Abstract: In the drilling process of deepwater and deep oil and gas, there is a high demand for real-time calculation of the wellbore temperature field. Therefore, a high-precision and high-efficiency wellbore temperature field solution method is the key to accurately calculate fluid properties and precisely guarantee the safety of wellbore flow. In this study, a wellbore temperature field model is embedded into the neural network in the form of loss function, and the optimization method of self-adaptive weight and self-adaptive learning rate is used to improve the training efficiency. Further, the paper establishes a method for solving the wellbore temperature field driven by physical information neural network, and analyzes the transient changes in wellbore temperature during drilling and gas well testing. The results show that during drilling, the average errors of drill pipe temperature and annular temperature are 0.847 % and 0.725 % , respectively, and those of bottom hole temperature and wellhead temperature are 0.162 % and 1.047 % , respectively, from which it can be seen that the computational efficiency is improved by about 150 times when compared with the finite difference algorithm. Compared with the field measurments, the average errors of the predicted solution driven by the physical information neutral network and the finite difference numerical solution are 2.16 % and 2.27 % , respectively, and the model accuracy can be improved by avoiding the truncation errors in partial differential equations. During the gas well testing for two days, the inferred time for the risk of natural gas hydrate formation is 0.728 1 s, and this method can be applied to quickly predict the hydrate formation areas. In conclusion, the proposed solution method can not only ensure the calculation accuracy, but also significantly improve the computational speed.