A Pattern Recognition Method for Offshore Drilling Gas Kick and Overflow Diagnosis

In offshore drilling, accidents such as gas invasion, overflow, and kicks are unavoidable, they can escalate into blowouts other catastrophic events, resulting in casualties significant economic losses. Therefore, ensuring drilling safety requires precise monitoring of invasion overflow. Currently, most overflow methods used at sites based on threshold criteria. However, the parameters obtained during actual operations often contain noise signals, which makes it challenging for threshold-based to achieve a balance between improving accuracy minimizing false positives. This paper proposes novel method called Pattern-Recognition-based Kick Detection (PRKD) diagnosing drilling. The PRKD utilizes evolution process by integrating multiphase flow calculations, data filtering theory, pattern recognition Bayesian framework, theoretical models. By analyzing shape wave characteristics curves, effectively detects monitors intrusion single parameters. Through case analysis, is demonstrated that proposed achieves high precision while maintaining low positive rate. combining advanced computational techniques with algorithms, improves reliability kick detection, enabling proactive responses potential risks, protecting environment human lives, optimizing operations. analysis shows probabilistic information pre-drilling various characteristic parameters, when amplitude less than 8 L/s, model exhibits superior detection performance. Moreover, 16 continuous approximately 200 s after occurs predicts 96.4% probability occurrence specified location, meeting on-site requirements. this provides accurate diagnostic results rate, offering valuable guidance