Overflow is one of the complicated working conditions that often occur in the drilling process. If it is not discovered and controlled in time, it will cause gas invasion, kick, and blowout, which will bring inestimable accidents and hazards. Therefore, overflow identification and early warning has become a hot spot and a difficult problem in drilling engineering. In the face of the limitations and lag of traditional overflow identification methods, the poor application effect, and the weak mechanisms of existing models and methods, a method of series fusion of feature data obtained from physical models as well as sliding window and random forest machine learning algorithm models is proposed. The overflow identification and early warning model of managed pressure drilling based on a series fusion data-driven model is established. The research results show that the series fusion data-driven model in this paper is superior to the overflow identification effect of other feature data and algorithm models, and the overflow recognition accuracy on the test samples reaches more than 99%. In addition, when the overflow is identified, the overflow warning is performed through the pop-up window and feature information output. The research content provides guidance for the identification of drilling overflow and the method of model fusion.

The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application effect of existing models and methods is not obvious, and there is no integration of the physical model and the intelligent algorithm model for overflow diagnosis, this paper proposes a method of adaptive weight fusion of physical model and intelligent algorithm model diagnosis results. Based on the fusion of the physical model and the intelligent algorithm model, an overflow diagnosis model of managed pressure drilling is established. The research results show that the fusion model in this paper can combine the accuracy weight of the physical model and the intelligent algorithm model for the intelligent diagnosis of overflow risk, which improves the mechanization and interpretability of the model and diagnosis results while ensuring accuracy and efficiency. And the intelligent algorithm model used in the fusion model is superior to other algorithm models. The overflow diagnosis accuracy of the fusion model on the test set samples reaches more than 98%, and the accuracy of the validation set is 94.25%. The content of this study provides guidance for drilling overflow diagnosis and model fusion methods.