Artificial Neural Networks Models for Rate of Penetration Prediction in Rock Drilling
Subject Areas :
naser ebadati
1
(Department of Geology, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran)
Ronak Parvaneh
2
(Institute of Petroleum Engineering, School of Chemical Engineering, University of Tehran, Iran)
Mehrab Azizi
3
(Department of Petroleum Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Keywords:
Abstract :
Based on field data, there are various methods to reduce the cost of drilling wells. One of these methods is to optimize the drilling parameters to obtain the maximum rate of penetration (ROP). Many parameters affect ROP. The main purpose of this research is the use of smart networks for the penetration rate of drilling, for this purpose, well input data including drilling depth, duration of the drilling operation, speed of rotation of the drill, weight on the drill, weight and volume of drilling mud as input data. And the drilling penetration rate was prepared as output data from one of the fields located in the Persian Gulf. 70% of data is allocated for network training, 15% of data for validation and 15% of data for sensitivity analysis. According to the obtained results, it was found that using this tool, a good relationship with the total regression coefficient (0.96) is obtained for predicting the penetration rate using a neural network. Also, by repeating the calculations in repetition 12, the best value was obtained, which is equal to 14.24
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