Gas-bearing prediction method and device based on attenuation factor change along with incident angle

Abstract

The invention discloses a gas-bearing prediction method and device based on attenuation factor change along with an incident angle. The method comprises the following steps: collecting a pre-stack angle domain gather, and determining an incident angle range of the pre-stack angle domain gather; superposing the pre-stack angle domain gathers according to a predetermined incident angle range to obtain a plurality of superposed seismic data; extracting a longitudinal wave attenuation factor of each superimposed seismic data; rearranging the extracted longitudinal wave attenuation factors according to the incident angle to obtain pre-stack longitudinal wave attenuation factors; and extracting the change characteristics of the pre-stack longitudinal wave attenuation factor at the target layer along with the incident angle to predict the gas-bearing property of the well. According to the application, the seismic attributes are calculated on the basis of the post-stack seismic data, so that the stability is high, and the method does not depend on logging data, so that the method is high in determinacy. Therefore, the method has great application and popularization value in the gas-bearing area.

Description

technical field [0001] The invention belongs to the field of petroleum exploration, and more specifically relates to a method for predicting gas content based on the variation of the attenuation factor with the incident angle and a gas content prediction device based on the variation of the attenuation factor with the incident angle. Background technique [0002] Because pre-stack seismic data contains more information than post-stack seismic data, so most of the current researches on gas-bearing detection are based on pre-stack seismic data. At present, there are two main ideas when using pre-stack seismic data for gas-bearing detection research: [0003] 1. Pre-stack inversion, the main problems are the limitation of the low-frequency model and the multiple solutions of the inversion method itself; [0004] 2. AVO attribute. The main problem is that the conventional AVO attribute is less sensitive to gas content. [0005] The above two existing gas content prediction sch...

AI Technical Summary

Problems solved by technology

[0003] 1. Pre-stack inversion, the main problems are the limitation of the low-frequency model and the multiple solutions of the inversion method itself;

[0004] 2. AVO attribute, the main problem is that the conventional AVO attribute is less sensitive to gas content

[0005] The above two existing gas content prediction schemes are unsatisfactory

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