Reservoir non-isotropy detection method and equipment based on gradient structure tensor

Abstract

The invention provides a reservoir non-isotropy detection method and equipment based on a gradient structure tensor. The method comprises the steps that seismic data are collected; the seismic data are processed conventionally, and post-stack seismic data are obtained; the gradient structure tensor is built according to the post-stack seismic data; a confidence coefficient is built according to the gradient structure tensor; reservoir predication is carried out according to the confidence coefficient. The reservoir non-isotropy detection method and equipment based on the gradient structure tensor use the analysis technology of the gradient structure tensor, builds the confidence coefficient according to the partial geometrical characteristics of seismic data, and describes the discontinuous boundary of an image, thereby achieving the purposes of identifying faults, fissures, karst caves, watercourses and other target geologic bodies.

Description

technical field [0001] The invention relates to the technical field of oil and gas exploration, in particular to the detection technology of post-stack seismic data, specifically a method and equipment for reservoir heterogeneity detection based on gradient structure tensor. Background technique [0002] With the continuous development of seismic data acquisition technology, high-quality seismic data with high density, large offset, and wide azimuth has greatly improved the resolution and accuracy of exploration results, enabling us to identify underground More subtle differences caused by residual oil. Therefore, on the basis of accurate structural imaging, new technical means are needed to describe the heterogeneity of the subsurface medium in more detail, so as to obtain a more realistic geological and geophysical understanding, and assist the research work in the exploration stage of lithologic oil and gas reservoirs. [0003] Faults, fractures, caves, channels, sand bo...

AI Technical Summary

Problems solved by technology

The anti-noise performance of coherence and curvature attributes is not good, and they are susceptible to noise pollution. Therefore, there are often situations where the difference between faults / fractures and the surrounding background is too low. / The depiction of fractures is often not clear enough, and even affects the interpreter's judgment on fault interpretation

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