Abstract: Research of oil and gas artificial intelligence can be divided into two levels, academic and industrial research, which faces different problems and challenges.Academic oil and gas artificial intelligence application scenarios are mainly concerned with algorithms and their related theoretical applications, focusing on solving the local problems of intelligent points.Industrial-grade artificial intelligence applications are mainly concerned with data sets, platforms, multi-source multi-scale data fusion modeling, data-driven and mechanism model fusion modeling, and machine learning model explanatory issues.In this study, three suggestions are put forward for data-driven and mechanism model fusion: algorithm fusion, evaluation method fusion and data set fusion, and experimental verification is given.In view of the problems of oil and gas artificial intelligence models, the author illustrates that industrial-grade oil and gas artificial intelligence must be explanatory and puts forward some preliminary solutions, including multi-level interpretation, pre-modeling, in-modeling, and post-modeling.Finally, the author suggests that, to explore the development of industrial-grade artificial intelligence theory and application scenarios, we must clarify the interaction between the "physical world, " "digital world, " "the world recognized by humans, " "the world recognized by machines, " and "the world in which machines are being transformed."