Method for removing thin layer tuning effect and method for identifying and predicting thin reservoir and gas layer

Abstract

The invention discloses a method for removing the tuning effect of thin layers, and a method for identifying and predicting thin reservoirs and gas layers. The method for removing the thin layer tuning effect includes the following steps: preprocessing the pre-stack seismic data of the target work area to correct the event and / or improve the signal-to-noise ratio of the pre-stack seismic data; Decompose the wavelet scale of the data spectrum to obtain several wavelets with different scales, analyze and determine the wavelet scale to be suppressed; reconstruct the wavelets of different scales by constructing scale constraint items to weaken the wavelet scale information to be suppressed ; Detuned seismic gathers are formed using the reconstructed wavelet and prestack seismic data convolution. The present invention proposes a targeted method based on wavelet scale decomposition to remove the thin layer tuning effect. By suppressing the large-scale information of seismic data, the real AVO characteristics caused by the physical parameters of thin reservoirs are restored, so as to improve the seismic performance of thin reservoirs (or thin oil and gas layers). prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of oil and gas geophysics, in particular to a method for thin-layer tuning based on pre-stack gather removal, and a method for identifying and predicting thin reservoirs and gas layers based on thin-layer tuning based on pre-stack gather collection Methods. 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 ...

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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