A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12145-024-01474-5.pdf
Reference82 articles.
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2. Ahmed A, Elkatatny S, Ali A et al (2019) New model for pore pressure prediction while drilling using artificial neural networks. Arab J Sci Eng 44:6079–6088. https://doi.org/10.1007/s13369-018-3574-7
3. Ahmed A, Elkatatny S, Ali A (2021) Fracture pressure prediction using surface drilling parameters by artificial intelligence techniques. J Energy Resour Technol Trans ASME 143:033201. https://doi.org/10.1115/1.4049125
4. Ahmed A, Elkatatny S, Gamal H, Abdulraheem A (2022a) Artificial intelligence models for real-time bulk density prediction of vertical complex lithology using the drilling parameters. Arab J Sci Eng 47:10993–11006. https://doi.org/10.1007/s13369-021-05537-3
5. Ahmed A, Gamal H, Elkatatny S, Ali A (2022b) Bulk density prediction while drilling vertical complex lithology using artificial intelligence. J Appl Geophys 199:104574. https://doi.org/10.1016/j.jappgeo.2022.104574
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