Abstract: With the development of oil and gas exploration and development toward complex formations, the risk of blowout during drilling has increased, and overflow is the precursor of blowout, so the early warning of overflow becomes a key direction to well control and safety prevention. Aiming at the problem that the traditional prediction algorithm fails to analyze the severity of overflow and the prediction accuracy is not high when performing overflow warning based on ground parameters, through the study of overflow symptoms and the mechanism of overflow, the annulus electromagnetic flow system and other systems are used to directly measure the underground near the bit, and an overflow intelligent early warning model was established based on artificial intelligence algorithm—Random Forest to classify and predict the severity of overflow. In order to verify the feasibility of the early warning model, a simulation experimental platform was built for testing, and compared with the conventional BP neural network. The results show that the accuracy of this method is as high as 92.68%, and the accuracy of classification prediction is significantly higher than that of the BP neural network. The research results verify the reliability of the random forest model for downhole overflow early warning, which well realizes the early warning of overflow, and provides a safety technical guarantee for drilling, and has good application prospects.

Key words: overflow intelligent warning, overflow symptoms, annulus parameters, random forest, artificial intelligence