Machine-learning models for classification of mudcake thickness in oil-based drilling fluidsOA

The performance of oil-based drilling fluids(OBDFs)is critical for maintaining drilling efficiency under conditions in which water-based fluids fail to perform effectively.Mudcake or filtercake thickness(MCT)is a key property of OBDFs because it directly influences wellbore stability and the extent of formation damage.Conventional methods for determining MCT rely on manual laboratory measurements that are time-consuming and cannot represent real-time downhole conditions,thereby limiting their value for operational decision-making and motivating the development of reliable predictive methods.This study proposes a novel framework for the near-real-time classification of MCT in OBDF systems.A comprehensive field dataset was compiled from five routinely measured,rapidly acquired laboratory parameters:mud weight,temperature,alkalinity,emulsion stability,and Marsh funnel viscosity.Four machine-learning classifiers-extreme gradient boosting(XGB),support vector machine,extreme learning machine,and multi-layer perceptron-were developed to classify MCT into three operationally meaningful categories:Excellent(≤1.0 mm),Good(1.0-2.2 mm),and Poor(>2.2 mm).The developed models achieved high classification accuracy,with the XGB algorithm demonstrating the most stable and reproducible performance(test accuracy:0.98,F1-score:0.98).Comprehensive evaluation-including receiver operating characteristic analysis,model confidence assessment,learning curves,group K-fold cross-validation,and external validation-confirmed the strong generalization capability of the XGB model,which achieved 94.81% accuracy when applied to completely unseen wells.Interpretability analysis using SHapley Additive exPlanations(SHAP)identified emulsion stability as the dominant predictive feature and revealed that parameter interactions(e.g.,alkalinity-emulsion stability)vary dynamically across different MCT classes.The primary innovation of this study lies in reframing MCT prediction as a multi-class classification problem rather than a traditional regression task,enabling rapid and actionable interpretation of drilling fluid performance.The proposed framework provides a semiautomated,data-driven tool for near-real-time monitoring of MCT in OBDF systems,reducing reliance on slow laboratory measurements and supporting timely operational decisions that improve drilling efficiency and preserve wellbore integrity in complex drilling environments.