Characterization of volcano structure and identification of lithofacies based on 5D seismic data:a case study on Carboniferous volcanic rocks in Junggar Basin
Five-dimensional(5D)seismic data can better analyze the changes in attributes such as travel time,speed,amplitude,frequency,and phase of seismic waves propagating in anisotropic media with azimuth angle.Moreover,the offset information is related to the scale,stratigraphic lithology,and fluid composition of the tar-get geological body,while the azimuth angle information is related to the development characteristics of strati-graphic faults and fractures.Therefore,this paper proposes a technique for volcanic structure characterization and lithofacies identification based on 5D seismic data.By considering that the underground structure responds more obviously in the direction perpendicular to the strike direction,the paper constructs an azimuth analysis window to extract the dominant azimuth information and uses dip imaging to enhance the processed seismic data,so as to predict the volcanic structure.By using changes in dip and azimuth angles,the paper calculates the similarity of adjacent channels,improves the lateral signal-to-noise ratio of seismic data,and clarifies the macroscopic distribution range of volcanic structures.By defining the azimuth time window and combining the seismic trace inverse distance weighting algorithm,the paper extracts the most sensitive information about faults at each azimuth angle,improves the accuracy of volcanic structure characterization,and obtains a clearer volcanic morphology.Combined with kernel principal component analysis(KPCA),the dominant attributes are fused to predict the favorable lithofacies of volcanic rocks.The proposed method accurately predicts the fa-vorable zones of the third phase of volcanic rocks in the KM1 well area of the Junggar Basin,laying the founda-tion for the exploration and development of volcanic rock reservoirs in this area.
volcanic rockJunggar Basin5D seismic datacharacterization of volcanic structuredip imaging en-hancementkernel principal component analysis
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