An intelligent drilling accident diagnosis method considering the influence of data imbalance

The accurate recognition of drilling accident ensures the smooth implementation of drilling engineering. Existing drilling accident diagnosis method based on machine learning doesn’t consider the data imbalance of drilling data, which may mistake drilling accidents for normal working conditions. In this paper, a drilling accident diagnosis method considering the influence of data imbalance was established based on decision tree classification model. In this method, initial data is collected from mud logging data, engineering anomaly records and other field data, weight on bit (WOB), hook load, displacement and other drilling parameters are extracted and a sample set is constructed based on fluctuation values. Mis-classification cost is introduced to correct the influence of data imbalance, a decision tree model with the expected minimum mis-classification cost as the classification objective is established to replace the classification model with the maximum accuracy as the objective. This new model was applied to diagnose the sticking accident in one certain shale-gas horizontal well. The results show that after data imbalance is taken into consideration, the model can recognize the sticking samples that are neglected by traditional method and the expected cost is reduced by 85%. This proposed data imbalance treat method is not limited to decision tree model and it can be popularized to other machine learning methods to assist the recognition of drilling accidents.