

A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, “The application of ANFIS prediction models for thermal error compensation on CNC machine tools.” Applied Soft Computing, vol. 27, Nov. 2014, pp. 158–68, doi:10.1016/j.asoc.2014.11.012.

W. Yaïci and E. Entchev, “Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system,” Renewable Energy, vol. 86, pp. 302–315, Aug. 2015, doi: 10.1016/j.renene.2015.08.028.

M. Kazemipoor, M. Hajifaraji, C. W. J. B. W. M. Radzi, S. Shamshirband, D. Petković, and L. M. Kiah, “Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.” Computer Methods and Programs in Biomedicine, vol. 118, no. 1, Oct. 2014, pp. 69–76, doi:10.1016/j.cmpb.2014.10.006.

F. Maulida & M. T. Fathaddin, “Application of Natural Surfactant from Morus alba, Soapnut, Sapindus rarak for Enhanced Oil Recovery – Critical Review.” IOP Conference Series Earth and Environmental Science, vol. 1339, no. 1, p. 012025, 2024. https://doi.org/10.1088/1755-1315/1339/1/012025.

F. Maulida, A. Sutiadi, M. T. Fathaddin, D. A. Mardiana, R. Setiati, P. A. Rakhmanto, A. Ristawati, S. Irawan, M. D. Arkaan, “Evaluation of The Characteristics of Sapindus Rarak Surfactant Injection to Enhance Oil Recovery.” Scientific Contributions Oil & Gas, vol. 47, no. 3, 2024. https://doi.org/10.29017/SCOG.47.3.1637.

M. T. Fathaddin, S. Irawan, R. Setiati, P. A. Rakhmanto, S. Prakoso & D. A. Mardiana, “Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs.” Heliyon, vol. 10, no. 13, pp. e33824, 2024. https://doi.org/10.1016/j.heliyon.2024.e33824.

A.A. Mahmoud, S. Elkatatny, W. Chen, & A. Abdulraheem, “Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence.” Energies, vol. 12, pp. 3671–3683, 2019. https://doi.org/10.3390/en12193671.

Mada Sanjaya, W.S.P. Panduan Praktis Pemrograman Robot Vision Menggunakan Matlab dan IDE Arduino. Bandung: Penerbit Andi, 2016. https://digilib.uinsgd.ac.id/11590/.




DOI: http://dx.doi.org/10.12962/j24604682.v21i1.21953

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.