Detection of Changes in Oil Well Power Consumption Profile on the Basis of Dynamic Time Warping Algorithm

Ivan Luzyanin, Anton Petrochenkov

References:

1. L. Hailong, “The numerical simulation for multistage fractured horizontal well in low-permeability reservoirs based on modified Darcy’s equation”, Journal of Petroleum Exploration and Production Technology, vol. 7(3), pp. 735-746, 2017, DOI:10.1007/s13202-016-0283-1
2. V. V. Alekseev, A. P. Emel’yanov, and A. E. Kozyaruk, “Analysis of the dynamic performance of a variable-frequency induction motor drive using various control structures and algorithms”, Russian Electrical Engineering vol. 87, no. 4, pp. 181-188, DOI: 10.3103/S1068371216040027
3. D. C. Montgomery, “Design and Analysis of Experiments”, Wiley, 2012, 752 p.
4. Chatfield, C. “Time-series forecasting”, New York: Chapman and Hall, 2001;
5. Gurol Irzik “Causal Modeling and the Statistical Analysis of Causation”, 1996;
6. G. Takacs, Electrical submersible pump manual: design, operations, and maintenance, Gulf Professional Publishing, 2009, 420 p.
7. J.F.Gülich, Centrifugal pumps, 2nd Edition, Springer, 2010, 998 p.
8. Sakoe H., Chiba S., A Dynamic Programming Approach to Continuous Speech Recognition, In Proceedings of the 7th International Congress on Acoustics, vol. 3, 1971, pp. 65-69.
9. I. Luzyanin, A. Petrochenkov, B. Krause, Problems of tiny changes analysis in complex time series using dynamic time warping algorithm, Proceedings of the XIX International Conference on Soft Computing and Measurements. SCM`2016, 2016, pp. 419-422, DOI: 10.1109/SCM.2016.7519799
10. Y. Zhang, T. F. Edgar, A Robust Dynamic Time Warping Algorithm for Batch Trajectory Synchronization, American Control Conference, Seattle, June 2008, pp. 2864-2869.
11. C. Cassisi, P. Montalto et al. (2012). Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining, in: A. Karahoca (Ed.), “Advances in Data Mining Knowledge Discovery and Applications”, InTech, 2012, pp. 71-94.
12. L. R. Rabiner, A. E. Rosenberg and S. E. Levinson Considerations in Dynamic Time Warping Algorithms for Discrete Word Recognition, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-26, Dec. 1978, pp. 575-582.
13. M. Müller, Dynamic Time Warping, Information Retrieval for Music and Motion, Berlin Heidelberg: Springer 2007, pp. 69-84.
14. Y. Jeong, M. K. Jeong, O. A. Omitaomu, Weighted Dynamic Time Warping for Time Series Classification, Pattern Recognition, No. 44, 2011, pp. 2231-2240.
15. M. Kotas, J. M. Leski, and T. Moró, (2016). Dynamic time warping based on modified alignment costs for evoked potentials averaging DOI:10.1007/978-3-319-23437-3_26.
16. T. Giorgino, Computing and Visualizing Dynamic Time Warping Algorithms in R: The DTW Package, Journal of Statistical Software, Vol. 31, Issue 7, 2009, pp. 1-25.
17. M. Schmidt, M. Baumert et al., Two-Dimensional Warping for One-Dimensional Signals — Conceptual Framework and Application to ECG Processing, IEEE Transactions on Signal Processing, Vol. 62, No. 21, Nov. 2014, pp. 5577-5588. Proc. of the 6th International Conference on Applied Innovations in IT, (ICAIIT), March 2018