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Extraction of Characteristics about Reservior Logging Signals Based on Nonlinear Chaos

Author: LiZe
Tutor: NieChunYan
School: Changchun University of Science and Technology
Course: Information and Communication Engineering
Keywords: Nonstationary Chaos Time Series Lyapunov exponent Logging Signals
CLC: O415.5
Type: Master's thesis
Year: 2011
Downloads: 23
Quote: 0
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Abstract


With the rapid development of society , the gradual increase in demand for oil . Although China's demand for oil , but the oil and gas exploitation rate is not great . In the oil fields of the mining process , logging time sequence to identify the logging has been a problem in the field of oil exploration . In the past, people often use artificial way to carry out the investigation the oilfield cyclical longer , but the effect is not very satisfactory , oil exploration also want the computer as soon as possible access to the oil field surveys , in order to achieve fast and accurate positioning often log time series as stationary signal processing by calculating the mean, variance , etc. , hope to summarize some of the law , but actually logging signal non-stationary and chaotic , just by these linear the method is not enough to extract the related features therein . In this paper, chaotic method of logging time series analysis to verify the sequence of non-stationary and chaotic characteristics , the In research log time series based on the mechanism and the basic characteristics of the extracted logging time series of three kinds of chaotic characteristics of a correlation dimension , largest Lyapunov exponent and approximate entropy . The experimental results show that the reservoir largest Lyapunov exponent , correlation dimension and approximate entropy and its contained fluid nature constitute preliminary method to predict the location of the main reservoir group by extracting characteristic parameters such as the largest Lyapunov exponent is feasible .

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CLC: > Mathematical sciences and chemical > Physics > Theoretical Physics > Nonlinear physics > Chaos Theory
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