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Model-based predictive control of the well drilling process

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Parent Category: 2026

Category: Content №2 2026

Created on 25 April 2026

Last Updated on 25 April 2026

Published on 30 November -0001

Written by V. S. Morkun, N. V. Morkun, Ye. Yu. Bobrov, Ya. O. Hryshchenko

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Authors:

V. S. Morkun, orcid.org/0000-0003-1506-9759,  University of Bayreuth, Bayreuth, Federal Republic of Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N. V. Morkun, orcid.org/0000-0002-1261-1170,  University of Bayreuth, Bayreuth, Federal Republic of Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ye. Yu. Bobrov, orcid.org/0009-0008-3251-300X, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ya. O. Hryshchenko*, orcid.org/0009-0002-0582-4140, Volodymyr Dahl East Ukrainian National University, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (2): 013 - 021

https://doi.org/10.33271/nvngu/2026-2/013

Abstract:

Purpose. Improving the quality and efficiency of well drilling process management by applying a model-predictive control (MPC) strategy.

Methodology. The following methods were used in the work: analysis of scientific and practical solutions; methods of analytical synthesis; methods of computer modeling for the synthesis and analysis of mathematical models; and statistical methods for processing the results of experimental studies.

Findings. The researched method of controlling a drilling rig based on an MPC controller involves the use of adjustable variables such as bit rotation speed (RPM), bit load (weight) (WOB), and pressure in the well cleaning system (P). The purpose of control is to maintain the rate of penetration (ROP) at a nominal set value corresponding to the estimated physical and mechanical properties of the rock being drilled (PMP). In traditional control systems, proportional-integral-derivative (PID) controllers generate control actions based on historical data without predicting the state of existing systems and cannot take constraints into account. The use of the MPC strategy allows the formation of a multivariable system that takes into account constraints and includes various elements, such as nonlinear, higher-order, interrelated, etc. MPC uses a physical model to predict future system states, thereby facilitating informed decisions that optimize current and future system performance.

Originality. The automated drilling rig control system has been improved by using two levels in its structure. The lower level is represented by closed loops of proportional-integral-derivative control of the load on the drill bit, its rotation speed, and the pressure in the well cleaning system. The upper level uses model-predictive control for dynamic adjustment of the set values of the specified controlled variables, the magnitude of which is determined by operational estimates of the characteristics of mineralogical and technological varieties of ore.

Practical value. The technology described in the has been tested and can be recommended for improving the efficiency of drilling operations in iron ore quarries. The use of the proposed approach to forming a model-predictive control system for the technological process of drilling wells allows improving the quality of control, reducing the time of transitional processes, increasing the speed of drilling, and reducing energy consumption.

Keywords: controlling, control actions, modeling

References.

1. World Economic Forum (2017). Digital Transformation Initiative. Mining and Metals Industry. 2017. Retrieved from http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-mining-and-metals-white-paper.pdf

2. Morkun, V., Morkun, N., & Tron, V. (2017). Automatic control of the ore suspension solid phase parameters using high-energy ultrasound. Radio electronics computer science control, 3, 175-182. https://doi.org/10.15588/1607-3274-2017-3-19

3. Morkun, V., Morkun, N., Tron, V., Hryshchenko, S., Serdyuk, O., & Dotsenko, I. (2019). Basic regularities of assessing ore pulp parameters in gravity settling of solid phase particles based on ultrasonic measurements. Archives of Acoustics, 44(1), 161-167. https://doi.org/10.24425/aoa.2019.126362

4. Veysi, Р., Adeli, M., & Naziri, N. P. (2024). Introduction to PID Controller and Model Predictive Control in Engineering Systems. Preprints. Retrieved from https://www.researchgate.net/publication/379956442

5. Dittmar, R. (2019). Model Predictive Control mit MATLAB und Simulink – Model Predictive Control with MATLAB and Simulink. https://doi.org/10.5772/intechopen.86001. ISBN978-1-83880-096-3

6. Azaryan, A. A., Batareyev, O. S., Karamanits, F. I., Kolosov,V. O., & Morkun, V. S. (2018). Ways to reduce ore losses and dilution in iron ore underground mining in kryvbass. Science and innovation, 14(4), 17-24. https://doi.org/10.15407/scine14.03.017

7. Nascimento, A., Tamas Kutas, D., Elmgerbi, A., Thonhauser, G., & Hugo Mathias, M. (2015). Mathematical Modeling Applied to Drilling Engineering: An Application of Bourgoyne and Young ROP Model to a Presalt Case Study. Mathematical Problems in Engineering, 631290, 9. https://doi.org/10.1155/2015/631290

8. Sauki, A., Megat Khamaruddin, PNF., Irawan, S., Kinif, I., Ridha, S., Ab-dulbari Ali, S., & Mohd Aswade Ali (2021). Development of a modified Bourgoyne and Young model for predicting drilling rate. Journal of Petroleum Science and Engineering, 205, 108994. https://doi.org/10.1016/j.petrol.2021.108994

9. Cui, M., Sun, M., Zhang, J., Kang, K., & Luo, Y. (2014). Maximizing drilling perfor-mance with real-time surveillance system based on parameters opti-mization algorithm. Advances in Petroleum Exploration and Development, 8(1), 15-24. https://doi.org/10.3968/5537

10.      Sharma, A., Al Dushaishi, M., & Nygaard, R. (2021). Fixed bit rotary drilling failure criteria effect on drilling vibration. Conference: 55^th U.S. Rock Mechanics/Geomechanics Symposium, Virtual, June 2021; Related Information: ARMA-2021-2083, OSTI ID:1844942.

