Predicting Rate of Penetration and optimization Weight on bit using Artificial Neural Networks

Tien Hung Nguyen (1), The Vinh Nguyen (2), Hong Duong Vu (3)
(1) a:1:{s:5:"en_US";s:38:"Hanoi university of Mining and Geology";}, Viet Nam,
(2) Faculty of oil and gas, Hanoi university of Mining and Geology, 18 Vien, Duc Thang, Bac Tu Lim, Hanoi, Vietnam, Viet Nam,
(3) Faculty of oil and gas, Hanoi university of Mining and Geology, 18 Vien, Duc Thang, Bac Tu Lim, Hanoi, Vietnam, Viet Nam


Achieving the greatest Rate of Penetration (ROP) is the aim of each drilling engineer because it could save time, diminish cost and limit drilling problems. Nonetheless, ROP could be affected by many drilling parameters which lead to complication in its prediction. Subsequently, it is essential and critical to propose a new approach to predict ROP with high accuracy and optimize drilling parameters. In this review, another methodology utilizing Artificial Neural Network (ANN) has been proposed to estimate ROP from real – time drilling data of a few wells in Nam Rong - Doi Moi oil field, Vietnam with more than 900 datasets included significant parameters like rotary speed (RPM), the weight on bit (WOB), standpipe pressure (SPP), flow rate (FR), weight of mud (MW), torque (TQ). The number of neurons in the hidden layer were varied then the results of different ANN models were compared in order to obtain the optimal model. The final ANN model shows high exactness when contrasted with actual ROP, in this manner it tends to be suggested as a successful and reasonable approach to predict the ROP of different wells in Nam Rong – Doi Moi field. Also, based on the proposed ANN model, the optimal WOB was determine for the drilling interval from 1800 to 2300 m of oil wells in research region.

Full text article

Generated from XML file


Adetifa, O., Iyalla, I., & Amadi, K. (2021). Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction. SPE Nigeria Annual International Conference and Exhibition, D021S008R008.

Al-AbdulJabbar, A., Elkatatny, S., Mahmoud, M., & Abdulraheem, A. (2018). Predicting rate of penetration using artificial intelligence techniques. SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition.

AL-Mahasneh, M. A. (2017). Optimization drilling parameters performance during drilling in gas wells. Int J Oil Gas Coal Eng, 5, 19–26.

Bani Mustafa, A., Abbas, A. K., Alsaba, M., & Alameen, M. (2021). Improving drilling performance through optimizing controllable drilling parameters. Journal of Petroleum Exploration and Production, 11, 1223–1232.

Bingham, M. G. (1965). A new approach to interpreting--rock drillability. (No Title).

Bourgoyne Jr, A. T., & 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, S., & Kumar, G. S. (2020). Drilling efficiency improvement and rate of penetration optimization by machine learning and data analytics. International Journal of Mathematical, Engineering and Management Sciences, 5(3), 381.

Elkatatny, S. M., Tariq, Z., Mahmoud, M. A., & Al-AbdulJabbar, A. (2017). Optimization of rate of penetration using artificial intelligent techniques. ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2017.

Eren, T., & Ozbayoglu, M. E. (2010). Real time optimization of drilling parameters during drilling operations. SPE Oil and Gas India Conference and Exhibition?, SPE-129126.

Hadi, F., Altaie, H., & AlKamil, E. (2019). Modeling rate of penetration using artificial intelligent system and multiple regression analysis. Abu Dhabi International Petroleum Exhibition and Conference, D021S032R001.

Irawan, S., Rahman, A., & Tunio, S. (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), 1690–1695.

Jahanbakhshi, R., Keshavarzi, R., & Jafarnezhad, A. (2012). Real-time prediction of rate of penetration during drilling operation in oil and gas wells. ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2012.

Kahraman, S. (2016). Estimating the penetration rate in diamond drilling in laboratory works using the regression and artificial neural network analysis. Neural Processing Letters, 43, 523–535.

Maurer, W. C. (1962). The" perfect-cleaning" theory of rotary drilling. Journal of Petroleum Technology, 14(11), 1270–1274.

Mohaghegh, S. (2000). Virtual-intelligence applications in petroleum engineering: Part 1—Artificial neural networks. Journal of Petroleum Technology, 52(09), 64–73.

Moran, D., Ibrahim, H., Purwanto, A., & Osmond, J. (2010). Sophisticated ROP prediction technologies based on neural network delivers accurate drill time results. IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition.

Tripathy, S. S. (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), 233.

Соловьев, Н. В., & Нгуен, Т. Х. (2015). Разработка элементов эффективной технологии бурения скважин на месторождениях углеводородов предприятия" Вьетсовпетро". Инженер-Нефтяник, 2, 45–49.

Барон, Л.И., Берон, А.И., Алехова, З.Н., 1966. Разрушение горных пород механическими способами при бурении скважин. Наука. М. 244 c.

Нескромных, В.В., 2015. Разрушение горных пород при проведении геолого-разведочных работ. Сибирский федеральный университет. Красноярск, 396 с.

Нескромных, В.В., 2017. Разрушение горных пород при бурении скважин. Сибирский федеральный университет. Красноярск, 336 с.


Tien Hung Nguyen (Primary Contact)
The Vinh Nguyen
Hong Duong Vu
Nguyen, T. H., Nguyen, T., & Vu, H. (2022). Predicting Rate of Penetration and optimization Weight on bit using Artificial Neural Networks. Journal of Earth Energy Engineering, 11(2), 102–112.

Article Details

Received 2021-11-25
Accepted 2022-05-17
Published 2022-09-10