Point constraint Bayes sparse pulse inversion method

Abstract

The invention provides a point constraint Bayes sparse pulse inversion method. The point constraint Bayes sparse pulse inversion method comprises steps that: step 1, seismic wavelets are extracted; step2, an initial geology model is established; step 3, an inversion target function is established; step 4, derivation of each item of the inversion target function is carried out to acquire a non-linear inversion equation; step 5, inversion parameters are set; step 6, the non-linear inversion equation is solved through an iterative method; and step 7, a reservoir high-resolution inversion result is outputted. The point constraint Bayes sparse pulse inversion method integrates the random inversion with the traditional sparse pulse inversion, improves the inversion result resolution and further improves accuracy and stability of the inversion result, and thereby more abundant information is provided for reservoir description.

Description

technical field [0001] The invention relates to seismic inversion technology, in particular to a point-constrained Bayesian sparse pulse inversion method. Background technique [0002] In major oilfields at home and abroad, seismic inversion technology has become a conventional technology for reservoir description and prediction, and its application fields range from continental faulted lake basins to marine strata, from clastic rock reservoirs to carbonatite and igneous rock reservoirs, From the shallow layer to the middle-deep layer, a large number of research results come out every year. However, there are still some problems in the application of inversion data at present. The reasons are: the inversion resolution is not enough, the inversion results are often similar to seismic sections, and cannot be adapted to areas with complex structural and lithological changes. The ability to solve problems is limited; the inversion relies heavily on the prior geological model, h...

AI Technical Summary

Problems solved by technology

At present, there are many new inversion methods appearing every year at home and abroad, but in the current exploration practice, there are still some problems in the existing inversion techniques and methods, which restrict the improvement of the success rate of inversion application

The currently commonly used sparse pulse inversion has high accuracy and stability, but the inversion resolution is not high, and it cannot adapt to areas with complex structural and lithological changes, and has limited ability to solve practical geological problems.

Although the high resolution of stochastic inversion is high, it requires more well data, and the reliability of the regional inversion away from the well is significantly reduced

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