Application of machine learning in wellbore stability prediction: A review

Xu, Kai; Liu, Zouwei; Chen, Qi; Zhang, Qianqin; Ling, Xingjie; Cai, Xulong; He, Qingyi; Yang, Minghe

doi:10.1016/j.geoen.2023.212409

Cited by 4 publications

(1 citation statement)

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“…In nuclear engineering, the ML algorithm is employed in NPP to model the surveillance test data (Lee et al, 2021), crack fault diagnosis (Zhong and Ban, 2022), probabilistic safety assessment for fire hazard model (Worrell et al, 2019), seismic fragile analysis , and equivalence assessment between the simulation and operation data (Li X. et al, 2021). Yet, the ML shows limitations as reported in a review article in Ref (Xu et al, 2024). Figure 7 presents the workflow of ML that comprises different steps from loading the data to integration of the best-trained model into a production system.…”

Section: Machine Learningmentioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

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Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

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