Robust K-means algorithm with weighted window for seismic facies analysis

Abstract

Abstract Seismic facies analysis can generate a map to describe the spatial distribution characteristics of reservoirs, and therefore plays a critical role in seismic interpretation. To analyse the characteristics of the horizon of interest, it is usually necessary to extract seismic waveforms along the target horizon using a selected time window. The inaccuracy of horizon interpretation often produces some inconsistent phases and leads to inaccurate classification. Therefore, the developed adaptive phase K-means algorithm proposed a sliding time window to extract seismic waveforms. However, setting the maximum offset of the sliding window is difficult in a real data application. A value that is too large may cause the cross-layer problem, whereas a value that is too small reduces the flexibility of the algorithm. To address this disadvantage, this paper proposes a robust K-means (R-K-means) algorithm with a Gaussian-weighted sliding window for seismic waveform classification. The used weights punish those windows distant from the interpretation horizon in the objective function, consequently producing a smaller range of horizon adjustments even when using relatively large maximum offsets and benefitting the generation of stable and reliable seismic facies maps. The application of real seismic data from the F3 block proves the effectiveness of the proposed algorithm.