Document Type : Original Research Paper

Authors

• Shaghayegh Esmaeilzadeh ¹
• Ali Moradzadeh ²
• omid Asghari ¹
• Reza Mohebian ¹

¹ North kargar St.

² School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.

Keywords

• Stochastic inversion
• Local spatial continuity models
• Direct sequential simulation
• Self-learning
• Cluster validity index

Main Subjects

• Hydrocarbon Resources