In-well micro-seismic noise elimination method based on experience wavelet transformation and various threshold functions

An empirical wavelet, microseismic technology, applied in seismology, seismic signal processing, geophysical survey, etc., can solve problems such as poor performance

Inactive Publication Date: 2018-06-01
JILIN UNIV
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Problems solved by technology

However, these methods perform poorly in weak reflection events, especially when the SNR is below a given threshold

Method used

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  • In-well micro-seismic noise elimination method based on experience wavelet transformation and various threshold functions
  • In-well micro-seismic noise elimination method based on experience wavelet transformation and various threshold functions
  • In-well micro-seismic noise elimination method based on experience wavelet transformation and various threshold functions

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Embodiment Construction

[0070] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0071] Such as figure 1 As shown, the microseismic denoising method in the well based on empirical wavelet transform and multi-threshold function provided by the present invention includes:

[0072] Step 1, calculate the fast Fourier transform of the measured seismic wave signal f(t), obtain the frequency spectrum F(ω) of the measured signal;

[0073] Step 2. Separate the spectrum appropriately, and set the boundary as Ω={ω K} k＝1,2,···K , assuming that the spectrum is divided into K consecutive parts in [0,π]:

[0074] The spectral segmentation method is an important step, which provides a theoretical support for the EWT adaptive analysis signal. There are four spectrum segmentation methods in EWT, namely 'locmax', 'localmaxmin', localmaxminf' and 'adaptive'. The adapti...

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Abstract

The invention discloses noise inhibition which is an important processing step in a micro-seismic signal processing process. Complete ensemble empirical mode decomposition CEEMD and wavelet transformation WT are widely applied to seismic noise elimination; however, the CEEMD is lack of theoretical foundations and the self-adaptability of the WT is relatively weak. Therefore, the noise eliminationeffect is poor. According to the invention, experience wavelet transformation (EWT) is combined with various threshold functions to carry out micro-seismic noise elimination for the first time. The EWT is used for establishing a self-adaptive wavelet filter group through spectrum segmentation to extract different frequency blocks of a detected signal. In the EWT, four types of spectrum segmentation methods are adopted; an experiment finds out that an adaptive algorithm can be used for separating an effective signal and noises of micro-seismic data very well; after the EWT is carried out, the signal can be divided into two components through analyzing a spectrum and energy of each module. A hard threshold function is applied to the component containing more effective signals and an improvedthreshold function is applied to the component containing less effective signals. An extraction method is compared with the CEEMD and the WT in an analogue signal and an actual signal to prove the effectiveness of a provided method.

Description

technical field [0001] The invention relates to a seismic signal processing method in seismic exploration, in particular to a micro-seismic denoising method in wells based on empirical wavelet transform and multi-threshold functions. Background technique [0002] Microseismic monitoring is an important means to improve the natural permeability of unconventional oil and gas reservoirs. Microseismic waves are generated during hydraulic fracturing and have the characteristics of small amplitude, high frequency, and low signal-to-noise ratio. From the nature of the microseismic signal, it is difficult to process the microseismic signal, which cannot be directly applied to locate oil and gas reservoirs. It is necessary to denoise the microseismic signal first. Monitoring applications for hydraulic fracturing and mining-induced microseismic events have increased rapidly over the past 30 years. So how to effectively suppress the noise and restore the effective signal is very impo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/36G01V1/30
CPCG01V1/30G01V1/36G01V1/364
Inventor 李娟李元钱志鸿乔乔叶心左英泽
Owner JILIN UNIV
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