Low-contrast oil layer identification represents a significant challenge in sandy-shaly reservoirs, requiring advanced analytics.
The hybrid model combining SMOTE and BiLSTM shows improved accuracy compared to traditional methods.
Assessment using simulation data reveals enhanced performance metrics for oil layer detection across diverse geological settings.
Implementation may lead to more effective resource management strategies, though the model's adaptability to other contexts warrants further exploration.