Use of Artificial Neural Networks in predicting rate of penetration and optimization weight on bit for several wells in Nam Rong - Doi Moi field, Vietnam

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 PVEP, Hanoi, Vietnam

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  • Received: 7th-Feb-2021
  • Revised: 16th-May-2021
  • Accepted: 16th-June-2021
  • Online: 10th-July-2021
Pages: 37 - 47
Views: 2817
Downloads: 1238
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Abstract:

Obtaining the maximum Rate of Penetration (ROP) by optimization of drilling parameters is the aim of every drilling engineer. This helps to save time, reduces cost and minimizes drilling problems. Since ROP depends on a lot of parameters, it is very difficult to predict it correctly. Therefore, it is necessary and important to investigate a solution for predicting ROP with high accuracy in order to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real - time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as weight on bit (WOB), weight of mud (MW), rotary speed (RPM), stand pipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when comparing to actual ROP, therefore it can be recommended as an effective and suitable method to predict ROP of other wells in research area. Besides, base on the proposed ANN model, authors carried out experiments and determine the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in in Nam Rong Doi Moi field.

How to Cite
Nguyen, H.Tien, Vu, D.Hong, Nguyen, V.The, Tram, D.Thi and Trung, P.Van 2021. Use of Artificial Neural Networks in predicting rate of penetration and optimization weight on bit for several wells in Nam Rong - Doi Moi field, Vietnam (in Vietnamese). Journal of Mining and Earth Sciences. 62, 3a (Jul, 2021), 37-47. DOI:https://doi.org/10.46326/JMES.2021.62(3a).05.
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