Abstract: At present, the analysis of drilling time-efficiency usually relies on manual post-analysis, which is subjective and arbitrary, and not able to reflect the real field situation in time, with a lot of deviation. In order to identify drilling conditions automatically and accurately in real time and improve drilling efficiency, a data-driven real-time identification method of drilling conditions based on support vector machine (SVM) has been put forward, and established several intelligent identification models. By analyzing and comparing the kernel functions in the models, the optimal model parameters were obtained. Logging data from four wells were used to verify the correctness of the model, and the recognition results were basically consistent with the actual working conditions, and the recognition accuracy under six working conditions was higher than 95%. The analysis of drilling time-efficiency showed that the application of working condition identification result in drilling process shortened the invisible non-production time. Support vector machine has realized real-time intelligent identification of drilling conditions, and improved drilling time-efficiency, which could meet the requirements of digital and intelligent development of oilfields.