Prestack Gather Optimization Method Based on Wavelet Packet Decomposition

Abstract

The present invention provides a pre-stack gather optimization method based on wavelet packet decomposition. First, the wavelet packet is used to adaptively decompose the seismic gather, and the decomposition coefficients are divided into low-frequency, intermediate-frequency, and high-frequency parts of the signal, and noise is performed on the high-frequency signal. Detection and removal, event leveling is performed on the low-frequency and intermediate-frequency signals respectively, and finally the seismic gathers are reconstructed for the flattened low-frequency and intermediate-frequency signals; the method of the present invention uses wavelet packets for adaptive decomposition of seismic signals, and this decomposition is redundant. There are no omissions in the rest, the event features are different in each frequency band, and it is more accurate to calculate the event similarity in each frequency band; the method of the present invention defines two types of messages, the degree of attraction and the degree of belonging, to propagate the similarity between gathers, and automatically Selecting reference traces and similar traces does not require manual intervention and has high reliability; the method of the present invention uses the movement amount of the time window center point as the movement amount of the seed point, and the movement amounts of other points are obtained by interpolation of the movement amount of the seed point, thereby avoiding waveform distortion.

Description

technical field [0001] The invention belongs to the field of seismic gathers, in particular to a seismic gather leveling method. Background technique [0002] Fractures and brittleness of shale gas reservoirs are the two most important factors affecting the productivity of shale gas. Good natural fractures and bedding are the preconditions for stimulation and stimulation. The higher the brittleness index, the easier it is to form a fracture network. , which is conducive to the transformation of production increase. Wide-azimuth pre-stack inversion can obtain the required fracture and brittleness attributes, but the gathers required for wide-azimuth pre-stack inversion are flattened. Gather flattening is the pre-order work of inversion, which directly affects the inversion effect , so Gaussian leveling is of great significance. [0003] In addition, the flattened gather can obtain good imaging results, and the study of amplitude variation with offset (AVO) performed before ...

AI Technical Summary

Problems solved by technology

[0013] This method is simple to implement but has two major disadvantages: ① It is necessary to manually select a certain track as the reference track, but manual selection of the reference track is inaccurate

②Only one overall movement can be obtained for each pass. After moving the gathers as a whole, some events will have a better leveling effect, while some events will be worse.

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