Seismic fluid prediction method based on VMD and TK energy operators

Abstract

The invention discloses a seismic fluid prediction method based on VMD and TK energy operators. The method comprises the following steps: S1, performing variational mode decomposition on a seismic signal to obtain a set of modal components that reflect a natural mode of vibration inside the seismic signal and have an instantaneous frequency with real physical meaning; S2, getting TK energy spectrums of each modal component by using the TK energy operators to obtain more accurate energy distribution on a signal time frequency; and S3, performing effective and accurate seismic fluid prediction by searching fluid characteristics of ''enhanced low frequency energy and attenuated high frequency energy'' of oil and gas contained in the reservoir. The seismic fluid prediction method based on VMDand TK energy operators combines the variational mode decomposition with the TK energy operators, which has stronger identification for the fluid-containing characteristics of the reservoir, and can effectively improve the accuracy of seismic fluid prediction.

Description

technical field [0001] The invention relates to the identification and prediction of seismic fluid, in particular to a seismic fluid prediction method based on VMD and TK energy operators. Background technique [0002] Seismic exploration plays a vital role in oil and gas prediction, and is an effective means to search for oil and natural gas. During the propagation of seismic signals, due to the heterogeneity of the underground stratum, the seismic wavelets will continue to change, coupled with the influence of noise in the acquisition process, the actual received seismic signals are not stable in time or space. It exhibits typical nonlinear and non-stationary characteristics. Previous studies have shown that when seismic waves propagate in fluid-bearing formations, their characteristics vary with the type and state of the fluid. When the fluid type is oil and gas, the seismic signal will have the characteristics of "increased low-frequency energy and weakened high-freque...

AI Technical Summary

Problems solved by technology

On the basis of IMF, the Hilbert marginal spectrum of each IMF is obtained by using the Hilbert spectrum analysis method. The Hilbert marginal spectrum is a statistically significant spectrum, which reflects the contribution of each frequency component to the overall total energy of the signal. The contribution of the energy can reflect the distribution and transformation of energy in the high frequency, intermediate frequency and low frequency subbands to a certain extent, and it is an effective means to analyze the energy distribution characteristics of different frequency components in the frequency domain. However, Hill Burt-Huang transform suffers from endpoint effects and mode aliasing

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