Angular part superposition seismic data-based fluid identification method

Abstract

The invention provides an angular part superposition seismic data-based fluid identification method, which comprises the following three steps of: 1) rapidly estimating an impedance reflection coefficient, namely starting from Shuey approximation (Shuey, 1985) in a Zoeppritz equation, deducing a formula for accurately and rapidly estimating the impedance reflection coefficient of longitudinal and shear waves, and calculating a seismic attribute used for fluid identification; 2) extracting a new fluid identification attribute based on the angle gather part superposition seismic data, namely starting from a fluid factor formula provided in Smith and Gidlow (Smith, 2003), and obtaining the combination of the angle gather part superposition seismic data for fluid identification and reservoir prediction; and 3) performing a curvelet transform and Bayesian theory-based fluid identification, namely equalizing each angle superposition gather data by using a wavelet equalization method on the basis of the steps 1) and 2) so as to remove difference of different angles and keep the difference of different angle gather data caused by the fluid, obtaining abnormality at a target layer by using Bayesian wave field separation technology in a curvelet domain, naming the abnormality as angle fluid gather, and solving various attributes to obtain the abnormality at the target layer, wherein the attribute obtained by employing various combinations of the angle gather part superposition seismic data can better distinguish gas / oil-containing and water-containing reservoirs, particularly the attribute section obtained from the third power of the angle gather can better distinguish the gas / oil-containing and water-containing reservoirs.

Description

Technical field [0001] The invention belongs to the field of seismic data processing, and relates to a technology for identifying fluids by using seismic data. Background technique [0002] With the increasing difficulty of exploration, the accuracy of reservoir prediction is required to be higher and higher, and more effective methods and technologies for reservoir prediction are needed. Conventional seismic attribute analysis is based on post-stack seismic data. After multiple stacking of post-stack seismic data, the signal-to-noise ratio has been greatly improved, but a large amount of amplitude information has been lost at the same time. Compared with post-stack seismic data, pre-stack seismic data contains more abundant amplitude and travel time information, which can more sensitively reflect the changes of underground oil and gas reservoirs, but it is greatly affected by noise and the signal-to-noise ratio is not high. The advantages of the two kinds of data are compr...

AI Technical Summary

Problems solved by technology

Conventional seismic attribute analysis is based on post-stack seismic data. After multiple stacking of post-stack seismic data, the signal-to-noise ratio has been greatly improved, but at the same time a large amount of amplitude information has been lost.

Compared with post-stack seismic data, pre-stack seismic data contains more abundant amplitude and travel time information, which can more sensitively reflect the changes of underground oil and gas reservoirs, but it is greatly affected by noise, and the signal-to-noise ratio is not high

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