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Before drilling wells, a well plan and design are conducted to study the formation properties and set casing design at high-pressure zones. Rashidi et al.,<ref name=Rashidietal_2020>Rashidi, S., Mohamadian, N., Ghorbani, H., Wood, D. A., Shahbazi, K., & Alvar, M. A. (2020). Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells. Journal of Applied Geophysics, 183, 104205. https://doi.org/10.1016/j.jappgeo.2020.104205</ref> Hussain et al.,<ref name=Hussainetal_2019>Hussain, S. A., Chatterjee, C., Sarkar, S. K., Reyes, A., Majumdar, C., & Das, R. (2019). Predicting horizontal shear slowness- A machine learning approach. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2019, ADIP 2019, 1–12. https://doi.org/10.2118/197128-ms</ref> and Anemangely et al.<ref name=Anemangelyetal_2019>Anemangely, M., Ramezanzadeh, A., Amiri, H., & Hoseinpour, S. A. (2019). Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 174(November 2018), 306–327. https://doi.org/10.1016/j.petrol.2018.11.032</ref> (2019) have used advanced ML algorithms to predict the shear properties of the rocks using well data. The results of this prediction according to the authors can be utilized in the well planning process to avoid any well collapse or damage during drilling. Okoli et al.<ref name=Okolietal_2019>Okoli, P., Vega, J. C., & Shor, R. (2019). Estimating downhole vibration via machine learning techniques using only surface drilling parameters. SPE Western Regional Meeting Proceedings, 2019. https://doi.org/10.2118/195334-ms</ref> used supervised machine learning algorithms to estimate the downhole vibration on surface drilling parameters to avoid poor drilling accuracy, failures, and non-productive time from wear and tear in the drilling equipment. In addition, Noshi and Schubert<ref name=Noshiandschubert_2018>Noshi, C. I., & Schubert, J. J. (2018). The role of machine learning in drilling operations; a review. SPE Eastern Regional Meeting, 2018-October(October), 7–11. https://doi.org/10.2118/191823-18erm-ms</ref> did an overview of the role of machine learning in drilling operations that addresses potential drilling problems and the different application of each of the different types of machine learning algorithms which are, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Ma et al.<ref name=Maetal_2020>Ma, Z., Davani, E., Ma, X., Lee, H., Arslan, I., Zhai, X., Darabi, H., & Castineira, D. (2020). Finding a trend out of chaos, a machine learning approach for well spacing optimization. Proceedings - SPE Annual Technical Conference and Exhibition, 2020-Octob. https://doi.org/10.2118/201698-ms</ref> showcased the importance of utilizing augmented AI to understand the different impact of well spacing optimization on well production performance.

Before drilling wells, a well plan and design are conducted to study the formation properties and set casing design at high-pressure zones. Rashidi et al.,<ref name=Rashidietal_2020>Rashidi, S., Mohamadian, N., Ghorbani, H., Wood, D. A., Shahbazi, K., & Alvar, M. A. (2020). Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells. Journal of Applied Geophysics, 183, 104205. https://doi.org/10.1016/j.jappgeo.2020.104205</ref> Hussain et al.,<ref name=Hussainetal_2019>Hussain, S. A., Chatterjee, C., Sarkar, S. K., Reyes, A., Majumdar, C., & Das, R. (2019). Predicting horizontal shear slowness- A machine learning approach. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2019, ADIP 2019, 1–12. https://doi.org/10.2118/197128-ms</ref> and Anemangely et al.<ref name=Anemangelyetal_2019>Anemangely, M., Ramezanzadeh, A., Amiri, H., & Hoseinpour, S. A. (2019). Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 174(November 2018), 306–327. https://doi.org/10.1016/j.petrol.2018.11.032</ref> (2019) have used advanced ML algorithms to predict the shear properties of the rocks using well data. The results of this prediction according to the authors can be utilized in the well planning process to avoid any well collapse or damage during drilling. Okoli et al.<ref name=Okolietal_2019>Okoli, P., Vega, J. C., & Shor, R. (2019). Estimating downhole vibration via machine learning techniques using only surface drilling parameters. SPE Western Regional Meeting Proceedings, 2019. https://doi.org/10.2118/195334-ms</ref> used supervised machine learning algorithms to estimate the downhole vibration on surface drilling parameters to avoid poor drilling accuracy, failures, and non-productive time from wear and tear in the drilling equipment. In addition, Noshi and Schubert<ref name=Noshiandschubert_2018>Noshi, C. I., & Schubert, J. J. (2018). The role of machine learning in drilling operations; a review. SPE Eastern Regional Meeting, 2018-October(October), 7–11. https://doi.org/10.2118/191823-18erm-ms</ref> did an overview of the role of machine learning in drilling operations that addresses potential drilling problems and the different application of each of the different types of machine learning algorithms which are, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Ma et al.<ref name=Maetal_2020>Ma, Z., Davani, E., Ma, X., Lee, H., Arslan, I., Zhai, X., Darabi, H., & Castineira, D. (2020). Finding a trend out of chaos, a machine learning approach for well spacing optimization. Proceedings - SPE Annual Technical Conference and Exhibition, 2020-Octob. https://doi.org/10.2118/201698-ms</ref> showcased the importance of utilizing augmented AI to understand the different impact of well spacing optimization on well production performance.