Early Warning Method for Drilling Overflow Based on Temporal Deep Feature Cross

In the field of offshore drilling production, the shift from offshore to deepwater exploration has a rapidly growing frontier. The integration of artificial intelligence, particularly in deepwater drilling, gives a transformative solution to the complex challenges faced in such operations. A key application is that the analysis of different production data from deepwater oil and gas wellbores, which aids in detecting potential overflow scenarios. To address this, this paper proposes the Deep Feature Cross Sequence Analysis Network (DFCSAN), a model that combines the strengths of feature crossing and time series analysis, tailored for overflow warning in drilling operations. Experimental results show that the DFCSAN model achieves higher recall rates and F1 scores in overflow detection, providing more effective identification and early warning of overflow risks.