Development of a set of methods for preforecasting fractal time series analysis to determine the level of persistence

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.104425

Keywords:

persistence, Hurst exponent, time series, fractal dimension, sequential R/S analysis

Abstract

A set of methods for the pre-forecasting fractal time series analysis to determine the levels of persistence of chaotic information flows in the well-drilling control system is proposed. Based on this methodology, the values of the Hurst exponent H, fractal dimension D, spatial dimension n and correlation measure C are obtained for six time series. Since the Hurst exponent H for all the signals is greater than 0.5, the conclusion about a chaotic nature of the studied time series is made. However, the dynamics of these signals will not change and it can be predicted that it will evolve in the same direction as in the past. This allows using the obtained results for forecasting and early detection of deviations of the drilling process from the norm. Since the oil and gas well drilling process is a complex stochastic process proceeding in conditions of a priori and current uncertainty under the influence of immeasurable disturbances, calculation of the Hurst exponent H contributes to solving the forecasting problems in the automated support system of decision-making regarding well-drilling process control

Author Biographies

Vitaliai Kropyvnytska, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

PhD, Associate Professor

Department of computer systems and networks

Lev Kopystynskyy, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

Postgraduate student

Department of automation computer-integrated technologies

Georgiy Sementsov, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

Doctor of Technical Sciences, Professor

Department of automation computer-integrated technologies

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Published

2017-06-30

How to Cite

Kropyvnytska, V., Kopystynskyy, L., & Sementsov, G. (2017). Development of a set of methods for preforecasting fractal time series analysis to determine the level of persistence. Eastern-European Journal of Enterprise Technologies, 3(4 (87), 10–17. https://doi.org/10.15587/1729-4061.2017.104425

Issue

Vol. 3 No. 4 (87) (2017): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2017 Vitaliai Kropyvnytska, Lev Kopystynskyy, Georgiy Sementsov

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