Oil-bearing potential identification of low-resistivity layers during drilling: a small-sample smart method based on gas-logging and LWD gamma ray

Abstract

Low-resistivity oil layers are difficult to identify during drilling because their resistivity responses may overlap substantially with those of water-bearing intervals, while reliable calibration samples obtained from post-drilling field verification are often limited. To address these challenges, this study proposes a small-sample intelligent method for evaluating the oil-bearing potential of low-resistivity intervals using gas-logging parameters and LWD natural gamma-ray (GR) data. The model performs binary classification between oil-bearing and water-bearing intervals, with normal oil-layer and low-resistivity oil-layer samples combined into the oil-bearing class; the original low-resistivity oil-layer labels are retained for subgroup-specific evaluation. Within the training dataset, SMOTE-based augmentation was applied to the original geological subclasses to improve sample representation, and Kernel Principal Component Analysis (KPCA) was employed for nonlinear feature extraction and redundancy reduction. A KPCA-PSO-CatBoost model was subsequently established, with particle swarm optimization (PSO) used for hyperparameter optimization. On an independent test set comprising 25 intervals from two previously unused wells, the optimized model achieved an accuracy of 92.0% and a Macro-F1 score of 0.890. Ablation experiments indicated that KPCA provided the more substantial performance improvement, while PSO contributed an additional complementary gain through hyperparameter optimization. The proposed method shows potential as a data-driven auxiliary workflow for rapid evaluation of oil-bearing potential during drilling, particularly for low-resistivity intervals under conditions of limited calibration samples and ambiguous electrical responses.