Lithologic Reservoirs ›› 2025, Vol. 37 ›› Issue (6): 99-106.doi: 10.12108/yxyqc.20250609

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

A fully automatic velocity picking method based on weighted K-means clustering

LIU Zhengwen1,2,3,4, ZHAO Ruirui1,2,3,4, CHEN Yangyang1,2,3,4, XIE Junfa5, ZANG Shengtao5, QIN Long1,2,3,4   

  1. 1. R&D Center for Ultra-Deep Complex Reservoir Exploration and Development, CNPC, Korla 841000, Xinjiang, China;
    2. Engineering Research Center for Ultra-Deep Complex Reservoir Exploration and Development, Xinjiang Uygur Autonomous Region, Korla 841000, Xinjiang, China;
    3. Xinjiang Key Laboratory of Ultra-Deep Oil and Gas, Korla 841000, Xinjiang, China;
    4. Research Institute of Exploration and Development, PetroChina Tarim Oilfield Company, Korla 841000, Xinjiang, China;
    5. PetroChina Research Institute of Petroleum Exploration & Development-Northwest, Lanzhou 730020, China
  • Received:2025-05-29 Revised:2025-06-30 Online:2025-11-01 Published:2025-11-07

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Abstract: To improve the efficiency of seismic velocity picking, an improved weighted K-means clustering method was proposed based on the conventional K-means clustering algorithm. Both of the improved method and the conventional K-means clustering were applied to model data and actual data of TD block in Tarim Basin, and the picking results were analyzed. The results show that: (1) The weighted K-means clustering method eliminates weak amplitude velocity points by setting velocity spectrum amplitude threshold, then set the proportional coefficient to eliminate the outlier velocity points. Set a time window in the time direction and search for several velocity points with the largest amplitudes within the time window. The average velocity and average time of the found velocity points are taken as the initial values of the clustering centers. Based on the initial values, remove or merge some clustering centers, assign different weights to different velocity points, remove the velocity points at the edge of each cluster center, make the automatically picked cluster centers approach the energy group center. For picking results with velocity reversal, the first two picking results are used to make multiple wave judgments and eliminations. (2) The weighted K-means clustering method does not require prior velocity information, and achieves fully automatic velocity picking. In each iteration, some velocity points are eliminated, significantly reducing the number of required iterations. It not only speeds up the calculation but also enhances the picking accuracy. The weighted K-means clustering method was applied to automatically velocity picking on the model data and the actual data of TD block in Tarim Basin, compared with the conventional K-means clustering method, its computational efficiency was increased by approximately 7 times and the accuracy was improved by 1.7%.

Key words: cluster center, weighted K-means clustering, velocity picking, fully automatic, weighted, unsupervised, time domain, velocity modeling

CLC Number: 

  • TE319
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