Drilling Fluid Lost-circulation Prediction Model Hyperparameter Optimization and Interpretation:A Case Study of the Marun Field in Iran
To ensure an effective management of circulation loss,a critical challenge within drilling operations,four balanced data-sets was developed aimed at categorizing varying severity levels of circulation loss at Iran's Marun Oil Field.Employing diverse cross-validation strategies and setting multiple average performance metrics as the optimization objectives,optimal hyperparameters was iden-tified across several models,subsequently determining the best model and its parameter setup.It was found that the selected optimal model exhibited significant enhancements in accuracy,recall,and F1-scores.Specifically,different models,such as extreme gradient boosting and categorical gradient boosting,were applied to datasets corresponding to various well leakage degrees,with all performance met-rics surpassing the 0.95 threshold,thereby illustrating the models'superior performance.Moreover,by computing shapley Additive explana-tions values,the study provided deeper insights into the predictive behaviors,underscoring the substantial influence of hyperparameter opti-mization on model performance enhancement.Additionally,model interpretation emerged as crucial for a profound understanding of the pre-dictive mechanisms,highlighting the necessity of employing such techniques in contemporary research to elucidate model functionalities and outcomes.
万方数据
维普
