Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

Lost circulation and mud losses cause 10 to 20% of the cost of drilling operations under extreme pressure and temperature conditions. Therefore, this research introduces an integrated system for an automated lost circulation severity classification and mitigation system (ALCSCMS). This proposed system allows decision makers to reliability predict lost circulation severity (LCS) based on a few drilling drivers before starting drilling operations. The proposed system developed and compared a total of 11 ensemble machine learning (EML) based on collection 65,377 observations, the data was pre-processed, cleaned, and normalized to be filtered using factor analysis. For each generated algorithm, the proposed system performed Bayesian optimization to acquire the best possible results. As a result, the optimized random forests (RF) model algorithm was the optimal model for classification at 100% classification accuracy based on testing data set. Mitigation optimization model based on genetic algorithm has been incorporated to convert high severe classes into acceptable classes of lost circulation. The system classifies the LCS into 5 classes where the classes from 2 to 4 are converted to be class 0 or 1 to minimize lost circulation severity by optimizing the input parameters. Therefore, the proposed model is reliable to predict and mitigate lost circulation during drilling operations. The main drivers that served as LCS inputs were explained using the SHapley Additive exPlanations (SHAP) approach.