Multicomponent seismic data can be used to predict unconventional reservoirs; however, this is a challenging task. Although machine learning (ML), particularly deep learning, can be used in this regard, its accuracy in reservoir prediction depends largely on the amount of data available for training and the complexity of the architecture. This study attempted to address this problem using transfer learning (TL) and a compact convolutional neural network with a self-attention mechanism (SACNN). We developed a framework for unconventional reservoir prediction by expanding the data samples and optimizing model performance. First, the synthetic data for both oil and gas reservoirs were used as the source data; their effectiveness was tested using the SACNN model. Subsequently, a real dataset was obtained by optimizing the real multicomponent seismic attributes. The TL dataset was constructed by transferring synthetic gas reservoir data to real dataset. Finally, the constructed SACNN model was used to predict the gas-bearing distribution in tight sandstone gas reservoirs. The results showed the superiority of the proposed model over conventional ML models, with lower error in the unconventional reservoir distribution prediction. Moreover, the proposed model exhibited superior prediction performance (R² = 0.9731) on the testing dataset compared to models trained solely on synthetic (R² = 0.9389) and real (R² = 0.9627) data. Moreover, uncertainty analysis showed that the proposed model is robust and efficient. The proposed framework provides a basis for constructing data-driven models for energy conversion and utilization.

多分量地震数据可用于非常规储层预测；然而，这是一项具有挑战性的任务。尽管机器学习（ML），特别是深度学习，可以在这方面使用，但其储层预测的准确性很大程度上取决于可用于训练的数据量和架构的复杂性。本研究尝试使用迁移学习（TL）和具有自注意力机制的紧凑卷积神经网络（SACNN）来解决这个问题。我们通过扩展数据样本和优化模型性能，开发了非常规油藏预测框架。首先，以油气藏综合数据作为源数据；使用 SACNN 模型测试了它们的有效性。随后，通过优化真实的多分量地震属性，获得了真实的数据集。 TL数据集是通过将合成气藏数据转换为真实数据集构建的。最后，利用构建的SACNN模型对致密砂岩气藏含气性分布进行预测。结果表明，该模型相对于传统的ML模型具有优越性，在非常规储层分布预测中具有较低的误差。此外，与仅在合成数据（ R ²  = 0.9389）和真实数据（R ² = 0.9627） 上训练的模型相比，所提出的模型在测试数据集上表现出优越的预测性能（R ² = 0.9731）  。此外，不确定性分析表明所提出的模型是稳健且有效的。所提出的框架为构建能源转换和利用的数据驱动模型提供了基础。