Enhanced random forest with geologically-informed feature optimization for complex volcanic rock lithology...

Identifying lithologies within the volcanic reservoirs of the Huoshiling Formation (Wangfu Fault Depression, Songliao Basin) remains challenging due to extreme heterogeneity, limited core control, and ambiguous responses on conventional logs. We introduce an enhanced machine-learning framework for high-precision classification of these complex volcanic sequences, leveraging detailed core descriptions and five conventional well logs-gamma ray (GR), compensated neutron (CNL), bulk density (DEN), acoustic travel-time/sonic (AC), and deep array laterolog resistivity (RLA5)-from 12,388 depth-matched samples across 20 wells, encompassing 18 lithologies. The core innovation is an enhanced Random Forest (eRF) specifically engineered for geological and data-centric challenges. The eRF synergistically integrates: (1) Borderline-SMOTE to counteract severe class imbalance by selectively augmenting minority instances near decision boundaries, critical for rare but geologically significant facies; (2) C4.5 decision trees with gain-ratio splitting to optimize node-level feature selection from correlated continuous logs; and (3) Kendall's coefficient of concordance (Kendall's W) to stabilize feature-importance ranking across trees, prioritizing robust predictors. Against standard RF, back-propagation neural network (BPNN), k-nearest neighbors (kNN), and support-vector machine (SVM), the eRF attains 96.34% overall accuracy. Per-class accuracies exceed 0.88 for all 18 lithologies, with the largest improvement (+43 percentage points) for trachytic tuff. Sensitivity analysis indicates GR and AC dominate, together accounting for >60% of model decisions. This geologically attuned, optimized ensemble provides a transferable route to high-resolution lithology logs in uncored intervals, substantially aiding hydrocarbon sweet-spot prediction in complex volcanic settings.