Quitian-Ardila, LH et al. Increasing xanthan gum concentration improves rheological and thermal stability of water-based drilling fluids. Physics. fluid 36102305. https://doi.org/10.1063/5.0230214 (2024).
Google Scholar
Reyes, C. et al. Review on steep slope colonists for water purification. Miner. engineering 184107639. https://doi.org/10.1016/j.mineng.2022.107639 (2023).
Google Scholar
Concha, F. Solid-Liquid Separation in Mining, Fluid Mechanics and Its Applications, 2014. http://www.springer.com/series/5980
Marbun, BTH, Ridwan, RH, Nugraha, HS, Sinaga, SZ, Perbantanu, BA A review of directional drilling design and operation of geothermal wells in Indonesia. Update. energy. 176135–152. https://doi.org/10.1016/j.renene.2021.05.078 (2021).
Google Scholar
Ma, T., Chen, P., Zhao, J. Overview of vertical and directional drilling technology for exploration and development of deep petroleum resources. Geomech. Geophysics. Geoenergy, georesource. 2365–395. https://doi.org/10.1007/s40948-016-0038-y (2016).
Google Scholar
Mohiuddin, MA, Khan, K., Abdulraheem, A., Al-Majed, A. & Awal, MR Analysis of wellbore instability in vertical, directional, and horizontal wells using field data. J. Pet. Science. engineering 5583–92. https://doi.org/10.1016/j.petrol.2006.04.021 (2007).
Google Scholar
Vath, B. Directional and horizontal drilling in oil wells. Universidad Federico. fluminense(2011). https://app.uff.br/riuff/handle/1/1415
Quitian, LH, Andrade, DEV & Franco, AT Bentonite-free water-based drilling fluids under HP/HT conditions: a rheometry analysis. Leolu. Actor 61841–855. https://doi.org/10.1007/s00397-022-01356-x (2022).
Google Scholar
Amorim, LV Improvement, Rheology Protection and Recovery of Hydroclay Fluids Used in Oil Well Drilling, (2003). http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/11455
INTEQ, BH Drilling Engineering Workbook. Distributed Learning Course, Houston, (1995).
Rocha, RR et al. Sedimentation of bulking agents in non-Newtonian fluids into offshore drilling wells: Modeling, parameter estimation, and constitutive equation analysis. J. Pet. Science. engineering https://doi.org/10.1016/j.petrol.2019.106535 (2020).
Google Scholar
JL Rams & JJ Hazard Drilling fluid optimization is an on-site approach (Penwell Books, 1986).
Google Scholar
Boycott, AE hemocyte sedimentation. nature 104532–532. https://doi.org/10.1038/104532b0 (1920).
Google Scholar
Burger, R., Ruiz-baier, R., Schneider, K., Torres, H. Multiresolution methods for simulation. internal. J. Number. Anal. model. 9479–504 (2012).
Google Scholar
Herbolzheimer, E. & Acrivos, A. Enhanced sedimentation in narrow and sloping channels. J.Fluid Mecha. 108485–499. https://doi.org/10.1017/S0022112081002231 (1981).
Google Scholar
Gazelli, E. & Hinch, J. Sedimentation variability and instability. Anne. Rev. Fluid Mech. 4397–116. https://doi.org/10.1146/annurev-fluid-122109-160736 (2011).
Google Scholar
Murisic, N., Pausader, B., Peschka, D. & Bertozzi, AL Dynamics of particle sedimentation and resuspension in viscous liquid films. J.Fluid Mecha. https://doi.org/10.1017/jfm.2012.567 (2013).
Google Scholar
Palma, S., Ihle, CF & Tamburrino, A. Characterization of deposit layers of concentrated fluid-solid mixtures in inclined ducts at low Reynolds numbers H ( t ) θ. powder technology. 325192–201. https://doi.org/10.1016/j.powtec.2017.10.053 (2018).
Google Scholar
Zeng, J., Tang, P., Li, H., Zhang, D. Simulating particle sedimentation in an inclined narrow channel using an unresolved CFD-DEM method. Physics. Rev. Fluid 634302. https://doi.org/10.1103/PhysRevFluids.6.034302 (2021).
Google Scholar
Apaz, F. & Ihle, CF Applied scientific experimental observations of heat-assisted boycott effects in trapezoidal enclosures. applied science. 151–26 (2025).
Google Scholar
Schimicoscki, RS, Souza, EA, Fagundes, FM, Damasceno, JJR & Arouca, FO Analysis of solids concentration profiles in particle sedimentation in directional reservoirs using gamma-ray attenuation techniques. geological energy science and engineering 251213865. https://doi.org/10.1016/j.geoen.2025.213865 (2025).
Google Scholar
Moreira, BA, Arouca, FO & Damasceno, JJR Analysis of sedimentation of suspensions in fluids due to rheological shear-thinning properties and thixotropic effects. powder technology. 308290–297. https://doi.org/10.1016/j.powtec.2016.12.034 (2017).
Google Scholar
Zhong, R., Salehi, C., Johnson, R. Machine learning for drilling applications: A review. J. Nat. gas science and engineering 108104807. https://doi.org/10.1016/j.jngse.2022.104807 (2022).
Google Scholar
Hariharan, G., Navada, MK, Brahmavar, J. & Aroor, G. A machine learning-based predictive model for evaluating the rheological dynamics of green oils as biolubricants enriched with SiO2 nanoparticles. lubricant 1292. https://doi.org/10.3390/lubricants12030092 (2024).
