Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning

Abstract

Drilling motors are widely used in unconventional oil and gas exploration. Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. However, service companies face significant challenges in achieving predictive maintenance: operational data acquisition, automated statistics analysis, and drilling state recognition. This paper presents a miniature vibration recorder, an automatic statistical analysis method, and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency. The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle. Time-series data from the recorder can be used to automatically generate operation statistics, mitigating the costs incurred by manual data analysis. The layered recognition algorithm then enables the automatic identification of drilling operation states, i.e., surface, downhole non-drilling, downhole sliding, and downhole rotation. The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software, yielding a functional data collection, automatic data statistical analysis, and operation state recognition accuracy of 95%. Through achieving improved data collection and analysis, the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.

Keywords

Drilling dynamics / Operation state recognition / Random forest / Drilling motor

Author statement

Fei Li: Software, Resources, Data curation, Visualization, Project administration;

Haolan Song: Formal analysis, Writing—original draft preparation, Writing—review and editing, Supervision;

Yifan Wang: Writing—editing, Funding acquisition.

Declaration of competing interest

The authors declared that they have no conflicts of interest to this work.

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Acknowledgments

This research was funded by the National Science Foundation of China (U20B2029), China Key Research and Development Program (2023YFC2810900), Natural Science Basic Research Plan in Shaanxi Province (2023-JC-QN-0405), General Project of Shaanxi Province's Key Research and Development Plan (2024GX-YBXM-504)，Shaanxi Province Technical Innovation Guidance Special Project (2024ZC-YYDP-22), Shaanxi QinChuangYuan ‘Scientist + Engineer’ Team Construction Plan (2022kxj-125) and Shaanxi Universities’ Young Scholar Innovation Team and Xi’an Shiyou University’s Innovation Team.

References

[1] M.J. Sajid, Sar Khan, Edrs Gonzalez, Identifying contributing factors to China's declining share of renewable energy consumption: no silver bullet to decarbonisation, Environ. Sci. Pollut. Control Ser. 29 (47) (2022) 72017-72032.

[2] M.J. Sajid,Machine learned artificial neural networks vs linear regression: a case of Chinese carbon emissions, IOP Conf. Ser. Earth Environ. Sci. 495 (2020) 012044.

[3] S. W. Chesher, M. E. Williamson, B. B. Dougherty, R. Spencer, K. Schmitz, and S. K. Tyagi. Operator/service Company Research Collaboration Leads to Reduced Downhole Vibration Levels and Improved Drilling Performance. Society of Petroleum Engineers.

[4] Z. Zhang, Y. Shen, W. Chen, Z. Chen,Analyzing energy and efficiency of drilling system with mud motor through big data, in:SPE Annual Technical Conference and Exhibition, 2020.

[5] E. S. Sitinjak,B. Kurniawan, and Y. Besol. How to Use Drill String Vibration Data to Reveal Geology Phenomena in Geosteering Jobs: an Application Sensor at the Bit.

[6] K. Mills, S. Menand, N.H. Dao,Fatigue tracking for mud motors and mwds in unconventional wells, in:SPE Annual Technical Conference and Exhibition, 2018.

[7] P. Okoli, J.C. Vega, R. Shor,Estimating downhole vibration via machine learning techniques using only surface drilling parameters, in:SPE Western Regional Meeting, 2019.

[8] G. Dong, P. Chen, A Review of the Evaluation, Control, and Application Technologies for Drill String Vibrations and Shocks in Oil and Gas Well, 2016, 2016(PT.7):7418635.1-747418635.34.

[9] Oil and gas. https://www.shockwatch.com.au/oil-and-gas/.

[10] E. Temer and H. J. Pehl. Moving toward Smart Monitoring and Predictive Maintenance of Downhole Tools Using the Industrial Internet of Things Iiot.

[11] J.R. Jain, H. Oueslati, A. Hohl, H. Reckmann, G.P. Ostermeyer, High-frequency torsional dynamics of drilling systems: an analysis of the bit-system interaction, High-Frequency Torsional Dynamics of Drilling Systems, An Analysis of the Bit-System Interaction (2014), SPE/IADC Drilling Conference, Proceedings, 1. https://doi.org/10.2118/167968-MS.

[12] D. Castieira, R. Toronyi, N. Saleri,Machine learning and natural language processing for automated analysis of drilling and completion data, in:SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 2018.

[13] P. Lu, H. Liu,C. Serratella, and X. Wang. Assessment of data-driven, machine learning techniques for machinery prognostics of offshore assets. In Offshore Technology Conference.

[14] Fei Li, Vibration of drilling motors in unconventional oil and gas exploration with a miniature recorder, J. Eng. (2) (2020) 79-85, 2020.

[15] T. Baumgartner, E.V. Oort,Pure and coupled drill string vibration pattern recognition in high frequency downhole data, in:SPE Annual Technical Conference and Exhibition, 2014.

[16] D. Cao, C. Loesel, S. Paranji,Rapid Development of Real-Time Drilling Analytics System, 2018.

[17] C. Coley,Building a rig state classifier using supervised machine learning to support invisible lost time analysis, in:SPE/IADC International Drilling Conference and Exhibition, 2019.

[18] Y. Yu, Q. Liu, S. Chambon, M. Hamzah,Using deep kalman filter to predict drilling time series, in:International Petroleum Technology Conference, 2019.

[19] Y. Ben, W. Han, C. James, D. Cao,Building a general and sustainable machine learning solution in a real-time drilling system, in:IADC/SPE International Drilling Conference and Exhibition, 2020.

[20] G. Veres, Z. Sabeur,Data analytics for drilling operational states classifications, in:European Symposium on Artificial Networks, Computational Intelligence and Machine Learning (ESANN2015), 2015.

[21] 09, Enhanced Kick Detection With Low-Cost Rig Sensors through Automated Pattern Recognition And Real-Time Sensor Calibration,Volume Day 2 Wed, September 16, 2015 of SPE Middle East Intelligent Oil And Gas Symposium, 2015. D021S006R001.

[22] J. Cutler, M. Dickenson,Introduction to Machine Learning with python, 2020.

[23] F. Li, H. Song,Post-job data analysis by python for a miniature recorder in drilling motors, in:IOP Conference Series Materials Science and Engineering, 799, 2020 012048.

[24] John D. Macpherson, P. Pastusek M. Behounek R. Harmer,A framework for transparency in drilling mechanics and dynamics, in:SPE Annual Technical Conference and Exhibition, 2015.