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Proceedings Paper

Intelligent data fitting technique for 3D velocity reconstruction
Author(s): Li Chen; Donald H. Cooley; Lan Zhang
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Paper Abstract

Intelligent data fitting is often used in seismic data processing to reconstruct large three-dimensional data volumes based on relatively small amounts of 2D seismic and/or well- log data. This technique is useful for such applications because of the many different layers and lithologies in stratum. However, if such data fitting is performed over two different layers in the stratum, it often results in incorrect profiles. A more accurate way to perform such fitting is to first perform segmentation of the data into known classes, and then fit the data inside of each segmented class. In seismic data processing, interval velocity is one of the most important factors in rock type identification, i.e. lithology classification. To find a region with low velocity generally indicates a lithology of high porosity. Such a region is much more likely to contain oil and/or gas. Velocity data is more difficult to obtain than seismic waveform, which is obtained by 3D seismic prospecting. To obtain a vertical trace of interval velocity data requires extensive processing. Generally, such a velocity volume is obtained by means of a data fitting technique. This paper presents a general intelligent data fitting approach to reconstruct interval velocity volumes. This technique uses a process we term SEGFIT-segmentation followed by fitting. In SEGFIT, we use a fuzzy connected segmentation technique for the segmentation, which we term (lambda) -connected followed by a specialized fitting method which maintains the (lambda) -connectedness of the fitted data in each segmented class. In this paper we show that a real velocity volume can be obtained using this technique.

Paper Details

Date Published: 25 March 1998
PDF: 10 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304794
Show Author Affiliations
Li Chen, Utah State Univ. (United States)
Donald H. Cooley, Utah State Univ. (United States)
Lan Zhang, Utah State Univ. (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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