Prestack channel set optimization method based on wavelet packet decomposition

Abstract

The invention provides a prestack channel set optimization method based on wavelet packet decomposition. The method comprises the steps that firstly self-adaptive decomposition is carried out on earthquake channel sets through wavelet packets, a decomposition coefficient is divided into low-frequency signals, medium-frequency signals and high-frequency signals, noise detection and removal are carried out on the high-frequency signals, event alignment is respectively carried out on the low-frequency signals and the medium-frequency signals, and finally earthquake channel set construction is carried out on the low-frequency signals and the medium-frequency signals obtained after alignment. According to the method, self-adaptive decomposition is carried out on earthquake signals through the wavelet packets, the decomposition is free of redundancy and free of omissions, the event characteristics are different in all frequency bands, and event similarity calculation is carried out on each frequency band more accurately. According to the method, two types of messages of the attraction degree and the attribute degree are defined to carry out similarity spreading between the channel sets, reference channels and similar channels are automatically selected, manual intervention is not needed, and high reliability is achieved. According to the method, the movement amount of time window center points is used as the movement amount of seed points, the movement amount of other points is obtained through the interpolation of the movement amount of the seed points, and wave form distortion is avoided.

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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