Advances in physics-constrained and data-driven dual paradigm for artificial intelligence in oil and gas

Integrating physical mechanisms with data-driven methods overcomes the limitations of purely data-driven artificial intelligence and purely mechanism-based models. Purely data-driven approaches suffer from poor interpretability and weak generalization under sparse data, while purely physics-based models are computationally expensive and struggle with complex nonlinearities. This work highlights advances in the physics-constrained, data-driven dual paradigm across petroleum engineering: mechanism–artificial intelligence fusion via Bayesian networks provides traceable hydrocarbon spatial distribution predictions; knowledge–data-driven modelling ensures geological realism; and collaborative physics–data fault diagnosis enhances well monitoring under noise. These advances demonstrate that deep fusion of domain knowledge, physical laws, and multi-source data is essential for creating interpretable, reliable, and efficient intelligent systems for complex subsurface resource development.