ABSTRACT

Seismic waveform classification is an important technique for seismic facies analysis. The most existing methods use a fixed time window to extract seismic data along the interpretation horizon and then classify them. However, when the thickness of target layer has a large change at different points, an appropriate time window size is difficult to choose. Instead, by using both the top and bottom horizons to extract seismic waveforms, we propose a new similarity measure called dynamic sub-windows matching (DSWM) for calculating directly the distance between two waveforms with different lengths in this paper. DSWM is the average distance of multiple short signals and uses dynamic programming to find the best matching sub-windows. Furthermore, the KNN (K-Nearest Neighbors)-DSWM method is developed for supervised seismic facies analysis. The comparison and analysis of the results from the synthetic and real field data demonstrate that the proposed approach is suitable for reservoir with varying thickness, and provides an effective seismic interpretation tool.

Presentation Date: Monday, September 16, 2019

Session Start Time: 1:50 PM

Presentation Time: 2:15 PM

Location: Poster Station 2

Presentation Type: Poster

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