AMD-HHT-based time-frequency domain fluid identification method

Abstract

The invention discloses an AMD-HHT-based time-frequency domain fluid identification method which comprises: performing empirical mode decomposition (EMD); performing Hilbert transform through an IMF component to obtain Hilbert spectrum time frequency; successively extracting the signals of respective frequency components by an AMD method for AMD decomposition; successively subjecting the channel numbers of a seismic signal to loop operation so as to obtain N frequency signals decomposed by AMD. The method solves modal aliasing caused by intermittent signals in the process of EMD, obtains accurate frequency-domain information, solves a problem that HHT cannot recognize non-stationary signals with similar frequency, can detect all characteristic frequencies, accurately extracts low-frequencyinformation, improves the fidelity of seismic signals, and accurately identifies reservoir fluids.

Description

technical field [0001] The invention relates to the technical field of oil and gas exploration, in particular to an AMD-HHT-based time-frequency domain fluid identification method. Background technique [0002] In recent years, with the continuous deepening of oil and gas exploration and development, the form of reservoirs has become more and more complex. The exploration of reservoirs has gradually increased in difficulty from shallow to deep, from large to small, and the focus of exploration is on lithologic oil and gas reservoirs or hidden Oil and gas reservoir transfer. Moreover, in addition to judging whether the reservoir exists, the prediction of the reservoir also needs to judge the type and nature of the fluid contained in the reservoir. Therefore, many geophysicists have noticed that low-frequency seismic anomalies are closely related to oil and gas reservoirs in the search for new methods of reservoir prediction with high precision and low cost. Although the for...

AI Technical Summary

Problems solved by technology

However, in the process of EMD decomposition, the biggest shortcoming and it is unavoidable is the modal aliasing effect, that is, a decomposed IMF component signal contains signals of other IMF components

[0006] After the Hilbert-Huang Transform (HHT) was proposed, although it has made up for the defects of EMD, it cannot effectively identify similar frequency signals whose frequency ratio is less than 1.5, so that the low-frequency information of the reservoir Incomplete decomposition and poor frequency resolution can lead to errors in prediction of reservoir fluids

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