Method for eliminating thin layer tuning effect based on prestack gather

Abstract

The invention discloses a method for eliminating thin layer tuning effect based on prestack gather. The method comprises the following steps: preprocessing data about prestack seismic gather of a target work area so as to correct an event and / or improve the signal-to-noise ratio of prestack seismic data; carrying out wavelet scale decomposition on the frequency spectrum of the preprocessed prestack seismic data so as to obtain a plurality of wavelets of different scales and analyzing and determining to-be-suppressed wavelet scales; reconstructing the wavelets of different scales by constructing scale bound terms so as to weaken information of the to-be-suppressed wavelet scales; and forming detuned seismic gather through of the reconstructed wavelets and the prestack seismic data. According to the invention, the targeted method for eliminating thin layer tuning effect based on wavelet scale decomposition is fundamentally brought forward by researching the generation mechanism of the tuning effect of prestack seismic gather; and the accuracy of earthquake prediction on a thin reservoir (or thin hydrocarbon reservoir) is improved by suppressing large-scale information of the seismic data to recover actual AVO characteristics caused by physical property parameters of the thin reservoir.

Description

technical field [0001] The invention relates to the technical field of oil and gas geophysics, in particular to a thin layer tuning method based on pre-stack gathers. Background technique [0002] Lithologically subtle oil and gas reservoirs have become one of the key targets of oil and gas exploration in my country at the present stage, and how to effectively use seismic data to predict thin reservoirs has become a top priority. [0003] Aiming at the prediction of thin sand layers, many scholars have carried out some new research attempts in recent years. In terms of post-stack earthquake prediction, Xu Liying et al. (2006, Petroleum Geophysical Exploration Phase 3, Prediction of Thin Reservoir Using Spectral Decomposition Technology), Yang Guixiang et al. High-resolution inversion technology), Castagna et al. (2003, TheLeadingEdge No. 2, Detection of flow-frequency shadow associated with hydrocarbon), Liu Wei et al. (2012, Scientific Observation No. 5, use spectral decom...

AI Technical Summary

Problems solved by technology

However, in the middle and late stages of exploration and development of lithologic oil and gas fields, the target reservoirs are usually thin and deeply buried.

When the main frequency of seismic data is low, it will cause a certain tuning effect, resulting in the AVO characteristics of thin reservoirs not obvious, which will bring difficulties to the prediction of reservoirs and oil and gas.

[0006] Since the existing technology is still based on the Fourier transform technology in the frequency division prediction of thin reservoirs, it is difficult to guarantee the prediction accuracy

In addition, the frequency division techniques developed for the thin-bed tuning effect are mostly applied to post-stack seismic data. In the analysis of the tuning effect of a few pre-stack gathers, there is no systematic discussion on the mechanism of the thin-bed tuning effect, and only the conventional frequency division technology is used experimentally. Carried out the primary analysis of thin-layer tuning effect, but did not clearly propose a targeted solution to remove the tuning effect of pre-stack gathers

[0007] Therefore, in the prior art, there is no method and means to effectively remove the tuning effect to accurately predict the characteristics of thin reservoirs for thin reservoir prediction

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