Data-driven approaches & Machine Learning has been widely used in different aspects of the life of human beings. The applications of this technology in petroleum industry are becoming more and more popular. The purpose of this approach is to model physics and engineering-related problems purely based on data, and several different types have been investigated, including the smart proxy models, the data-driven model, and the hybrid model. However, the understanding of Artificial Intelligence and Machine Learning technology in the petroleum industry has not been deep and solid enough to take full advantage of this technology. The issue is proposed to improve the applications of machine learning and data-driven approaches in petroleum engineering, and it is open for studies on applying data-driven approaches & machine learning applications in petroleum data analytics and process modeling, which may include reservoir engineering, production engineering, and drilling engineering.