Abstract:

An early prediction and warning method of offshore drilling overflow based on data model collaboration was proposed to prevent blowout accidents during offshore drilling. Firstly, an overflow risk prediction model based on PSO-LSSVM was established to predict the trend of drilling monitoring parameters in the future, and analyze the correlation between overflow events and characterization parameters. Then, a single-parameter overflow probability estimation prediction model was proposed based on the Naive Bayesian method, and the probabilities of multiple drilling parameters were integrated through the optimized D-S method to realize a hierarchical early warning of overflow events. The results indicated that the overflow characterization parameters simulated by the PSO-LSSVM model had low prediction errors. The overflow event probability represented by a single drilling parameter showed discrepancies due to different sensitivities. The fused early warning model can address the issues of inconsistent early warning times of single parameters and eliminate the possibility of false alarms.

Key words: data model collaboration, drilling overflow, early prediction, particle swarm optimization(PSO)-least squares support vector machines(LSSVM)(PSO-LSSVM), early warning models