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This is a Quantitative Study study.

September 12, 2025

Machine learning integration with seismic attributes for lithofacies prediction and distribution in Abu Madi Reservoir, onshore Faraskour Gas Field, East Nile Delta, Egypt

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Integrating machine learning and seismic attributes improves lithofacies evaluation in Abu Madi formation, indicating hydrocarbon potential.

The RF model achieved 84% blind test accuracy in predicting lithofacies, revealing sandstone, siltstone, shale, and marl associations.
Conventional well logs were used as input features, utilizing limited core data for ground truth labels to enhance accuracy.
An integrated workflow applying machine learning with seismic attributes was developed for reliable reservoir evaluation.
The methodology proved effective for lithofacies classification in a data-constrained environment, guiding exploration decisions.

Badr et al. (2025) studied this question.

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Integrating machine learning and seismic attributes improves lithofacies evaluation in Abu Madi formation, indicating hydrocarbon potential.