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.

Hydraulic fracturing (HF) in horizontal wells frequently encounters challenges such as uneven fracture propagation and elevated energy consumption, largely due to stress shadow effects and perforation inefficiencies. This paper presents a novel stress-coupled perforation (SCP) technique that dynamically optimizes perforation cluster parameters by integrating real-time stress interference. The approach employs a semi-numerical model grounded in linear elastic fracture mechanics (LEFM) and energy conservation principles, enabling rapid simulation of multi-fracture propagation with high accuracy. Field applications in both homogeneous and heterogeneous shale reservoirs demonstrate that SCP achieves more uniform fracture development while reducing energy consumption by 22%–37% per unit fracture area compared to the conventional extreme limited entry (EXL) method, without sacrificing fracture coverage. The method offers a practical and sustainable pathway to improve HF efficiency, lower operational costs, and reduce environmental impact.

Accurate geological modeling of shallow-water delta reservoirs remains challenging due to complex sedimentary architecture and strong heterogeneity. This study develops an advanced modeling technique that integrates geological process understanding with deep learning, with a focus on the accurate representation of channel geometry under multiple data constraints. Field outcrop investigations of the Chang 6 Member in the Ordos Basin were conducted to clarify key geological characteristics and geometric parameters of shallow-water distributary channels. An improved object-based method was employed to effectively generate three-dimensional training datasets capturing typical channel bifurcation and convergence patterns. A conditional progressive generative adversarial network is proposed to incorporate multi-source constraints, including global geological features, well logs, and seismic probability volumes, thereby enabling simultaneous learning of geological patterns and data fidelity. Application to a shallow-water delta reservoir in the Ordos Basin demonstrates that the method produces geologically realistic facies models that honor all available constraints, significantly improving modeling accuracy and computational efficiency. This work provides an innovative and adaptive methodology for intelligent modeling of complex reservoir systems.

The rapid integration of artificial intelligence into oil and gas exploration and development offers transformative opportunities within the context of the global energy transition. This article highlights the key advancements and challenges in artificial intelligence applications. Machine learning algorithms enable data-driven shale sweet spot prediction, overcoming the limitations of traditional methods by capturing complex controlling factors. Intelligent core image analysis, leveraging computer vision and foundation models, enables automatic mineral identification, pore analysis, and rock structure characterization, thereby providing a comprehensive framework for microscopic reservoir appraisal. Physics-informed neural networks address the limitations of purely data-driven reservoir simulation by embedding governing seepage equations into their loss functions, thereby ensuring physical consistency and improved generalization. Multimodal architectures significantly enhance unconventional shale gas production prediction by integrating geological heterogeneity with dynamic production behavior, leading to more accurate and stable forecasts. Collectively, these AI-driven approaches underscore the importance of combining domain expertise, multi-source data, and physics-aware modeling to achieve efficient and intelligent oil and gas development.