Pre-stack seismic non-linear three-parameter association inversion method

Abstract

The invention discloses a pre-stack seismic non-linear three-parameter association inversion method comprising the steps of: adopting ground seismic data to construct an objective function based on a second order Taylor series expansion equation; respectively constructing the first order local derviation and the second order local derviation of the objective function into banded symmetrical matrices; utilizing a wavelet transformation method to search the smallest point in the overall situation so as to obtain a multi-scale solution of the objective function in a wavelet domain; inversing the multi-scale solution to a time-space domain for reconstruction to obtain a final solution of the objective function; and utilizing the final solution for conducting inversion to obtain a stratigraphic profile model finally, the method realizes non-linear wavelet multi-scale solving and forced determining algorithm, improves reservoir resolution ability of inversion result and effectively resolves the problem of underdetermined equation; and a plurality of angle stacked profiles are subjected to inversion simultaneously, thus greatly reducing time for operation, improving operating efficiency, being capable of using a plurality of parts of angles for superposing the seismic data so as to generate various elastic parameter results such as the velocity and density of compressional wave and shear wave and the like, through inversion.

Description

technical field [0001] The invention relates to the field of exploration geophysics, in particular to a nonlinear inversion method of pre-stack seismic elastic parameters. Background technique [0002] Pre-stack seismic inversion can provide a variety of effective formation parameter data volume results, and is an important technique for describing complex oil and gas reservoirs. At present, most of the commonly used pre-stack inversions at home and abroad are based on the first-order approximation of the objective function based on the Taylor series expansion. Reservoir resolution capability. The second-order Taylor approximation has obvious advantages in calculation accuracy compared with the first-order approximation, but the second-order approximation only holds when the initial model is in the global minimum neighborhood of the objective function (near the real model), and the approximate linearization relationship holds. The result of the exercise can be close to the...

AI Technical Summary

Problems solved by technology

At present, most of the commonly used pre-stack inversions at home and abroad are based on the first-order approximation of the objective function based on the Taylor series expansion. Reservoir resolution

The second-order Taylor approximation has obvious advantages in calculation accuracy compared with the first-order approximation, but the second-order approximation only holds when the initial model is in the global minimum neighborhood of the objective function (near the real model), and the approximate linearization relationship holds. The result of the exercise can be close to the global optimal solution, otherwise it is easy to fall into the local minimum, which greatly limits the practical application of the second-order approximation method

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