Evaluation of the performance of artificial neural networks integrated with whale optimization and ant colony optimization algorithms in estimating the drilling rate of penetration and compare with simple neural networks and mathematical conventional models

Document Type : Computer Article

Author

Masters degree of Petroleum Engineering, Science and Research Branch, Islamic Azad University, Tehran-Iran

Abstract

Rate of penetration (ROP) estimation in a drilling process is very important because it leads to the optimal selection of drilling parameters and reduction of the operating costs. The main purpose of this paper is to modeling and estimating ROP using optimized multilayer perceptron neural network with whale optimization algorithm (WOA-MLPNN), optimized multilayer perceptron neural network with ant colony optimization algorithm (ACO-MLPNN), back propagation multilayer perceptron neural network (BP-MLPNN) and two mathematical models including Bourgoyne and Young model (BYM) and Bingham model. The data required for development of the models were collected from the mud logging unit and the final reports of three drilled wells in an oil field located in southwestern Iran, which were first pre-processed to remove outliers and reduce noise. In the following, 12.25” hole-section information of two wells containing a similar sequence of drilled formations was used to train and test the models, and then the generated models were validated by the third well information. In the end, the performance of models was evaluated by statistical indicators and various graphical tools. The results of this study showed that the machine learning methods are much more accurate than conventional mathematical models. Also, more detailed studies showed that the WOA-MLPNN model with AAPRE values of 3.19, 5.48 and 9.31 for the three sections of training, testing and validation, respectively, has the highest performance compared to other models.

Keywords

References

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Journal of Modeling in Engineering
Volume 19, Issue 65
June 2021
Pages 115-135

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History

  • Receive Date: 15 February 2020
  • Revise Date: 11 February 2021
  • Accept Date: 04 April 2021

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