Machine learning models predict the synergistic effect of ultrasound and solvation affect crude oil viscosity

Machine Learning


  • Ahmad, QA, Ashraf, S., Iqbal, W. & Qiang, ML Disc Spherical Fuzzy Aczel Alsina Aggregation Information for Determining Technology Enhancement Technology for crude oil pretreatment. SCI. manager (Nature Publishing Group UK, 2024). https://doi.org/10.1038/S41598-024-62036-9.

  • Homayuni, F., Hamidi, Aa, Vatani, A., Shaygani, Aa & Faraji Dana, R. Reduction of viscosity of heavy and excess heavy crude oil due to pulsating electric fields. Pet. SCI. Technor. 292052–2060 (2011).

    CAS Google Scholar

  • Gomaa, S., Salem, K.G. & El-Hoshoudy. oil https://doi.org/10.1016/j.petlm.2023.10.001 (2023).

    ArticleGoogle Scholar

  • Badger, MW & Schaubert, HH Reduced viscosity of excess heavy crude oil. ACS Div. Fuel chemistry. 43461–465 (1998).

    CAS Google Scholar

  • Hasan, SW, Ghannam, MT & Esmail, N. fuel 891095–1100 (2010).

    CAS Google Scholar

  • Alomair, OA & Almusallam, as the effects of heavy crude viscosity reduction and asphaltene precipitation. Energy fuel 277267–7276 (2013).

    CAS Google Scholar

  • Subramanian, D., Wu, K. & Firoozabadi, A. Ionic liquids as viscosity modifiers for heavy, enormous, heavy crude oils. fuel https://doi.org/10.1016/j.fuel.2014.11.051 (2014).

    ArticleGoogle Scholar

  • Ahmad, QA, Ashraf, S., Shahid, T., Hameed, MS & Qiang, Mali are offshore drilling explorations for oil and gas operations based on the stochastic language Dombi coalition decision model. IEEE Access pp1 (2024).

  • Khan, N. Etal. Experimental and Mechanistic Studies: Partially hydrolyzed polyacrylamide gel degradation and depletion via ultrasonic waves and chemicals. Ultrason. Sonochem. 56350–360 (2019).

    CAS PubMed Google Scholar

  • Khan, N. Etal. Comparison of acidification and ultrasonic waves, and synergistic effects on inorganic plug relaxation. Energy fuel 3111134–11145 (2017).

    CAS Google Scholar

  • Khan, N. Etal. Restoring permeability of damaged water-sensitive cores using ultrasound. Ultrason. Sonochem. 38381–389 (2017).

    CAS PubMed Google Scholar

  • Anchliya, A. Acoustic Stimulation: Improved boring damage nearby and oil recovery. in 7th International Petroleum Conference on Canada 1–6 (2006).

  • Duhon, Rd & Campbell, Effect of ultrasonic energy on fluid flow in JM porous media. in 2nd Annual Eastern Regional Conference 1–15 (1965).

  • Guo, X., Du, Z., Li, G. & Shu, Z. in SPE International Thermal Operations and Heavy Oil Symposium and Western Regional Meeting 1–18 (2004).

  • Poosumas, J., Ngaosuwan, K., Quitain, At & Assabumrungrat, S. The role of ultrasonic irradiation in esterification of palm oil using calcium oxide as a solid base catalyst. Energy conversation. manager. 12062–70 (2016).

    Advertising CAS Google Scholar

  • Flint, EB & Suslick, KS Cavitation Temperatures. Science (New York, New York) 2531397–1399 (1991).

    ADS CAS PubMed Google Scholar

  • Suslick, K.S. Sonochemistry. Kirk-Othmer Encycl. Chemistry. Technor. 1–21 (2000).

  • Mujumdar, S., Senthil Kumar, P. & Pandit, AB urination, ultrasound AB venting: Relationship between strength and emulsion quality. Indian J. Chem. Technor. 4277–284 (1997).

    CAS Google Scholar

  • Razavifar, M., Qajar, J. & Riazi, M. Experimental study on the pore scale mechanism of ultrasonic-assisted heavy oil recovery by solvent effect. J. Pet. SCI. Eng. 214110553 (2022).

    CAS Google Scholar

  • Pu, C., Shi, D., Zhao, S., Xu, H. & Shen, H. A technology to remove inorganic scale damage to wells through high-power ultrasound therapy. Pet. expedition. Developer. 38243–248 (2011).

    CAS Google Scholar

  • Hamidi, H. Etal. A technique for evaluating oil/heavy oil viscosity under ultrasound in simulated porous media. Ultra Sonics 54655–662 (2014).

    CAS PubMed Google Scholar

  • Mullakaev, MS, Volkova, Gi & Gradov, OM are the effects of ultrasonic waves on the viscosity temperature properties of crude oils of various compositions. theory. Found. Chemistry. Eng. 49302–311 (2015).

