1887

Abstract

Summary

Iterative geostatistical seismic inversion methods rely on stochastic sequential simulation to generate and adjust models, typically using a global variogram to capture spatial continuity patterns. However, in complex, non-stationary geological settings, a single variogram model often fails to effectively represent geological variability, leading to suboptimal inversion outcomes. This study introduces an iterative geostatistical seismic inversion method that incorporates self-updating local variogram models to address this limitation.

The proposed method utilizes automatic clustering and optimizes a cluster validity index (CVI) to dynamically update spatial continuity models throughout the inversion process. It was tested using three different CVIs: Silhouette (SI), Davies-Bouldin (DB), and Calinski-Harabasz (CH). Validation on a synthetic 3D seismic dataset replicating a deep-water turbidite field showed that using SI and CH achieved a high global correlation coefficient (0.9) between predicted and true seismic data, with CH offering the best balance of accuracy and computational efficiency.

This innovative method effectively captures local spatial variability, enhancing the geological realism of inverted models. Its robust performance in a non-stationary example underscores its potential to improve reservoir characterization in real-world applications.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202510225
2025-06-02
2026-02-16

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References

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/content/papers/10.3997/2214-4609.202510225
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Automatic Clustering for Updating Local Variogram Models in Geostatistical Seismic Inversion
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202510225
/content/papers/10.3997/2214-4609.202510225
/content/papers/10.3997/2214-4609.202510225
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