1887
Volume 50, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

ABSTRACT

Multi-attribute analysis of multi-component seismic data can provide abundant information on seismic hydrocarbon reservoirs. It can exploit the different responses of compressional and shear waves to the reservoir, in conjunction with colour blending and fusion technology applied to seismic images, to enable the human eye to better discriminate features in seismic data, thereby improving the accuracy of reservoir characterisation. The integration of multi-component data and sophisticated visualisation techniques helps to reduce the number of possible models of the reservoir. Thus, we designed a reservoir prediction method based on unsupervised learning and colour feature blending. First, a large number of compressional and shear wave seismic attributes were extracted using cluster analysis to conduct unsupervised learning to optimise the attributes. Then, using the different responses of compressional and shear waves to oil and gas, and an understanding of rock physics, three types of composite attribute were constructed to highlight oil and gas anomalies by multi-component seismic attributes. Finally, the three composite attributes were transformed to the colour space by a first-order linear transformation and RGB colour blending. Applying this scheme to reservoir prediction shows that unsupervised learning and colour blending techniques could help the human eye perceive geological anomalies, highlight common hydrocarbon characteristics, reduce differences and decrease interpretation ambiguity. The prediction results are essentially consistent with actual data and can be used to predict favourable exploration areas.

© 2019 Australian Society of Exploration Geophysics
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2019-05-04
2026-01-16

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Reservoir characterisation method with multi-component seismic data by unsupervised learning and colour feature blending
Exploration Geophysics 50, 269 (2019); https://doi.org/10.1080/08123985.2019.1603078
/content/journals/10.1080/08123985.2019.1603078
/content/journals/10.1080/08123985.2019.1603078
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  • Article Type: Research Article
Keyword(s): cluster analysis; colour blending; multi-component composite attributes; seismic reservoir prediction; Unsupervised learning

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