Abstract
The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-training framework integrated with a shape context descriptor. This method leverages abundant unlabeled seismic data as well as complementary information on related physical properties. Specifically, we introduce a shape context descriptor to encode seismic waveform morphology and spatial context, thereby improving the lateral continuity and interpretability of predictions while mitigating issues inherent in the sequence-to-point paradigm, wherein three-dimensional seismic data are used as input and a single target point is predicted. To overcome data limitations, a sliding-window resampling strategy is employed to expand the training samples. For co-training, we design an asymmetric dual-task architecture wherein one model performs porosity regression while the other conducts reservoir type classification, thereby enabling synergistic learning. The proposed framework is validated using real three-dimensional seismic data from the Yinggehai Basin in the South China Sea through ablation experiments. The results demonstrate superior performance in prediction accuracy, spatial consistency, and training stability compared to baseline methods.