抄録
As oilfield development extends to complex and low-permeability reservoirs, fracturing technology has become a core approach to enhancing oil and gas recovery rates. However, traditional methods for evaluating fracturing performance often fall short of meeting the efficiency and precision demands of modern oilfield development due to limited data samples and simplistic analytical tools. This paper systematically reviews the latest advances in using big data and artificial intelligence (AI) technologies for assessing fracturing effectiveness. It begins by analyzing the application scenarios of mainstream fracturing technologies and the limitations of traditional evaluation methods. It then highlights the role of big data in cleaning, integrating, and analyzing fracturing operation data, as well as the potential of AI models, such as machine learning and deep learning, in predicting and optimizing production enhancement outcomes. Through case studies and empirical research, the significant benefits of data-driven fracturing optimization in improving development efficiency and reducing costs are demonstrated. Finally, the challenges of current applications, such as insufficient algorithm robustness and uneven data quality, are discussed, along with possible directions for future research and applications. This paper provides robust support for the intelligent and data-driven advancement of fracturing technology, offering significant practical implications for the efficient development of oil and gas resources.
