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With the continuous surge of production data in the field of oil exploration and development, the difficulty of integration and analysis is increasing, and manual analysis methods are difficult to meet the development needs of oil and gas fields on site. Machine learning can accurately and efficiently process data, and is a key technology in the field of data analysis and optimization. Utilizing machine learning to solve difficult problems in oil and gas field development has become the main focus of the industry. However, due to limitations in standardization construction, sample library establishment, and on-site integration, there are still many challenges and shortcomings in the application of machine learning in the field of oil exploration and development. In the future, efforts should be made to strengthen the standardization construction of machine learning, improve the data processing ability of deep learning algorithms, and pro-mote interdisciplinary research in the field of deep machine learning. Through multiple approaches, we can jointly promote the significant development of machine learning in the oil and gas field industry. [ABSTRACT FROM AUTHOR]