A new methodology for optimization and prediction of rate of penetration during drilling operations
Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is a...
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| Format: | Article |
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Springer
2020
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| author | Zhao, Yanru Noorbakhsh, Amin Koopialipoor, Mohammadreza Azizi, Aydin Tahir, M. M. |
| author_facet | Zhao, Yanru Noorbakhsh, Amin Koopialipoor, Mohammadreza Azizi, Aydin Tahir, M. M. |
| author_sort | Zhao, Yanru |
| collection | ePrints |
| description | Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations. |
| first_indexed | 2024-03-05T20:41:15Z |
| format | Article |
| id | utm.eprints-87211 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:41:15Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | utm.eprints-872112020-10-31T12:26:46Z http://eprints.utm.my/87211/ A new methodology for optimization and prediction of rate of penetration during drilling operations Zhao, Yanru Noorbakhsh, Amin Koopialipoor, Mohammadreza Azizi, Aydin Tahir, M. M. TA Engineering (General). Civil engineering (General) Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations. Springer 2020 Article PeerReviewed Zhao, Yanru and Noorbakhsh, Amin and Koopialipoor, Mohammadreza and Azizi, Aydin and Tahir, M. M. (2020) A new methodology for optimization and prediction of rate of penetration during drilling operations. Engineering with Computers, 36 (2). pp. 587-595. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00715-2 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Zhao, Yanru Noorbakhsh, Amin Koopialipoor, Mohammadreza Azizi, Aydin Tahir, M. M. A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title | A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title_full | A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title_fullStr | A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title_full_unstemmed | A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title_short | A new methodology for optimization and prediction of rate of penetration during drilling operations |
| title_sort | new methodology for optimization and prediction of rate of penetration during drilling operations |
| topic | TA Engineering (General). Civil engineering (General) |
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