In the process of drilling, the speed at which a drill bit breaks through rock and deepens the drill hole is called the rate of penetration (ROP), which is an important index reflecting drilling efficiency. In recent years, machine learning methods have been applied to the ROP prediction. However, it is found in practice that the prediction accuracy of ROP based on existing machine learning methods is significantly reduced when applied to new oil fields, and the main reason is that the data available for learning and training in these new fields are usually scarce or even completely missing. Therefore, improving the prediction performance of ROP in new oil fields is an important issue to be solved. Considering this, a cross-oilfield ROP prediction method based on transfer learning is proposed, and a boosting transfer regression model with physical constraints is constructed to predict ROP of new oil fields. The experiments on real drilling datasets indicate that the proposed method is effective, and the prediction accuracy is significantly better than that of the current mainstream ROP prediction methods.