TY  - JOUR
AU  - Hui, Gang
AU  - Wang, Muming
AU  - Cheng, Haibo
PY  - 2026
TI  - Advances in physics-constrained and data-driven dual paradigm for artificial intelligence in oil and gas
JO  - Advances in Geo-Energy Research
SN  - 2207-9963
SP  - 201
EP  - 204
VL  - 20
IS  - 3
AB  - 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.
UR  - https://doi.org/10.46690/ager.2026.06.01
DO  - 10.46690/ager.2026.06.01