Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review; Applied Soft Computing; Vol. 176
| Parent link: | Applied Soft Computing.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 176.— 2025.— Article number 113129, 22 p. |
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| Andre forfattere: | , , , |
| Summary: | 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 |
| Sprog: | engelsk |
| Udgivet: |
2025
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| Fag: | |
| Online adgang: | https://doi.org/10.1016/j.asoc.2025.113129 |
| Format: | MixedMaterials Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680330 |
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| 200 | 1 | |a Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review |f Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood, Mohammad Mehrad | |
| 203 | |a Текст |b визуальный |c электронный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 172 tit | ||
| 330 | |a 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 | ||
| 336 | |a Текстовый файл | ||
| 371 | |a AM_Agreement | ||
| 461 | 1 | |t Applied Soft Computing |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 176 |v Article number 113129, 22 p. |d 2025 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Machine/deep learning | |
| 610 | 1 | |a Hybrid prediction models | |
| 610 | 1 | |a Real-time prediction | |
| 610 | 1 | |a Intelligent automated drilling | |
| 610 | 1 | |a Bibliometric and scientometric analysis | |
| 610 | 1 | |a Artificial intelligence deployment economics | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |9 22200 | |
| 701 | 1 | |a Al-Shargabi |b M. A. T. S. |c specialist in the field of petroleum engineering |c Engineer of Tomsk Polytechnic University |f 1993- |g Mokhammed Abdulsalam Takha Sallam |9 22768 | |
| 701 | 1 | |a Wood |b D. A. |g David | |
| 701 | 1 | |a Mekhrad |b M. |g Mokhammad | |
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