Abstract

Accurate lithofacies identification in carbonate reservoirs is essential for reservoir characterization and development decision-making. However, the strong heterogeneity of carbonate rocks, nonlinear responses of well logging parameters, and imbalance among lithofacies samples significantly limit the performance of conventional machine learning methods. To address these challenges, an improved K-Nearest Neighbor (KNN) lithofacies identification method is proposed in this study using logging data from the Mishrif Formation in the Halfaya Oilfield, Iraq. A total of 600 samples from five wells (X1–X5) were used for model construction and validation. Four carbonate lithofacies types, including grainstone, packstone, wackestone, and marl, were identified based on core observation and thin-section analysis. Five logging parameters, including GR, AC, CNL, DEN, and RT, were selected to construct the feature space. A hybrid SMOTE–NearMiss-1 sampling strategy was introduced to alleviate class imbalance, while a feature-weighted Manhattan distance and distance-weighted voting mechanism were developed to improve the discrimination capability of KNN. The results show that the improved KNN model achieved an overall accuracy of 84.44%, outperforming the baseline KNN model (78.89%) as well as other comparison models, including Random Forest (RF, 76.67%) and Backpropagation Neural Network (BPNN, 75.56%). The area under the ROC curve (AUC) values for all lithofacies classes range from 0.917 to 0.989, indicating robust classification performance. In addition, balanced accuracy and macro-F1 score demonstrate improved recognition performance for minority lithofacies. The predicted lithofacies profiles show good agreement with the core interpretation results and effectively capture vertical lithofacies variations. This study demonstrates that the improved KNN method provides an effective data-driven approach for carbonate reservoir lithofacies characterization, especially under conditions of heterogeneous geological environments and limited labeled samples.

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