Using rock physics analysis driven feature engineering in ML-based shear slowness prediction using logs of wells from different geological setup
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01266-3.pdf
Reference46 articles.
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2. Ahmed SA, Hussain M, Khan ZU (2022) Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan. Front Earth Sci 10:919130
3. Al Ghaithi A, Prasad M (2020) Machine learning with artificial neural networks for shear log predictions in the Volve field Norwegian North Sea. In: SEG technical program expanded abstracts 2020. Society of Exploration Geophysicists, pp 450–454
4. Alameedy U, Alhaleem AA, Isah A, Al-Yaseri A, El-Husseiny A, Mahmoud M (2022) Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir. Iraq J Appl Geophys 205:104760
5. Anemangely M, Ramezanzadeh A, Amiri H, Hoseinpour S-A (2019) Machine learning technique for the prediction of shear wave velocity using petrophysical logs. J Petrol Sci Eng 174:306–327. https://doi.org/10.1016/j.petrol.2018.11.032
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