Method for improving capability of seismic inverted data prediction storage layer

Abstract

The invention discloses a method for improving the capability of a seismic inverted data prediction storage layer, comprising the steps of: carrying out well wave impedance or speed inversion on seismic data in a work area so as to generate inverted data; determining the length of an initial time window, the sliding step length of the time window, the numerical value amplitude error achieved by curve data according to requirements in the time window, and judgment control parameters of low and high points in the time window; generating a searching time window for the wave impedance or the speed inverted data; generating a self-adaptive time window according to the numerical value amplitude error achieved by the curve data in the time window according to the requirements; computing the statistic of inversion channel data in the self-adaptive time window and the change rate of the numerical value at each sampling point so as to determine relatively low points; generating lower lines corresponding to the inversion channel data by selecting a reasonably and relatively low point in the relatively low points and carrying out interpolation and extrapolation on the reasonably and relatively low point; and carrying out relative difference output on the seismic inverted data via the low lines of the inversion channel data and determining the lithology or the stored optimum relative difference recognition value through adjusting a sectional color code under the contrast of borehole materials.

Description

technical field [0001] The present invention relates to seismic exploration data processing technology, in particular to the technology of using seismic inversion data to predict reservoir capacity, specifically a method for improving the ability of seismic inversion data to predict reservoir. Background technique [0002] Seismic wave impedance or velocity inversion can convert the underground reflection information received by the ground into information reflecting underground rocks, and is an important technical method for people to use post-stack seismic data for reservoir prediction and reservoir physical parameter estimation. In sand and mudstone formations, when the wave impedance of sand and mudstone has clear and uniform differences in the vertical and horizontal directions, a single numerical threshold can be used to identify and predict sandstone reservoirs in wave impedance inversion data, but this Situations of simple dominant differences are rare today. Due to...

AI Technical Summary

Problems solved by technology

However, this method requires known wells that are evenly distributed, and the estimation results are also affected by the model. When the depositional relationship is complex, the workload is heavy, and sufficient hardware support is required

3. Use foreign emerge software to combine the inverted wave impedance and the amplitude, frequency, phase and other attributes extracted from the seismic data through linear or nonlinear algorithms to fit curves that have a clear response to lithology, such as Sp, porosity, etc. Certain relevant conditions need to be met to obtain better results, and the resolution of seismic attributes is limited by seismic data, which will affect the fitting effect

However, the quality requirements of the seismic data used are high, and it is also faced with the influence of factors such as wavelet extraction, well seismic calibration and well constraints of the near, middle and far angle superposition data, and when there is a lack of shear wave logging data. Inversion results have a large impact

To sum up, the above technical methods all have certain applicable conditions, and cannot meet the production requirements of reservoir prediction without restriction.

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