Phase-controlled random inversion thin reservoir prediction method based on seismic frequency extension processing

Abstract

The invention provides a seismic frequency-broadening processing-based phase-controlled random inversion thin reservoir prediction method. The method includes the following steps that: seismic interpretation data and logging information are utilized to carry out fine reservoir calibration, and response characteristics of a reservoir on a seismic profile are clarified; frequency-broadening technology-based fine target processing is carried out on target stratum seismic data, and therefore, the resolution of the target stratum seismic data can be improved, a sensitive discriminant curve which can clearly distinguish the reservoir from surrounding rock is selected according to ground reservoir characteristic analysis; recursive inversion-based constrained sparse pulse inversion is carried out on the seismic data of a research region; reservoir parameter spatial distribution rules of various sedimentary facies are clarified; and based on deterministic inversion data and the reservoir parameter spatial distribution rules of the sedimentary facies, Markov chain Monte Carlo algorithm-based random inversion is carried out. With the seismic frequency-broadening processing-based phase-controlled random inversion thin reservoir prediction method of the invention adopted, an inversion effect can be optimal, and the reliability of the prediction of the thin reservoir is improved.

Description

technical field [0001] The invention relates to the technical field of reservoir prediction in petroleum exploration and development, in particular to a phase-controlled random inversion thin reservoir prediction method based on seismic frequency extension processing. Background technique [0002] With the improvement of oil exploration and development, the potential targets of oil and gas exploration are becoming more and more complex. Finding hidden oil and gas reservoirs and thin interbedded oil and gas reservoirs has become the main goal of oil and gas exploration, which requires continuous improvement of the corresponding exploration technology level. [0003] There are many existing seismic prediction technologies for thin reservoirs, including seismic frequency extension technology, seismic attribute analysis technology, spectral decomposition technology, logging constrained inversion technology, forward modeling technology, etc. Seismic frequency extension technology...

AI Technical Summary

Problems solved by technology

[0004] The main problem of the existing thin reservoir prediction technology is that the identification of thin reservoirs by logging constrained inversion technology and forward modeling technology relies on the addition of high vertical resolution logging data, and does not start with the original seismic data; However, techniques such as seismic frequency extension technology, seismic attribute analysis, and spectral decomposition are used to process or calculate seismic data without the intervention of well logging data.

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