Seismic inversion reservoir prediction method based on decompaction acoustic wave velocity

Abstract

The invention discloses a seismic inversion reservoir prediction method based on decompaction acoustic wave velocity. First, original acoustic wave velocity time difference data are arranged and analyzed, time frequency analysis processing is performed on the data, low-frequency components and intermediate- and high-frequency components are saved respectively, then a low-frequency acoustic wave time difference curve is corrected according to a low-frequency long trend fine adjustment method, the corrected low-frequency components and the originally saved high-frequency components are fused to obtain a new acoustic wave time difference curve after decompaction correction, accordingly a crossplot analysis method is utilized to perform reservoir response characteristic analysis on the corrected acoustic wave time difference, the reservoir acoustic wave response characteristic is clear, and is then converted into acoustic wave velocity as a characteristic curve to carry out reservoir multi-attribute neural network seismic inversion treatment, thereby obtaining an acoustic wave velocity invertomer, and finally, a seismic and geological comprehensive assessment means is adopted to carry out explanation and analysis on the acoustic wave velocity invertomer, thereby obtaining a reservoir qualitative and quantitative prediction result. The seismic inversion reservoir prediction method based on decompaction acoustic wave velocity has the advantages of strong operability and wide application range.

Description

【Technical field】 [0001] The invention relates to the field of oil exploration reservoir prediction, in particular to a seismic inversion reservoir prediction method based on decompression sound wave velocity. 【Background technique】 [0002] Reservoir inversion is a key technical means to realize quantitative analysis and evaluation of reservoirs in the process of reservoir prediction. When carrying out reservoir inversion, seismic data and logging acoustic velocity data are two types of basic data that are necessary. Practice shows that the quality of basic data has a decisive impact on the results of reservoir inversion and reservoir prediction. The quality of seismic data mainly includes two important attributes: signal-to-noise ratio and resolution. Among them, the factor of signal-to-noise ratio affects the recovery degree of effective seismic inversion signals, while the resolution determines the identification of vertical thickness and lateral shape of seismic invers...

AI Technical Summary

Problems solved by technology

The commonly used stratum skeleton volume constant decompaction correction method is theoretically correct, but it is unreasonable to regard the porosity of different rock formations at different depths today as the porosity of the same rock formation at different depths in different geological periods

In addition, the porosity-depth function determined by the statistical regression method often cannot objectively reflect the initial porosity and compaction process of rock formations.

Although some researchers propose to divide formation compaction units according to lithology and stratigraphic age, and then determine the initial porosity and compaction model according to the experimental method, there is still a problem that the real pore-depth variation function cannot be obtained.

Images