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Alain P. Tchameni, Lin Zhao, Joseph X. F. Ribeiro & Ting Li. (2019) Predicting the rheological properties of waste vegetable oil biodiesel-modified water-based mud using artificial neural network. Geosystem Engineering 22:2, pages 101-111.
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Karim Golzar, Sepideh Amjad-Iranagh & Hamid Modarress. (2014) Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques. Journal of Dispersion Science and Technology 35:12, pages 1809-1829.
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Mohamed Riad Youcefi, Ahmed Hadjadj, Abdelak Bentriou & Farouk Said Boukredera. (2021) Real-Time Prediction of Plastic Viscosity and Apparent Viscosity for Oil-Based Drilling Fluids Using a Committee Machine with Intelligent Systems. Arabian Journal for Science and Engineering 47:9, pages 11145-11158.
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Yee Cai Ning, Syahrir Ridha, Suhaib Umer Ilyas, Shwetank Krishna & Muslim Abdurrahman. (2022) Shear Stress and Filtration Loss Properties Assessment of Nano-Silica Water-Based Drilling Fluid Using Machine Learning Approaches. Journal of Energy Resources Technology 144:6.
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Raphael R. Silva, Alfredo I.C. Garnica, Giovanna L.R. Leal, Luara R. Viana, Júlio C.O. Freitas, Alex N. Barros, Thales L.S. Silva, João Adauto de S. Neto & Fabiola D.S. Curbelo. (2022) Evaluation of novel microemulsion-based (O/W) drilling fluid with nonionic surfactant and shale interaction mechanisms. Journal of Petroleum Science and Engineering 213, pages 110327.
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Ahmed Alsabaa, Hany Gamal, Salaheldin Elkatatny & Yasmin Abdelraouf. (2022) Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud. ACS Omega 7:18, pages 15603-15614.
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