抄録
The rapid development of big data technology has brought new opportunities and challenges to oil reservoir modeling. This paper systematically reviews the key applications of big data in oil reservoir modeling, covering crucial stages such as data acquisition, preprocessing, data fusion, and integration, and focuses on innovative methods for predicting oil reservoir properties (e.g., porosity and permeability) based on machine learning algorithms. By combining the strengths of data-driven models with traditional physical models, strategies to improve the accuracy and reliability of oil reservoir modeling are proposed. Furthermore, the paper delves into the applications and value of big data technology in real-time oil reservoir modeling, oil reservoir evaluation, development decision-making, uncertainty analysis, and risk assessment. Finally, the paper envisions future trends in the deep integration of big data artificial intelligence, and efficient computing technologies in oil reservoir management, especially their potential to support sustainable oilfield development. This work aims to provide practical insights for big data-driven oil reservoir modeling, promoting the intelligence and efficiency of oilfield development.