    Google Scholar

  • Wang, Z., Xu, Y. &Gu, Y. Ultrasonics Sonochemistry niobate niobate Oil recovery for Ultrasonic Transducer designs. Ultrason. Sonochem. 27171–177 (2015).

    CAS PubMed Google Scholar

  • Mohsin, M. & Meribout, M. An expanded model for ultrasonic-based reinforced oil recovery with experimental verification. Ultrason. Sonochem. https://doi.org/10.1016/j.ultsonch.2014.08.007 (2014).

    ArticlePubMed Google Scholar

  • Taheri-Shakib, J., Naderi, H., Salimidelshad, Y. & Teymouri, A. Using Ultrasonic as a new approach to the elimination of inorganic scales (NACLs): an experimental study. J. Pet. expedition. product. Technor. 8553–564 (2018).

    CAS Google Scholar

  • Rezaei, R., Mohebbi, A., Riazi, M. & Danafar, F. Visualization study of oil type and model geometry for oil recovery under ultrasonic irradiation of glass micromodels. fuel 239709–716 (2019).

    Google Scholar

  • He, S., Tan, X., Hu, X. & Gao, Y. The effect of ultrasonic waves on oil recovery from crude oil containing sludge. environment. Technor. 01–7 (2018).

    Google Scholar

  • Razavifar, M. &Qajar, J. Experimental investigation of the ultrasonic wave effect on the viscosity and thermal behavior of asphaltenic crude oil. Chemistry. Eng. process. process. Intensif. 153107964 (2020).

    CAS Google Scholar

  • Xu, X. & Bao, T. Studies on the removal of close-range obstruction caused by asphaltene deposition using sonochemical methods. Ultrason. Sonochem. 64104918 (2020).

    CAS PubMed Google Scholar

  • Otumudia, E., Hamidi, H., Jadhawar, P. & Wu, K. Effects of reservoir rock geometry and ultrasonic parameters on removal of asphaltene deposition under ultrasound. Ultrason. Sonochem. 83105949 (2022).

    CAS PubMed PubMed Central Google Scholar

  • Razavifar, M. &Qajar, J. Synergistic effect of ultrasonic irradiation and α-Fe2o3 Application to nanoparticles regarding the viscosity and thermal properties of asphaltenic crude oil and in situ combustion EOR. Ultra Sonics 120106655 (2022).

    CAS PubMed Google Scholar

  • Khalesi Moghaddam, R., Yarranton, H. W. & Natale, G. Sonicated asphaltene-mediated interface: insight into emulsion destabilization mechanisms using a micropathy approach. Chemistry. Eng. SCI. 280119065 (2023).

    CAS Google Scholar

  • Kuryakov, Study of asphaltene redecomposition in toluene/heptane mixtures by VN dynamic and static light scattering. Pet. res. 948–54 (2024).

    CAS Google Scholar

  • Nguele, R., Saheed, Ao, Okawa, H. & Sasaki, K. Insights into bitumen viscosity reduction using ultrasonic assisted EOR. in SPE Western Regional Meeting D021S001R003 (2023). https://doi.org/10.2118/212945-ms.

  • Qajar, J., Razavifar, M. & Riazi, M. Mechanical studies of the synergistic and counteracting effects of ultrasonic and solvent treatments on the rheology and asphaltene structure of heavy crude oils. Chemistry. Eng. process. process. Intensif. 195109619 (2024).

    CAS Google Scholar

  • Galimzyanova, Ar et al. The effect of ultrasonic treatment on bit blue oil properties to elucidate the effects of ultrasonic treatment: a comprehensive study of viscosity modification. Geo Energy Sci. Eng. 233212487 (2024).

    CAS Google Scholar

  • Ford, BA & MCELVANIA, E. Machine learning takes laboratory automation to the next level. J. Clin. Microbiol. 581–5 (2020).

    Google Scholar

  • Christin, S. & Lecomte, N. It furthers model verification and deep learning. Method ecol. Evol. 2021130–134 (2021).

    Google Scholar

  • Bryman, L. Predictors of bugs. Mach. learn. twenty four123–140 (1996).

    Google Scholar

  • Fang, Z., Cheng, J., Xu, C., Xu, Xu, X. &Qajar, J. Case study of construction materials: machine learning comparison and statistical approaches for estimating tensile strength of rocks. Case studs. const. maternal. 20E02890 (2024).

    Google Scholar

  • Yang, S., Zhang, L., Fan, J. & Sun, B. An experimental study of the erosion behavior of fracturing pipelines, including tensile stress and erosion prediction using random forest regression. J. Nut. Gas size. Eng. 87103760 (2021).

    Google Scholar

  • Yang, X., Dindoruk, B. &Lu, L. J. Pet. SCI. Eng. 185106598 (2020).

    CAS Google Scholar

  • Wang, S., Aggarwal, C. & Liu, H. Use random forests to stimulate neural networks and improve them. in Proc. 17th Siam int. conf. Data Mining, SDM 2017 1–9 (2017). https://doi.org/10.1137/1.9781611974973.1.



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