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Proceedings Paper

Digital surface reconstruction and oil-gas trap prediction
Author(s): Li Chen; Osei Adjei; Yayun Lu
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Paper Abstract

Automatic oil-gas trap prediction is usually based on 2D vertical section data or horizontal slice data. 3D seismic prospecting provides the possibility of using interfaces or digital surfaces to locate parameters that may be used to accomplish oil-gas trap prediction. However, to construct such digital surfaces usually requires computerized interaction between the software system and the interpreter. This paper describes an automatic digital surface reconstruction technique that is based on (lambda) -connected segmentation method, which generates a sequence of digital surfaces to represent the interfaces of seismic layers. The technique uses a surface fitting algorithm that is based on the gradually varied function. This algorithm can also be applied to irregular domains. Reconstructed surfaces are used as the reference surfaces to describe the actual locations of data (parameters) on the interfaces. A modified fuzzy evaluation technique is developed for the oil-gas trap prediction. After the evaluation of every point on all surfaces, a volume that indicates the possibility of oil-gas is built. This makes it possible to use (lambda) -connected searching to extract oil- gas components (traps) in the 3D image. Hence, it allows us to evaluate the size or volume of each trap. This paper also discusses the technique as used in a real oil-gas prediction problem to demonstrate the effectiveness of the concept in 3D seismic image processing.

Paper Details

Date Published: 23 October 2000
PDF: 10 pages
Proc. SPIE 4117, Vision Geometry IX, (23 October 2000); doi: 10.1117/12.404818
Show Author Affiliations
Li Chen, Univ. of Luton (UK), Wuhan Univ. (China), and Scientific and Practical Computing (United States)
Osei Adjei, Univ. of Luton (United Kingdom)
Yayun Lu, Research Institute of Geophysics (United States)


Published in SPIE Proceedings Vol. 4117:
Vision Geometry IX
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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