11.      Chen, X., Fan, H., Guo, B., Gao, D., Wei, H., & Ye, Z. (2014). Real-Time Prediction and Optimization of Drilling Performance Based on a New Mechanical Specific Energy Model. Arabian Journal for Science and Engineering, 39, 8221-8231. https://doi.org/10.1007/s13369-014-1376-0

12.      Yong-Xing, S., Li-Hua, Q., Hai-Fang, S., Lie-Xiang, H., & Guo, Z. (2016). Real-time Surveillance System of Mechanical Specific Energy Applied in Drilling Parameters Optimization. Conference: 2 ^nd Annual International Conference on Advanced Material Engineering (AME 2016). https://doi.org/10.2991/ame-16.2016.128

13.      Aldred, W., Bourque, J., Mannering, M., & Chapman, C. (2012). Drilling Automation. Oilfield Review, 24(2). Retrieved from https://www.researchgate.net/publication/248391350

14.      Zang, C., Lu, Z., Ye, S., Xu, X., Xi, C., Song, X., Guo, Y., & Pan, T. (2022). Drilling Parameters Optimization for Horizontal Wells Based on a Multiobjec-tive Genetic Algorithm to Improve the Rate of Penetration and Reduce Drill String Drag. Applied Sciences, 12(22),11704. https://doi.org/10.3390/app122211704

15.      Chen, X., Gao, D., Guo, B., & Feng, Y. (2016). Real-time optimization of drilling parameters based on mechanical specific energy for rotating drilling with positive displacement motor in the hard formation. Journal of Natural Gas Science and Engineering, 35, 686-694. https://doi.org/10.1016/j.jngse.2016.09.019

16.      Khaksar Manshad, A., Aghayari, M., Sabir Mahmood, B., Tabaeh Hayavi, M., H. Mohammadi, A., & A. Ali, J. (2021). Stability analysis and trajectory optimization of vertical and deviated boreholes using the extended-Mogi-Coulomb criterion and poly-axial test data. Upstream Oil and Gas Technology, 7, 00052. https://doi.org/10.1016/j.upstre.2021.100052

17.      Hankins, D., Salehi, S., & Fatemeh, K. (2015). An integrated approach for drilling optimization using advanced drilling optimizer. Journal of Petroleum Engineering, 1-12. https://doi.org/10.1155/2015/281276

18.      Hegde, C., Daigle, H., & Gray, K.E. (2018). Performance comparison of algorithms for real-time rate-of-penetration optimization in drilling using data-driven models. SPE Journal, 23(05), 1706-1722. https://doi.org/10.2118/191141-PA

19.      Gray, K. E., & Hegde, C. (2017). Use of machine learning and data analytic to increase drilling efficiency for nearby wells. Journal of Natural Gas Science and Engineering, 40(04). https://doi.org/10.1016/j.jngse.2017.02.019

20.      Abughaban, M., Alshaarawi, A., & Meng, C. (2019). Optimization of drilling performance based on an intelligent drilling advisory system. Proceedings of the International Petroleum Technology Conference, Beijing, China, 26–28 March 2019. https://doi.org/10.2523/IPTC-19269-MS

21.      Payette, G. S., Spivey, B. J., Wang, L., & Bailey, J. R. (2017). Real-time well-site based surveillance and optimization platform for drilling: Technology, basic workflows and field results. Proceedings of the SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14–16 March 2017. https://doi.org/10.2118/184615-MS

22.      Gidh, Y., Purwanto, A., & Bits, S. (2012). Artificial Neural Network Drilling Parame-ter Optimization System Improves ROP by Predicting/Managing Bit Wear. Proceedings of the SPE Intelligent Energy International, Utrecht, The Netherlands, 27–29 March 2012. https://doi.org/10.2118/149801-MS

23.      Guria, C., Goli, K.K., & Pathak, A.K. (2014). Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm. Petroleum Science, 11, 97-110. https://doi.org/10.1007/s12182-014-0321-x

24.      Ammar, A., Mahmoud, A., & Beshir, M. (2021). Hybrid data driven drilling and rate of penetration optimization. Journal of Petroleum Science and Engineering, 200, 108075. https://doi.org/10.1016/j.petrol.2020.108075

25.      Model Predictive Control Toolbox. Design and simulate model predictive controllers. Retrieved from https://se.mathworks.com/products/model-predictive-control.html

26.      Pfeiffer, B-M., Grieb, H., Lorenz, O., & Losert, D. (2014). Applications of DCS embedded Model Predictive Control. atp magazine, 56(03), 28-37. https://doi.org/10.17560/atp.v56i03.2237

27.      Ordys, A.W., Pike, A.W., Johnson, M.A., Katebi, R.M., & Grimble, M.J. (2012). Modelling і Simulation of Power Generation Plants. Advances in Industrial Control. Springer Science & Business Media. ISBN 1447121147; 9781447121145.

28. Controller State Estimation. Retrieved from https://se.mathworks.com/help/mpc/ug/controller-state-estimation.html

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