Lithology identification using stratigraphic continuity and petrophysical features of well logs

• Simple linear iterative clustering with Transformer is used for lithology prediction. • Simple linear iterative clustering extracts stratigraphic continuity from well logs. • Dependencies among petrophysical features in well logs are captured by Transformer. • The proposed method improves lithology accuracy from the Zagros Basin in Middle East. • The approach improves lithology identification by up to 4.1% relative to K-Means. Lithology identification underpins reservoir characterization and plays a pivotal role in hydrocarbon exploration. Considering vertical stratigraphic continuity is particularly important, as it reflects sedimentary patterns and enhances the reliability of lithology prediction. Existing methods based on statistical identifiability and K‐Means clustering cannot fully capture the stratigraphic continuity. To address these limitations, a novel Simple Linear Iterative Clustering (SLIC) with Transformer (SLICTF) method is proposed that jointly captures vertical stratigraphic continuity and dependencies among petrophysical features in well logs to improve lithology identification. The SLIC algorithm is designed for well logs to extract stratigraphically coherent features that preserve continuity across transitional lithologic intervals. SLIC segmentation features are then fused with the original well logs and fed into Transformer model. It explicitly learns feature interactions. To validate the proposed method, experiments were conducted using complex lithological datasets from the Zagros Basin in Middle East. Empirical results demonstrate that SLICTF substantially improves lithology classification, achieving up to 92.5% and outperforming K‑Means based approaches by up to 4.1%. By capturing vertical stratigraphic features and feature dependencies, SLICTF with attention heads matched to input feature dimensionality enhances discrimination among complex carbonate and mudstone lithologies. SLIC accelerates feature characterization by reducing mean computational time by 82.7% and stabilizes Transformer accuracy four iterations earlier than K‑Means. The method demonstrates reliability for engineering applications.