Development of an enhanced torque and drag model using machine learning for optimizing drilling efficiency
DOI:
https://doi.org/10.15587/1729-4061.2025.312989Keywords:
torque and drag, machine learning, drilling efficiency, prediction of drilling parameters, neural networkAbstract
The object of this research is drilling process. The key is to ensure safe and efficient drilling operations by proactively identifying and eliminating critical anomalies, such as stuck pipes, that cause downtime, increase costs and degrade performance.
A machine learning model combining Multilayer Perceptron (MLP) and XGBoost was developed to predict critical parameters such as hook weight, minimum weight on bit, effective tension, and torque on bit. The model achieved 86 % accuracy in detecting drilling anomalies, including sinusoidal and spiral buckling. This enabled timely corrective actions and improving drilling efficiency.
The model’s accuracy is due to its ability to process large datasets and capture complex, nonlinear relationships between drilling parameters. By training on both historical and real-time field data, it can learn patterns that are difficult to detect with traditional tools which allows to predict of drilling anomalies in real-time.
The distinctive feature of this model is its adaptability to new data, as well as its ability to predict complex phenomena like helical buckling and torque fluctuations, which are challenging for traditional methods. Unlike conventional models that need manual tuning, this model continuously learns from data, improving over time and under varying conditions.
The model can be applied practically in real-time drilling operations to optimize drilling parameters, reduce the risk of stuck pipes, and minimize non-productive time
References
- Miguel Pérez-Enciso, Laura M. Zingaretti, A Guide for Using Deep Learning for Complex Trait Genomic Prediction, Genes, Barcelona-Spain, Genes 2019, 10, 553; doi:10.3390/genes10070553
- Johancsik, C.A., D.B. Friesen, and Rapier Dawson. 1984. Torque and Drag in Directional WellsPrediction and Measurement. Journal of Petroleum Technology 36(6): 987–992.
- Maidla, E. E. and Wojtanowicz, A.K. 1987. Field Comparison of 2-D and 3-D Methods for the Borehole Friction Evaluation in Directional Wells. Presented at 62nd SPE Annual Technical Conference, Dallas, Texas, September.
- Behounek, M., Hofer, E., Thetford, T., White, M., Yang, L., & Taccolini, M. (2017, March 14). Taking a Different Approach to Drilling Data Aggregation to Improve Drilling Performance. Society of Petroleum Engineers. doi: 10.2118/184741-MS
- James, G., Witten, D., Hastie, T., & Tibshirani, R. 2013. An introduction to statistical learning. New York: springer.
- Efron, B. and Tibshirani. R. 1994. An Introduction to the Bootstrap. New York: Chapman & Hall. Hadley Wickham and Romain Francois (2015). dplyr: A Grammar of Data Manipulation. R package version 0.4.1. http://CRAN.R-project.org/packagedplyr
- Breiman L. Random Forests[J].Machine Learning,2001,45(1):5-32.
- Wallace, Hegde and Gray (2015). System for Real Time Drilling Performance Optimization and Automation Based on Statistical Leanring Methods. Presented at SPE Middle East Intelligent Oil & Gas Conference & Exhibition, Abu Dhabi, United Arab Emirates, 15-16 September. SPE 176804.
- Burges, C. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data mining and knowledge discovery 2(2): 121–167.
- Ashok, P., Ambrus, A., Ramos, D., Lutteringer, J., Behounek, M., Yang, Y. L., ... Weaver, T. (2016, September 6). A Step by Step Approach to Improving Data Quality in Drilling Operations: Field Trials in North America. Society of Petroleum Engineers. doi: 10.2118/181076-MS
- Mojtaba Shahri, Timothy Wilson, Taylor Thetford, Brian Nelson, and Michael Behounek, Apache Corp.; Adrian Ambrus, Intellicess, Norwegian Re, Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study , 018 SPE Annual Technical Conference and Exhibition held in Dallas, Texas, 24-26 September 2018, SPE-191426-MS
- Kankan Bai*1, Honghai Fan1, Hongbao Zhang2,1, Fei Zhou1, Xingang Tao1, Real Time Torque and Drag Analysis by Combining of Physical Model and Machine Learning Method, Unconventional Resources Technology Conference held in Houston, Texas, USA, 20-22 June 2022, DOI 10.15530/urtec-2022-3723045
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Aizada Sharauova, Dinara Delikesheva, Asset Kabdula, Sharau Kadirbek, Nurlan Zaripov
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
