Lost circulation prediction based on deep AUC maximization
Lost circulation is a significant challenge in oil and gas drilling,which can lead to various costly and time-consuming problems.It is of great significance to use artificial intelligence technology to accurately predict the risk of lost circulation.The lost circulation prediction problem was converted into an imbalanced classification problem,which pose challenges to traditional deep learning models due to the imbalance between categories and the lack of high correlation between drilling features.Ac-curacy is not an appropriate measurement for imbalanced classification algorithms.A deep AUC maxi-mization(DAM)algorithm,which is called FAUC-S,is introduced in this paper.It trains a combina-tion deep learning model by focusing on the AUC loss of hard samples(FAUC-S).Several traditional deep learning methods are also applied to classify lost circulation risk during oil exploration in the ex-periments.The result shows that the FAUC-S method achieved the highest accuracy,recall,and F1 score among the other three models.This confirms that the FAUC-S model has superior classification performance.Therefore,the successful implementation of this deep model can help drilling teams ef-fectively solve drilling problems.
lost circulationimbalanced classificationdeep learningAUC maximization
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