In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid performance, allowing us to employ an OS-ELM algorithm derived from the extreme learning machine. Particularly, the proposed prediction method is bidirectional to identify an appropriate slurry formulation. The forward prediction model is established to predict the fluid performance, where the mud additive contents are inputs, and the drilling fluid properties parameters are outputs. Correspondingly, the backward prediction model is established to modify the slurry formula, where differences in the drilling fluid properties are inputs and percentages of slurry additives amount are output. The simulation results show that the two-direction OS-ELM prediction model can better predict the drilling fluid properties in water well drilling.

1. [1] M. Sathishkumar and Y.-C. Liu, “Hybrid-triggered reliable dissipative control for singular networked cascade control systems with cyber-attacks,” J. of the Franklin Institute, Vol.357, No.7, pp. 4008-4033, 2020. https://doi.org/10.1016/j.jfranklin.2020.01.013
2. [2] G. Jiang et al., “Research status and development directions of intelligent drilling fluid technologies,” Petroleum Exploration and Development, Vol.49, No.3, pp. 660-670, 2022.
3. [3] D. Wen et al., “Research and application of the intelligent and efficient small-size integrated mud non-landing system,” Drilling Engineering, Vol.49, No.4, pp. 49-54, 2022 (in Chinese).
4. [4] R. Zhang et al., “Preparation of a high-temperature-resistant lightening agent and its application in a cement slurry system,” J. of Applied Polymer Science, Vol.136, No.13, Article No.47292, 2019. https://doi.org/10.1002/app.47292
5. [5] J.-H. Zhao et al., “Calcium cemented geological bored pile mud ratio test,” TranspoWorld, Vol.2019, No.34, pp. 106-108+132, 2019 (in Chinese). https://doi.org/10.16248/j.cnki.11-3723/u.2019.34.134
6. [6] Y. Qin et al., “Numerical study of the dynamics of the hole formation during drilling with combined ms and ns laser pulses,” Optics & Laser Technology, Vol.112, pp. 8-19, 2019. https://doi.org/10.1016/j.optlastec.2018.10.057
7. [7] D. Liao-McPherson, M. M. Nicotra, and I. Kolmanovsky, “Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction,” Automatica, Vol.117, Article No.108973, 2020. https://doi.org/10.1016/j.automatica.2020.108973
8. [8] M. Mitrevska et al., “Discrete terminal sliding mode repetitive control for a linear actuator with nonlinear friction and uncertainties,” Int. J. of Robust and Nonlinear Control, Vol.29, No.13, pp. 4285-4297, 2019. https://doi.org/10.1002/rnc.4639
9. [9] X.-M. Wu et al., “Mud and geotechnical engineering slurry,” pp. 32-50, China University of Geosciences Press, 2002 (in Chinese).
10. [10] X.-M. Wu et al., “Experimental principles and methods of drilling fluid and geotechnical engineering slurry,” pp. 23-31, China University of Geosciences Press, 2010 (in Chinese).
11. [11] G.-B. Huang et al., “On-line sequential extreme learning machine,” Proc. of the IASTED Int. Conf. on Computational Intelligence, pp. 232-237, 2005.
12. [12] G.-B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, Vol.70, Nos.16-18, pp. 3056-3062, 2007. https://doi.org/10.1016/j.neucom.2007.02.009
13. [13] C. Deng et al., “Extreme learning machines: New trends and applications,” Science China Information Sciences, Vol.58, No.2, pp. 1-16, 2015. https://doi.org/10.1007/s11432-014-5269-3
14. [14] L. Yan et al., “An improved OS-ELM based real-time prognostic method towards singularity perturbation phenomenon,” Measurement, Vol.182, Article No.109673, 2021. https://doi.org/10.1016/j.measurement.2021.109673
15. [15] F. Lu et al., “Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm,” Aerospace Science and Technology, Vol.84, pp. 661-671, 2019. https://doi.org/10.1016/j.ast.2018.09.044
16. [16] H. Yu, X. Sun, and X. Yan, “Sequential prediction for imbalanced data stream via weighted OS-ELM and dynamic GAN,” Intelligent Data Analysis, Vol.23, No.6, pp. 1191-1204, 2019. https://doi.org/10.3233/IDA-184377