Google Scholar
Alsaihati, A. & Elkatatny, S. A new method for drill cut size estimation based on machine learning techniques. Arab. J.Sci.Engineering 4816739–16751. https://doi.org/10.1007/s13369-023-08007-0 (2023).
Google Scholar
Tian, D. et al. Comparative study of machine learning methods for gas hydrate identification. geological energy science and engineering 223211564. https://doi.org/10.1016/j.geoen.2023.211564 (2023).
Google Scholar
Davoodi, S., Mehrad, M., Wood, DA, Ghorbani, H. & Rukavishnikov, VS Hybrid machine learning for rapid prediction of rheology and filtration properties of water-based drilling fluids. Engineering application artif. intelligence. one two three106459. https://doi.org/10.1016/j.engappai.2023.106459 (2023).
Google Scholar
Mahmoudabadbozchelou, M. Data-driven physics-based compositional metamodeling of complex fluids: a multi-fidelity neural network (MFNN) framework. J. Rheol. 65179–198. https://doi.org/10.1122/8.0000138 (2021).
Google Scholar
Quitian-Ardila, LH et al. Development of a machine learning-based methodology for optimal hyperparameter determination – Mathematical modeling of high-pressure and high-temperature drilling fluid behavior. Chemistry. Engineering J.Adv. 20100663. https://doi.org/10.1016/j.ceja.2024.100663 (2024).
Google Scholar
Dabiri, M.-S. et al. Use advanced machine learning techniques to model drilling fluid density at high pressure and high temperature conditions. geological energy science and engineering 244213369. https://doi.org/10.1016/j.geoen.2024.213369 (2025).
Google Scholar
Eltrissi, M., Yousef, O., El-Banbi, A., Khalaf, F. Optimization of drilling operations using machine learning frameworks. geological energy science and engineering 228211969. https://doi.org/10.1016/j.geoen.2023.211969 (2023).
Google Scholar
Bender, A. et al. Guidelines for evaluating machine learning tools in the chemical sciences. nut. Rev. Chem. 6428–442. https://doi.org/10.1038/s41570-022-00391-9 (2022).
Google Scholar
McGreivy, N. & Hakim, A. Weak baselines and reporting bias lead to undue optimism in machine learning of fluid-related partial differential equations. nut. Mach. intelligence. 61256–1269. https://doi.org/10.1038/s42256-024-00897-5 (2024).
Google Scholar
Alkinani, HH et al. A data-driven neural network model to predict the equivalent circulation density ECD, SPE Gas Oil Technology Show c. meetingSPE, https://doi.org/10.2118/198612-MS. (2019).
Hashemizadeh, A., Maaref, A., Shateri, M., Larestani, A. & Hemmati-Sarapardeh, A. Experimental measurements and modeling of water-based drilling mud density using adaptive boosting decision trees, support vector machines, and K-nearest neighbors: A case study of the South Pulse gas field. J. Pet. Science. engineering 207109132. https://doi.org/10.1016/j.petrol.2021.109132 (2021).
Google Scholar
Murtaza, M. et al. Improving the performance of water-based drilling muds using biopolymer gums: Integrating experimental and machine learning techniques. molecule 292512. https://doi.org/10.3390/molecules29112512 (2024).
Google Scholar
Taherdoost, H. Deep learning and neural networks: Implications for decision making. symmetry 151723. https://doi.org/10.3390/sym15091723 (2023).
Google Scholar
Henriksson, J., Wollebæk, L. & Yang, Z. Hybrid modeling for multiphase flow simulation. offshore technology. meeting https://doi.org/10.4043/31938-MS (2022).
Google Scholar
Bressane, A. et al. Physics-based feature engineering using fuzzy symbolic regression to predict sedimentation rates in water treatment. J. Water Process Engineering 78108749. https://doi.org/10.1016/j.jwpe.2025.108749 (2025).
Google Scholar
Baydaulet, U., Omarova, P., Merembayev, T. & Yedilkhan, A. Modeling of river channel siltation using physically informed neural network techniques and numerical simulations. engineering science. 331-12. https://doi.org/10.30919/es1296 (2025).
Google Scholar
Yavari, H., Qajar, J., Sigve, B., Rasool, A. Optimal wellbore trajectory selection using multi-objective genetic algorithm and TOPSIS method. Arab. J.Sci.Engineering 4816831–16855. https://doi.org/10.1007/s13369-023-08149-1 (2023).
Google Scholar
Colliot, O. (ed.) Machine learning for brain disorders (Springer USA, 2023). https://doi.org/10.1007/978-1-0716-3195-9.
Agatonovic-Kustrin, S. & Beresford, R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. biomed. Anal. twenty two717–727. https://doi.org/10.1016/S0731-7085(99)00272-1 (2000).
Google Scholar
Bahiuddin, I. et al. Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis. J. Mech. behavior. meter. 33 https://doi.org/10.1515/jmbm-2022-0309 (2024).
Gamal, H., Abdelaal, A., Elkatatny, S. Machine learning model for equivalent circulation density prediction from drilling data. ACS Omega 627430-27442. https://doi.org/10.1021/acsomega.1c04363 (2021).
Google Scholar
Yavari, H., Khosravian, R., Wood, DA, Aadnoy, BS & Rock, C. Advances in the application of mathematical and machine learning models to predict differential pressure in autonomous downhole inflow controllers, 5 386–406. (2021). https://doi.org/10.46690/ager.2021.04.05
Schimicoscki, RS, Quitian-ardila, LH, Germer, EM, Franco, AT & García-blanco, YJ Recent advances in analytical techniques for solid sedimentation in drilling fluids: A review. Physics. liquid. https://doi.org/10.1063/5.0280983 (2025).
Google Scholar
