  Effective prediction of shale gas production is crucial for strategic
reservoir development. However, in new shale gas blocks, two main challenges
are encountered: (1) the occurrence of negative transfer due to insufficient
data, and (2) the limited interpretability of deep learning (DL) models. To
tackle these problems, we propose a novel transfer learning methodology that
utilizes domain adaptation and physical constraints. This methodology
effectively employs historical data from the source domain to reduce negative
transfer from the data distribution perspective, while also using physical
constraints to build a robust and reliable prediction model that integrates
various types of data. The methodology starts by dividing the production data
from the source domain into multiple subdomains, thereby enhancing data
diversity. It then uses Maximum Mean Discrepancy (MMD) and global average
distance measures to decide on the feasibility of transfer. Through domain
adaptation, we integrate all transferable knowledge, resulting in a more
comprehensive target model. Lastly, by incorporating drilling, completion, and
geological data as physical constraints, we develop a hybrid model. This model,
a combination of a multi-layer perceptron (MLP) and a Transformer
(Transformer-MLP), is designed to maximize interpretability. Experimental
validation in China's southwestern region confirms the method's effectiveness.
