Using wavelet‐domain adaptive filtering to improve signal‐to‐noise ratio of nuclear magnetic resonance log data from tight gas sands |
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| Authors: | Youbin Wu Kang Liu Mi Liu Xiangning Meng |
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| Affiliation: | 1. Reservoirs Evaluation Center, China Petroleum Logging Co. Ltd, Xi'an, China;2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China |
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| Abstract: | In tight gas sands, the signal‐to‐noise ratio of nuclear magnetic resonance log data is usually low, which limits the application of nuclear magnetic resonance logs in this type of reservoir. This project uses the method of wavelet‐domain adaptive filtering to denoise the nuclear magnetic resonance log data from tight gas sands. The principles of the maximum correlation coefficient and the minimum root mean square error are used to decide on the optimal basis function for wavelet transformation. The feasibility and the effectiveness of this method are verified by analysing the numerical simulation results and core experimental data. Compared with the wavelet thresholding denoise method, this adaptive filtering method is more effective in noise filtering, which can improve the signal‐to‐noise ratio of nuclear magnetic resonance data and the inversion precision of transverse relaxation time T2 spectrum. The application of this method to nuclear magnetic resonance logs shows that this method not only can improve the accuracy of nuclear magnetic resonance porosity but also can enhance the recognition ability of tight gas sands in nuclear magnetic resonance logs. |
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| Keywords: | Tight gas sands Nuclear magnetic resonance logs Signal‐to‐noise ratio Wavelet‐domain adaptive filtering |
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