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

  • Yanru Zhao
  • Amin Noorbakhsh
  • Mohammadreza KoopialipoorEmail author
  • Aydin Azizi
  • M. M. Tahir
Original Article


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.


Rate of penetration Optimization Prediction Artificial neural network, Artificial bee colony algorithm 



The authors would like to express their sincere appreciation to reviewers because of their valuable comments that increased the quality of our paper. Funding support from Shenzhen Science and Technology Innovation Commission (grant No. JCYJ 20160531192824598, JCYJ 20170811160740635) are greatly appreciated.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Yanru Zhao
    • 1
  • Amin Noorbakhsh
    • 2
  • Mohammadreza Koopialipoor
    • 3
    Email author
  • Aydin Azizi
    • 4
  • M. M. Tahir
    • 5
  1. 1.College of Civil EngineeringShenzhen UniversityShenzhenChina
  2. 2.Department of Petroleum EngineeringAmirkabir University of TechnologyTehranIran
  3. 3.Faculty of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  4. 4.Engineering DepartmentGerman University of TechnologyMuscatOman
  5. 5.UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia

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