Application of Artificial Intelligence in Oil and Gas Engineering

Dear Colleagues,

Artificial intelligence (AI) has become a core enabling technology for the intelligent transformation of petroleum engineering. By combining machine learning, big data analytics, digital twins and intelligent optimization, AI supports the efficient, safe and low-carbon development of oil and gas resources. The main research contents include:

1. Intelligent Exploration & Geophysical Analysis AI-based automated fault and reservoir prediction, lithology and physical property inversion, and high-precision identification of hydrocarbon reservoirs.
2. Intelligent Drilling EngineeringReal-time monitoring and early warning of downhole risks (kick, lost circulation, stuck pipe), automatic well trajectory control, rate of penetration (ROP) optimization, and intelligent drilling fluid design.
3. AI-driven Reservoir EngineeringFast reservoir simulation, dynamic production prediction, intelligent optimization of development plans, and data-driven enhanced oil recovery (EOR) strategy design.
4. Intelligent Production OptimizationReal-time production data analysis, productivity prediction, intelligent control of artificial lift systems, and parametric optimization of hydraulic fracturing and stimulation.
5. Intelligent Gathering & TransportationPipeline leak detection and location, flow assurance prediction, energy consumption optimization of surface facilities, and intelligent operation of gathering systems.
6. Equipment Health & Safety Risk ControlAI-based equipment fault diagnosis and remaining useful life prediction, real-time safety risk early warning, and intelligent HSE management.
7. Digital Twin & Smart OilfieldConstruction of full-cycle digital twin models for wells and reservoirs, unmanned production platforms, and integrated intelligent decision-making for the entire upstream industrial chain.
8. Low-carbon & Sustainable DevelopmentAI-assisted optimization of carbon capture, utilization and storage (CCUS), energy efficiency improvement, and intelligent decision-making for green and low-carbon oilfield development.

Prof. Dr. Chaodong Tan
Dr. Kang Li
Prof. Dr. Xiaoyong Gao
Dr. Ziming Feng
Dr. Bin Liu
Guest Editors

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