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- W2014232390 abstract "Pure silica core optical fiber is commonly used as the sensing fiber in Raman-backscatter distributed temperature sensors (DTS) in heavy oil thermal well. However the sensing signal collected from this type of fiber statistically belongs to nonstationary random process which cannot be effectively de-noised by simply applying conventional methods. To solve this problem, we develop a novel noise suppression algorithm by combining wavelet multi-scale analysis and moving grey model GM(1,1). The algorithm first applies wavelet de-noising in spatial domain of temperature profile to remove the high frequency noise, then uses moving GM(1,1) method to remove both high frequency and low frequency nonstationary noise in time domain. Autoregressive (AR) model and least square regression are used to optimize the forecasting parameters of GM(1,1). Finally the results of both domains are reconstructed to obtain the de-noised profile. Long-term field test was proposed on the Karamay oil field F11051 steam stimulation well, Xinjiang Province, China. Field test result shows that signal to noise ratio (SNR) is improved by 11dB using the algorithm." @default.
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- W2014232390 date "2011-11-23" @default.
- W2014232390 modified "2023-09-23" @default.
- W2014232390 title "A new noise suppression algorithm for optical fiber temperature surveillance of heavy oil thermal recovery well" @default.
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- W2014232390 doi "https://doi.org/10.1117/12.906604" @default.
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