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Reference80 articles.
1. Abdelaal A, Elkatatny S, Abdulraheem A (2021) Data-driven modeling approach for pore pressure gradient prediction while drilling from drilling parameters. https://doi.org/10.1021/acsomega.1c01340
2. Abdulmalek Ahmed S, Elkatatny S, Abdulraheem A, Mahmoud M, Ali AZ (2018) New approach to predict fracture pressure using functional networks.−presented at SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018; Society of Petroleum Engineers. https://doi.org/10.2118/192317-ms
3. Abusurra, MSM, "Using artificial neural networks to predict formation stresses for Marcellus shale with data from drilling operations" (2017). Graduate theses, Dissertations, and problem reports. 5023. https://researchrepository.wvu.edu/etd/5023
4. Aghakhani Emamqeysi MR, Fatehi Marji M, Hashemizadeh A, Abdollahipour A, Sanei M (2023) Prediction of elastic parameters in gas reservoirs using ensemble approach. Environ Earth Sci. https://doi.org/10.1007/s12665-023-10958-4
5. Ahmadi M, Chen Z (2020) Machine learning-based models for predicting permeability impairment due to scale deposition. J Pet Explor Prod Technol 10(7):2873–2884. https://doi.org/10.1007/s13202-020-00941-1