Modelling and optimization of well hole cleaning using artificial intelligence techniques
Abstract Ineffective hole cleaning in deviated and horizontal well drilling can lead to issues like stuck pipes, reduced rate of penetration (ROP), and drill bit damage, resulting in increased non-productive time (NPT) and operational costs. Traditional methods for assessing hole cleaning rely on ex...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-06415-x |
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| author | Nageswara Rao Lakkimsetty Hassan Rashid Ali Al Araimi G. Kavitha |
| author_facet | Nageswara Rao Lakkimsetty Hassan Rashid Ali Al Araimi G. Kavitha |
| author_sort | Nageswara Rao Lakkimsetty |
| collection | DOAJ |
| description | Abstract Ineffective hole cleaning in deviated and horizontal well drilling can lead to issues like stuck pipes, reduced rate of penetration (ROP), and drill bit damage, resulting in increased non-productive time (NPT) and operational costs. Traditional methods for assessing hole cleaning rely on experimental and empirical models that often fail to account for all influencing factors and lack real-time applicability. This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. These AI methods analyze the impact of key drilling parameters—such as weight on bit (WOB), ROP, rock geomechanics, drilling fluid characteristics, and rig hydraulics—on hole cleaning. Results demonstrated that AI-driven models provide high-precision predictions and enable real-time optimization, significantly reducing NPT and enhancing drilling efficiency and safety. In conclusion, AI techniques like ANN and GA offer a robust solution to improve hole cleaning, overcoming limitations of traditional methods and contributing to safer, more cost-effective drilling operations. |
| format | Article |
| id | doaj-art-15fb749c5aab47419e89e199e2eff3b0 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-15fb749c5aab47419e89e199e2eff3b02025-02-09T12:49:51ZengSpringerDiscover Applied Sciences3004-92612025-02-017211510.1007/s42452-024-06415-xModelling and optimization of well hole cleaning using artificial intelligence techniquesNageswara Rao Lakkimsetty0Hassan Rashid Ali Al Araimi1G. Kavitha2Department of Chemical and Petroleum Engineering, School of Engineering and Computing, American University of Ras Al Khaimah (AURAK)Department of MIE, College of Engineering, National University of Science and TechnologyDepartment of Chemical Engineering, RVR & JC College of Engineering (A)Abstract Ineffective hole cleaning in deviated and horizontal well drilling can lead to issues like stuck pipes, reduced rate of penetration (ROP), and drill bit damage, resulting in increased non-productive time (NPT) and operational costs. Traditional methods for assessing hole cleaning rely on experimental and empirical models that often fail to account for all influencing factors and lack real-time applicability. This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. These AI methods analyze the impact of key drilling parameters—such as weight on bit (WOB), ROP, rock geomechanics, drilling fluid characteristics, and rig hydraulics—on hole cleaning. Results demonstrated that AI-driven models provide high-precision predictions and enable real-time optimization, significantly reducing NPT and enhancing drilling efficiency and safety. In conclusion, AI techniques like ANN and GA offer a robust solution to improve hole cleaning, overcoming limitations of traditional methods and contributing to safer, more cost-effective drilling operations.https://doi.org/10.1007/s42452-024-06415-xArtificial intelligencePetroleum engineeringHole cleaning indexGenetic algorithm |
| spellingShingle | Nageswara Rao Lakkimsetty Hassan Rashid Ali Al Araimi G. Kavitha Modelling and optimization of well hole cleaning using artificial intelligence techniques Discover Applied Sciences Artificial intelligence Petroleum engineering Hole cleaning index Genetic algorithm |
| title | Modelling and optimization of well hole cleaning using artificial intelligence techniques |
| title_full | Modelling and optimization of well hole cleaning using artificial intelligence techniques |
| title_fullStr | Modelling and optimization of well hole cleaning using artificial intelligence techniques |
| title_full_unstemmed | Modelling and optimization of well hole cleaning using artificial intelligence techniques |
| title_short | Modelling and optimization of well hole cleaning using artificial intelligence techniques |
| title_sort | modelling and optimization of well hole cleaning using artificial intelligence techniques |
| topic | Artificial intelligence Petroleum engineering Hole cleaning index Genetic algorithm |
| url | https://doi.org/10.1007/s42452-024-06415-x |
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