Prestack three-parameter inversion method based on space-variable objective function

Abstract

The invention provides a prestack three-term inversion method based on a space-variant target function. The method comprises the steps of (1) constructing a Bayes object function based on the Bayes theory, (2) using a robustness initial model for prestack three-term inversion to carry out three-parameter space intersect modeling, (3) obtaining a space-variant factor, correcting an observation earthquake data insufficiency to correct a model, (4) using a prestack three-term inversion under initial model constraint obtained by three-factor space intersection constraint modeling, wherein the ojbect function is the space-variant object with the adding of the space-variant factor. According to the prestack three-term inversion method based on a space-variant target function, in the inversion application process, through the method of three-parameter model space intersect constraint, the convergence of the object function is improved, and the method is suitable for an area with a well network nonuniform distribution or object layer transverse quick change.

Description

technical field [0001] The invention relates to the technical field of oilfield development, in particular to a pre-stack three-parameter inversion method based on a space-varying objective function. Background technique [0002] Pre-stack seismic inversion technology has been paid attention to because of its mining of pre-stack seismic information and rich elastic results, and has played an important role in the process of complex and subtle oil and gas exploration and development. However, the seismic data reflect the information of the underground reflection interface, and there are many solutions to the prediction of the distribution and abundance of the remaining underground oil and gas; the logging data can more accurately reflect the information of the underground rock formation at the well point, but the spatial extrapolation is poor . Although the combination of seismic data and logging data can improve the spatial prediction ability of inversion technology, the ex...

AI Technical Summary

Problems solved by technology

However, the seismic data reflect the information of the underground reflection interface, and there are many solutions to the prediction of the distribution and abundance of the remaining underground oil and gas; the logging data can more accurately reflect the information of the underground rock formation at the well point, but the spatial extrapolation is poor

Although the combination of seismic data and logging data can improve the spatial prediction ability of inversion technology, the existing methods do not carry out targeted research on the spatial matching of the two data.

[0003] Due to the limited accuracy of seismic data, there are many solutions for geological bodies with small scale or rapid changes, which will also lead to unsatisfactory inversion results

Although the inversion accuracy has been improved to a certain extent with the introduction of additional information such as well logging data, there are also problems in the actual application process, especially in the pre-stack seismic inversion

On the one hand, the distribution of well locations in the work area is uneven, and the accuracy of the initial model varies greatly in space; on the other hand, due to the lack of shear wave logging data, the initial model based on the estimated shear wave is likely to cause convergence in the inversion process question

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