Comprehensive Review on Seismic Facies Identification

Authors

  • Rui Liang

DOI:

https://doi.org/10.62051/ijcsit.v3n2.46

Keywords:

Seismic Facies Identification, Seismic Stratigraphy, Seismic Attributes, Machine Learning, Deep Learning, Reservoir Characterization, Hydrocarbon Exploration

Abstract

Seismic facies identification is crucial for interpreting subsurface geological features and predicting reservoir properties. This review discusses the methodologies, advancements, and applications in seismic facies identification, including traditional approaches, machine learning techniques, and case studies. The aim is to provide a thorough understanding of the current state and future directions in this field.

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References

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Wu, X., & Hale, D. (2016). Convolutional neural networks for fault interpretation in seismic images. Geophysics, 81(4), IM21-IM32.

Zeng, H., & Backus, M. (2005). Interpretation of seismic inversion results using reservoir facies. Geophysics, 70(5), P47-P56.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Abreu, V., Sullivan, M., Pirmez, C., & Mohrig, D. (2003). Lateral accretion packages (LAPs): An important reservoir element in deep water sinuous channels. Marine and Petroleum Geology, 20(6-8), 631-648.

Litenberg, J.H. (2005). Detection of fluid migration pathways in seismic data: implications for fault seal analysis. Basin Research, 17(1), 141-153.

Gao, D. (2007). Application of three-dimensional seismic texture analysis with special reference to deep-marine facies interpretation: a case study from the Gulf of Mexico. AAPG Bulletin, 91(2), 166-183.

Zhang, R., & Castagna, J.P. (2011). Seismic lithology classification using multivariate analysis. Geophysics, 76(2), C23-C34.

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Published

19-07-2024

Issue

Section

Articles

How to Cite

Liang, R. (2024). Comprehensive Review on Seismic Facies Identification. International Journal of Computer Science and Information Technology, 3(2), 416-420. https://doi.org/10.62051/ijcsit.v3n2.46