摘要
Abstract
With increasing global energy demand and depletion of shallow resources,exploration of deep and complex formations has become a critical focus in the petroleum industry.Traditional methods face bottlenecks in data processing,pattern recognition,and decision optimization.This study systematically reviews the advancements of artificial intelligence(AI)technologies in deep oil and gas exploration,focusing on applications of machine learning(ML),deep learning(DL),and other AI techniques in seismic interpretation,reservoir prediction,and drilling optimization.Case studies demonstrate that AI enhances exploration efficiency,mitigates risks,and optimizes resource management.The research identifies four key challenges for AI implementation:data quality,model interpretability,interdisciplinary talent shortages,and computational constraints,proposing solutions such as data augmentation and model light weighting.By integrating multidisciplinary convergence and emerging technologies(e.g.,edge computing,digital twins),AI is expected to drive the intelligent,real-time,and autonomous transformation of deep exploration.This work provides theoretical and technical insights to support the industry's transition toward next-generation exploration paradigms.关键词
人工智能/深部油气勘探/机器学习/深度学习/知识图谱Key words
artificial intelligence/deep oil and gas exploration/machine learning/deep learning/knowledge graph分类
交通工程
