A DTW distance-based seismic waveform clustering method for layers of varying thickness*
Hong Zhong 1,2, Li Kun-Hong 1, Su Ming-Jun 2, Hu Guang-Min?1, Yang Jun 3, Gao Gai 4, and Hao Bin 2
1. University of Electronic Science and Technology of China, The Center for Information Geoscience, Chengdu 611731,China.
2. PetroChina Research Institute of Exploration and Development (RIPED)-Northwest, Lanzhou 730020, China.
3. PetroChina Yumen Oilfi eld Company, Research Institute of Exploration and Development, Jiuquan 735000, China.
4. PetroChina Changqing Oilfi eld Company, Research Institute of Exploration and Development, Xi’an 710018, China.
Abstract：
Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization. The current seismic waveform clustering algorithms are predominantly based on a fixed time window, which is applicable for layers of stable thickness. When a layer exhibits variable thickness in the seismic response, a fixed time window cannot provide comprehensive geologic information for the target interval. Therefore, we propose a novel approach for a waveform clustering workflow based on a variable time window to enable broader applications. The dynamic time warping (DTW) distance is fi rstintroduced to effectively measure the similarities between seismic waveforms with various lengths. We develop a DTW distance-based clustering algorithm to extract centroids, and we then determine the class of all seismic traces according to the DTW distances from centroids. To greatly reduce the computational complexity in seismic data application, we propose a superpixel-based seismic data thinning approach. We further propose an integrated workfl owthat can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms. We evaluated the performance by applying the proposed workflow to synthetic seismograms and seismic survey data. Compared with the the traditional waveform clustering method, the synthetic seismogram results demonstrate the enhanced capability of the proposed workflow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness. Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workflow correlates well with the geological characteristics of wells in terms of reservoir thickness.
2020, Vol. 17 
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