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Engineering with Computers
Erschienen in: Engineering with Computers 2/2020

31.01.2019 | Original Article

A new methodology for optimization and prediction of rate of penetration during drilling operations

verfasst von: Yanru Zhao, Amin Noorbakhsh, Mohammadreza Koopialipoor, Aydin Azizi, M. M. Tahir

Erschienen in: Engineering with Computers | Ausgabe 2/2020

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Abstract

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.
Vorheriger Artikel Numerical shape optimization based on meshless method and stochastic optimization technique
Nächster Artikel Prediction of aerodynamics performance of continuously variable-speed wind turbine by adaptive neuro-fuzzy methodology

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Metadaten
Titel
A new methodology for optimization and prediction of rate of penetration during drilling operations
verfasst von
Yanru Zhao
Amin Noorbakhsh
Mohammadreza Koopialipoor
Aydin Azizi
M. M. Tahir
Publikationsdatum
31.01.2019
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 2/2020
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-019-00715-2

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