Rock reservoir structure characterization method and device, computer readable storage medium and electronic equipment

Abstract

The invention discloses a rock reservoir structure characterization method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: obtaininga three-dimensional seismic data volume of a to-be-characterized rock reservoir, and carrying out the decomposition of the three-dimensional seismic data volume; transforming the plurality of intrinsic mode function parts obtained by decomposition to obtain time-frequency spectrums of the intrinsic mode function parts, and adding the time-frequency spectrums of all the parts to obtain a time-frequency spectrum of the seismic data; sensitive parts with the highest relevancy are screened out through cross-correlation of time-frequency parts of all time-frequency spectrums of well-side seismic traces and logging data synthesis seismic traces to serve as input characteristics, fuzzy C-means clustering and spatial smoothing are conducted on the input characteristics, and seismic facies dividedaccording to set standards are obtained; and depicting the to-be-characterized rock reservoir to obtain structural characterization of the to-be-characterized rock reservoir. The apparatus, medium andapparatus can be used to implement the method. The method can solve the problem that the effectiveness, clustering reliability and noise immunity of input features cannot be guaranteed when reservoirs are depicted by means of an existing data-driven machine learning method.

Description

technical field [0001] The invention relates to a rock reservoir structure characterization method, in particular to a rock reservoir structure characterization method, device, computer-readable storage medium and electronic equipment. Background technique [0002] In the prior art, there is a rock reservoir structure characterization method through empirical mode decomposition and KNN clustering algorithm, which has the following technical defects: different seismic traces have different numbers of IMF components, and the corresponding seismic sections of each IMF component No lateral continuity; no quantitative constraints of actual logging response; each data in the KNN clustering method only belongs to one sedimentary facies, without its degree of membership to each sedimentary facies, which is not conducive to further smooth analysis of clustering results . In the prior art, there is also a rock reservoir structure characterization method through SST and K-Means cluste...

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