Extraction Of Characteristics About Reservior Logging Signals Based On Nonlinear Chaos

With the rapid development, people's demand for oil increases. Although the great demand for oil in China, but the exploitation of oil and gas range is not great. Mining process in the field, the use of log time series to identify the log-phase oil field has been a problem. In the past, people often use artificial means to carry out investigation on the field, periodic long, and the effect is not very good, so I want the computer to oil exploration personnel access to the field investigation as soon as possible in order to achieve fast, accurate positioning, People often log time sequence as a stationary signal processing, by computing its mean, variance, etc., hoping to summarize some of the relevant law, but in fact non-stationary, and chaos logging signal, and only by the linear which is not sufficient to extract relevant features. In this paper, the method of chaotic time sequence analysis of logging, through the sequence of non-stationary and chaotic characteristics of validation, logging time series generated in the study and the basic features of the mechanism on the basis of the three extraction log time sequence kind of chaotic characteristics-correlation dimension, largest Lyapunov exponent and approximate entropy. The results show that the largest reservoir Lyapunov exponent, correlation dimension and approximate entropy contained in the fluid nature of its composition is related to the maximum Lyapunov exponent by extracting the feature parameters to predict the main oil reservoir in the initial position of the method is feasible.