Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review

Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review

Détails bibliographiques
Parent link:Applied Soft Computing.— .— Amsterdam: Elsevier Science Publishing Company Inc.
Vol. 176.— 2025.— Article number 113129, 22 p.
Autres auteurs: Davoodi Sh. Shadfar, Al-Shargabi M. A. T. S. Mokhammed Abdulsalam Takha Sallam, Wood D. A. David, Mekhrad M. Mokhammad
Résumé:Title screen
In recent years, the petroleum upstream has increasingly relied on artificial intelligence (AI), with applications spanning machine/deep learning (ML/DL), hybrid models, and committee machine learning. Particularly in drilling engineering (DE), AI has become crucial for addressing complex subsurface challenges. Nevertheless, its implementation continues to be a significant obstacle owing to the technological, operational, and engineering challenges involved in real-time applications of DE approaches. This review examines AI technologies in DE, focusing on their practicality, performance, and associated challenges. It evaluates models for predicting drilling fluid properties, hole cleaning, rate of penetration, wellbore trajectory, fluid hydraulics, bit wear, borehole stability, subsurface problems, and fault diagnosis. It explores integrating AI models with downhole sensors and surface data for real-time/automated drilling control, alongside real-world AI application cases. It highlights the benefits of combining ML/DL with optimization algorithms in hybrid models and analyzes trends in AI research in DE through bibliometric and scientometric studies. Guidelines are provided for selecting and improving AI algorithms for various drilling applications and assessing their economic impacts. The review concludes by identifying future research directions to advance AI applications in the drilling industry
Текстовый файл
AM_Agreement
Publié: 2025
Sujets:
электронный ресурс
труды учёных ТПУ
Machine/deep learning
Hybrid prediction models
Real-time prediction
Intelligent automated drilling
Bibliometric and scientometric analysis
Artificial intelligence deployment economics
Accès en ligne:https://doi.org/10.1016/j.asoc.2025.113129
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680330

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