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Abstract

Summary

Interpreting seismic data in 3D can be challenging, especially when the target geometries are as complex as meandering river systems. The proposed integration of Machine Learning (ML) methods with frequency-decomposed seismic data enables the extraction of amplitude-frequency relationships, automatic clustering, and subsequent classification controlled by the interpreter. As a result, this approach aids in identifying and delineating subsurface channel environments, regardless of their geometrical complexity. The solution offers a fresh perspective on 3D seismic interpretation and geobody extraction, with reduced human bias and errors in interpretation. Furthermore, it provides a foundation for further petrophysical analysis, and gross and net volumes calculation, ultimately enabling more precise resource estimation, improved reservoir characterization, and optimized field development strategies.

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/content/papers/10.3997/2214-4609.2025101356
2025-06-02
2026-02-09

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References

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/content/papers/10.3997/2214-4609.2025101356
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3D Meandering Channels Segmentation from Seismic Data Using AI
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.2025101356
/content/papers/10.3997/2214-4609.2025101356
/content/papers/10.3997/2214-4609.2025101356
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