Abstract
With the increasing complexity of geological conditions and the depletion of global resources, petroleum exploration and reservoir modeling face unprecedented challenges. This study systematically explores the innovative applications of big data technology and artificial intelligence (AI) to improve exploration success rates and optimize reservoir modeling. Firstly, it analyzes the multidimensional complexity of seismic data and its pivotal role in petroleum exploration, detailing the applications of big data in data preprocessing, feature extraction, and pattern recognition. Secondly, it highlights the potential of AI algorithms in automated geological structure identification, refined reservoir modeling, and predictive analysis, emphasizing how data-driven approaches address the limitations of traditional modeling methods. The study proposes an intelligent exploration strategy based on big data and AI technologies, integrating real-time data analysis with dynamic adjustments to enhance the scientific accuracy of decision-making. Through case studies, the effectiveness of these methods in practical applications is validated, with an in-depth discussion of their advantages and technical challenges. Finally, the study envisions the future of big data and AI technologies in the petroleum industry, underscoring their pivotal role in advancing intelligent and efficient exploration and development.
