Abstract
Intelligent reservoir modeling and high-precision numerical simulation integrate artificial intelligence, machine learning, and big data analytics to provide efficient and accurate solutions for reservoir modeling, parameter optimization, and uncertainty assessment. Traditional reservoir modeling relies on geostatistics and physical numerical simulation methods, which are computationally expensive and struggle to accurately characterize complex reservoir structures and fluid migration patterns. Intelligent reservoir modeling leverages technologies such as deep learning, reinforcement learning, and surrogate modeling to extract key features from vast historical production data and geological information, enabling automated optimization of reservoir parameters and rapid predictions. Additionally, multi-scale numerical simulation combined with uncertainty quantification enhances the stability and computational efficiency of reservoir modeling, offering data-driven decision support for reservoir development optimization. The advancement of this research field will contribute to improving recovery rates, reducing oilfield development costs, and promoting the intelligent and efficient utilization of oil and gas resources.
