The accuracy of conventional reservoir models is sometimes limited by conventional seismic attribute analysis’s inability to resolve intricate, non-linear subsurface relationships. In order to minimize exploration risk and optimize recovery, data-driven strategies must be integrated as exploration moves toward structurally complex and mature areas. In order to improve subsurface imaging and reservoir characterization, this commentary paper explores the revolutionary role of machine learning (ML) in seismic attribute analysis. In particular, it assesses how sophisticated workflows maximize pattern detection and close gaps in trans-disciplinary data. We examine the use of important machine learning (ML) architectures in conjunction with cross-disciplinary geological, geophysical, and engineering datasets, concentrating on the advantages and deployment constraints of artificial neural networks (ANNs) and support vector machines (SVMs). According to the analysis, ML algorithms are able to accurately predict important rock qualities like porosity and fluid saturation while also correctly identifying tiny structural elements. This commentary paper also demonstrates the need of tackling the fundamental issues of data quality, interpretability of models, and computational scalability. Finally, we show that a more thorough, dependable, and scalable framework for contemporary reservoir characterization is produced by integrating ML with physics-based restrictions.

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O.F. Adizua and E.T. Anele “The Role of Machine Learning in Seismic Attribute Analysis: A Commentary on Progress, Pitfalls, and the Path Forward” International Journal of Applied and Physical Sciences, vol. 11, pp. 29-35, 2025. Doi: https://dx.doi.org/10.20469/ijaps.11.50004