Archives
 
Special Issues
 
Vol 62, Issue 3a, [07 - 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

DOI:10.46326/JMES.2021.62(3a).05

  • Affiliations:

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

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 7th-Feb-2021
  • Revised: 16th-May-2021
  • Accepted: 16th-June-2021
  • Online: 10th-July-2021
Pages: 37 - 47
Views: 3373
View onlineView online
Downloads: 1239
Downdload PDF
★★★★★Rating: 1.0, Total rating: 123
  • ★
  • ★
  • ★
  • ★
  • ★
Yours rating

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.
References

Azim Reda Abdel, (2020). Application of artificial neural network in optimizing the drilling rate of penetration of western desert Egyptian wells. Springer Nature Switzerland AG.

Baron L.I., Beron A.I., Alekhova Z. N. và nnk (1996). Nguyên lý phá huỷ đất đá trong khoan. Khoa Học. Mátx-Cơ-Va. 244 trang. Барон Л. И., Берон А. И., Алехова З. Н. и другие (1966). Разрушение горных пород механическими способами при бурении скважин. Наука. М. 244 c.

Bourgoyne Jr A. T., and Young Jr F. S. (1974). A multiple regression approach to optimal drilling and abnormal pressure detection. Society of Petroleum Engineers Journal, 14(04), 371 - 384.

Chandrasekaran Sridharan, Kumar G. Suresh (2020). Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics. International Journal of Mathematical, Engineering and Management Sciences Vol. 5, No. 3, 381 - 394.

Irawan Sonny, Tunio Saleem Qadir., (2012) Optimization of Weight on Bit During Drilling Operation Based on Rate of Penetration Model. Research Journal of Applied Sciences, Engineering and Technology 4(12).

Neskoromnux V.V., (2015). Nguyên lý phá huỷ đất đá trong khoan thăm dò. Trường ĐH Serbria. Krasnodar. 396 trang. Нескромных В. В., (2015). Разрушение горных пород при проведении геолого - разведочных работ. Сибирский федеральный университет. Красноярск, 396 с.

Neskoromnux V.V., (2017). Nguyên lý phá huỷ đất đá trong khoan thăm dò. Trường ĐH Serbria. Krasnodar. 336 trang. Нескромных В. В., (2017). Разрушение горных пород при бурении скважин. Сибирский федеральный университет. Красноярск, 336 с.

Mohaghegh Shahab, (2000). Part 1 - Artificial Neural Networks, Virtual - Intelligence Applications in Petroleum Engineering. Journal of Petroleum Technology, 52(9), 64 - 73.

Soloviev N.V., Nguyen Tien Hung, (2015). Công nghệ khoan tại các mỏ dầu khí thuộc Xí nghiệp Liên doanh Việt - Nga. Tạp chí KHKT “Kỹ sư Dầu khí”. Số 2. Trang 45-49. Соловьев Н. В., Нгуен Тиен Хунг, (2015). Разработка элементов эффективной технологии бурения скважин на месторождениях углеводородов предприятия «Вьетсовпетро». Научно - технический журнал «Инженер - нефтяник». - No2. - C. 45 - 49.

Tripathy S. S., Saxena R. K., Gupta P. K., (2013). Comparison of statistical methods for outlier detection in proficiency testing data on analysis of lead in aqueous solution. American Journal of Theoretical and Applied Statistics 2(6).

Other articles